code: 
  '''simple docstring'''
  
  
  
  import unittest
  
  from transformers import LayoutLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  				import torch
  
  				from transformers import (
  				    LayoutLMForMaskedLM,
  				    LayoutLMForQuestionAnswering,
  				    LayoutLMForSequenceClassification,
  				    LayoutLMForTokenClassification,
  				    LayoutLMModel,
  				)
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=13       ,     lowerCamelCase__=7       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=99       ,     lowerCamelCase__=32       ,     lowerCamelCase__=5       ,     lowerCamelCase__=4       ,     lowerCamelCase__=37       ,     lowerCamelCase__="gelu"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=512       ,     lowerCamelCase__=16       ,     lowerCamelCase__=2       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=3       ,     lowerCamelCase__=4       ,     lowerCamelCase__=None       ,     lowerCamelCase__=1_000       ,     )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       parent
  									lowercase__							 =       batch_size
  									lowercase__							 =       seq_length
  									lowercase__							 =       is_training
  									lowercase__							 =       use_input_mask
  									lowercase__							 =       use_token_type_ids
  									lowercase__							 =       use_labels
  									lowercase__							 =       vocab_size
  									lowercase__							 =       hidden_size
  									lowercase__							 =       num_hidden_layers
  									lowercase__							 =       num_attention_heads
  									lowercase__							 =       intermediate_size
  									lowercase__							 =       hidden_act
  									lowercase__							 =       hidden_dropout_prob
  									lowercase__							 =       attention_probs_dropout_prob
  									lowercase__							 =       max_position_embeddings
  									lowercase__							 =       type_vocab_size
  									lowercase__							 =       type_sequence_label_size
  									lowercase__							 =       initializer_range
  									lowercase__							 =       num_labels
  									lowercase__							 =       num_choices
  									lowercase__							 =       scope
  									lowercase__							 =       range_bbox
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )
  
  									lowercase__							 =       ids_tensor([self.batch_size, self.seq_length, 4]       ,     self.range_bbox       )
  									# Ensure that bbox is legal
  									for i in range(bbox.shape[0]       ):
  															for j in range(bbox.shape[1]       ):
  																					if bbox[i, j, 3] < bbox[i, j, 1]:
  																											lowercase__							 =       bbox[i, j, 3]
  																											lowercase__							 =       bbox[i, j, 1]
  																											lowercase__							 =       t
  																					if bbox[i, j, 2] < bbox[i, j, 0]:
  																											lowercase__							 =       bbox[i, j, 2]
  																											lowercase__							 =       bbox[i, j, 0]
  																											lowercase__							 =       t
  
  									lowercase__							 =       None
  									if self.use_input_mask:
  															lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2       )
  
  									lowercase__							 =       None
  									if self.use_token_type_ids:
  															lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size       )
  
  									lowercase__							 =       None
  									lowercase__							 =       None
  									lowercase__							 =       None
  									if self.use_labels:
  															lowercase__							 =       ids_tensor([self.batch_size]       ,     self.type_sequence_label_size       )
  															lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels       )
  															lowercase__							 =       ids_tensor([self.batch_size]       ,     self.num_choices       )
  
  									lowercase__							 =       self.get_config()
  
  									return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return LayoutLMConfig(
  									    vocab_size=self.vocab_size       ,     hidden_size=self.hidden_size       ,     num_hidden_layers=self.num_hidden_layers       ,     num_attention_heads=self.num_attention_heads       ,     intermediate_size=self.intermediate_size       ,     hidden_act=self.hidden_act       ,     hidden_dropout_prob=self.hidden_dropout_prob       ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,     max_position_embeddings=self.max_position_embeddings       ,     type_vocab_size=self.type_vocab_size       ,     initializer_range=self.initializer_range       ,     )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       LayoutLMModel(config=lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(lowerCamelCase__       ,     lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )
  									lowercase__							 =       model(lowerCamelCase__       ,     lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )
  									lowercase__							 =       model(lowerCamelCase__       ,     lowerCamelCase__       )
  									self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )
  									self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.hidden_size)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       LayoutLMForMaskedLM(config=lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(lowerCamelCase__       ,     lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )
  									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       LayoutLMForSequenceClassification(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(
  									    lowerCamelCase__       ,     lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )
  									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       LayoutLMForTokenClassification(config=lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(lowerCamelCase__       ,     lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )
  									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       LayoutLMForQuestionAnswering(config=lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(
  									    lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     start_positions=lowerCamelCase__       ,     end_positions=lowerCamelCase__       ,     )
  									self.parent.assertEqual(result.start_logits.shape       ,     (self.batch_size, self.seq_length)       )
  									self.parent.assertEqual(result.end_logits.shape       ,     (self.batch_size, self.seq_length)       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.prepare_config_and_inputs()
  									(
  									    (
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						
  									)							 =       config_and_inputs
  									lowercase__							 =       {
  									    """input_ids""": input_ids,
  									    """bbox""": bbox,
  									    """token_type_ids""": token_type_ids,
  									    """attention_mask""": input_mask,
  									}
  									return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							Tuple       		=  (
  			    (
  			        LayoutLMModel,
  			        LayoutLMForMaskedLM,
  			        LayoutLMForSequenceClassification,
  			        LayoutLMForTokenClassification,
  			        LayoutLMForQuestionAnswering,
  			    )
  			    if is_torch_available()
  			    else None
  			)
  			lowerCamelCase							:							Optional[Any]       		=  (
  			    {
  			        """document-question-answering""": LayoutLMForQuestionAnswering,
  			        """feature-extraction""": LayoutLMModel,
  			        """fill-mask""": LayoutLMForMaskedLM,
  			        """text-classification""": LayoutLMForSequenceClassification,
  			        """token-classification""": LayoutLMForTokenClassification,
  			        """zero-shot""": LayoutLMForSequenceClassification,
  			    }
  			    if is_torch_available()
  			    else {}
  			)
  			lowerCamelCase							:							Optional[Any]       		=  True
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       LayoutLMModelTester(self       )
  									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     hidden_size=37       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.config_tester.run_common_tests()
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_model(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									for type in ["absolute", "relative_key", "relative_key_query"]:
  															lowercase__							 =       type
  															self.model_tester.create_and_check_model(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )
  
  
  
  			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  
  
  def  _A		(							):
  						# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
  						# fmt: off
  						lowercase__							 =       torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]		,		device=lowercase__	)  # noqa: E231
  						lowercase__							 =       torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]		,		device=lowercase__	)  # noqa: E231
  						lowercase__							 =       torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]		,		device=lowercase__	)  # noqa: E231
  						lowercase__							 =       torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]		,		device=lowercase__	)  # noqa: E231
  						# these are sequence labels (i.e. at the token level)
  						lowercase__							 =       torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]		,		device=lowercase__	)  # noqa: E231
  						# fmt: on
  
  						return input_ids, attention_mask, bbox, token_type_ids, labels
  
  
  
  
  
  
  
  @require_torch
  class 			A							(       unittest.TestCase				):
  
  
  
  			@slow
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""       ).to(lowerCamelCase__       )
  
  									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       prepare_layoutlm_batch_inputs()
  
  									# forward pass
  									lowercase__							 =       model(input_ids=lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )
  
  									# test the sequence output on [0, :3, :3]
  									lowercase__							 =       torch.tensor(
  									    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]       ,     device=lowerCamelCase__       ,     )
  
  									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,     lowerCamelCase__       ,     atol=1e-3       )       )
  
  									# test the pooled output on [1, :3]
  									lowercase__							 =       torch.tensor([-0.65_80, -0.02_14, 0.85_52]       ,     device=lowerCamelCase__       )
  
  									self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]       ,     lowerCamelCase__       ,     atol=1e-3       )       )
  
  
  
  			@slow
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""       ,     num_labels=2       ).to(
  									    lowerCamelCase__       )
  
  									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       prepare_layoutlm_batch_inputs()
  
  									# forward pass
  									lowercase__							 =       model(
  									    input_ids=lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=torch.tensor([1, 1]       ,     device=lowerCamelCase__       )       ,     )
  
  									# test whether we get a loss as a scalar
  									lowercase__							 =       outputs.loss
  									lowercase__							 =       torch.Size([]       )
  									self.assertEqual(loss.shape       ,     lowerCamelCase__       )
  
  									# test the shape of the logits
  									lowercase__							 =       outputs.logits
  									lowercase__							 =       torch.Size((2, 2)       )
  									self.assertEqual(logits.shape       ,     lowerCamelCase__       )
  
  
  
  			@slow
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""       ,     num_labels=13       ).to(
  									    lowerCamelCase__       )
  
  									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       prepare_layoutlm_batch_inputs()
  
  									# forward pass
  									lowercase__							 =       model(
  									    input_ids=lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )
  
  									# test the loss calculation to be around 2.65
  									# expected_loss = torch.tensor(2.65, device=torch_device)
  
  									# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
  									# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))
  
  									# test the shape of the logits
  									lowercase__							 =       outputs.logits
  									lowercase__							 =       torch.Size((2, 25, 13)       )
  									self.assertEqual(logits.shape       ,     lowerCamelCase__       )
  
  
  
  			@slow
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""       ).to(lowerCamelCase__       )
  
  									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       prepare_layoutlm_batch_inputs()
  
  									# forward pass
  									lowercase__							 =       model(input_ids=lowerCamelCase__       ,     bbox=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )
  
  									# test the shape of the logits
  									lowercase__							 =       torch.Size((2, 25)       )
  									self.assertEqual(outputs.start_logits.shape       ,     lowerCamelCase__       )
  									self.assertEqual(outputs.end_logits.shape       ,     lowerCamelCase__       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=1	):
  						if n_shave_prefix_segments >= 0:
  												return ".".join(path.split("""."""	)[n_shave_prefix_segments:]	)
  						else:
  												return ".".join(path.split("""."""	)[:n_shave_prefix_segments]	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0	):
  						lowercase__							 =       []
  						for old_item in old_list:
  												lowercase__							 =       old_item
  												lowercase__							 =       new_item.replace("""block."""		,		"""resnets."""	)
  												lowercase__							 =       new_item.replace("""conv_shorcut"""		,		"""conv1"""	)
  												lowercase__							 =       new_item.replace("""in_shortcut"""		,		"""conv_shortcut"""	)
  												lowercase__							 =       new_item.replace("""temb_proj"""		,		"""time_emb_proj"""	)
  
  												lowercase__							 =       shave_segments(lowercase__		,		n_shave_prefix_segments=lowercase__	)
  
  												mapping.append({"""old""": old_item, """new""": new_item}	)
  
  						return mapping
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0		,		lowercase__=False	):
  						lowercase__							 =       []
  						for old_item in old_list:
  												lowercase__							 =       old_item
  
  												# In `model.mid`, the layer is called `attn`.
  												if not in_mid:
  																		lowercase__							 =       new_item.replace("""attn"""		,		"""attentions"""	)
  												lowercase__							 =       new_item.replace(""".k."""		,		""".key."""	)
  												lowercase__							 =       new_item.replace(""".v."""		,		""".value."""	)
  												lowercase__							 =       new_item.replace(""".q."""		,		""".query."""	)
  
  												lowercase__							 =       new_item.replace("""proj_out"""		,		"""proj_attn"""	)
  												lowercase__							 =       new_item.replace("""norm"""		,		"""group_norm"""	)
  
  												lowercase__							 =       shave_segments(lowercase__		,		n_shave_prefix_segments=lowercase__	)
  												mapping.append({"""old""": old_item, """new""": new_item}	)
  
  						return mapping
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=None		,		lowercase__=None		,		lowercase__=None	):
  						assert isinstance(lowercase__		,		lowercase__	), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
  						if attention_paths_to_split is not None:
  												if config is None:
  																		raise ValueError("""Please specify the config if setting 'attention_paths_to_split' to 'True'."""	)
  
  												for path, path_map in attention_paths_to_split.items():
  																		lowercase__							 =       old_checkpoint[path]
  																		lowercase__							 =       old_tensor.shape[0] // 3
  
  																		lowercase__							 =       (-1, channels) if len(old_tensor.shape	) == 3 else (-1)
  
  																		lowercase__							 =       old_tensor.shape[0] // config.get("""num_head_channels"""		,		1	) // 3
  
  																		lowercase__							 =       old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
  																		lowercase__ ,						lowercase__ ,						lowercase__							 =       old_tensor.split(channels // num_heads		,		dim=1	)
  
  																		lowercase__							 =       query.reshape(lowercase__	).squeeze()
  																		lowercase__							 =       key.reshape(lowercase__	).squeeze()
  																		lowercase__							 =       value.reshape(lowercase__	).squeeze()
  
  						for path in paths:
  												lowercase__							 =       path["""new"""]
  
  												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  																		continue
  
  												lowercase__							 =       new_path.replace("""down."""		,		"""down_blocks."""	)
  												lowercase__							 =       new_path.replace("""up."""		,		"""up_blocks."""	)
  
  												if additional_replacements is not None:
  																		for replacement in additional_replacements:
  																								lowercase__							 =       new_path.replace(replacement["""old"""]		,		replacement["""new"""]	)
  
  												if "attentions" in new_path:
  																		lowercase__							 =       old_checkpoint[path["""old"""]].squeeze()
  												else:
  																		lowercase__							 =       old_checkpoint[path["""old"""]]
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       {}
  
  						lowercase__							 =       checkpoint["""temb.dense.0.weight"""]
  						lowercase__							 =       checkpoint["""temb.dense.0.bias"""]
  						lowercase__							 =       checkpoint["""temb.dense.1.weight"""]
  						lowercase__							 =       checkpoint["""temb.dense.1.bias"""]
  
  						lowercase__							 =       checkpoint["""norm_out.weight"""]
  						lowercase__							 =       checkpoint["""norm_out.bias"""]
  
  						lowercase__							 =       checkpoint["""conv_in.weight"""]
  						lowercase__							 =       checkpoint["""conv_in.bias"""]
  						lowercase__							 =       checkpoint["""conv_out.weight"""]
  						lowercase__							 =       checkpoint["""conv_out.bias"""]
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:2]	) for layer in checkpoint if """down""" in layer}	)
  						lowercase__							 =       {
  						    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowercase__	)
  						}
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:2]	) for layer in checkpoint if """up""" in layer}	)
  						lowercase__							 =       {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(lowercase__	)}
  
  						for i in range(lowercase__	):
  												lowercase__							 =       (i - 1) // (config["""layers_per_block"""] + 1)
  
  												if any("""downsample""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''down.{i}.downsample.op.weight'''
  																		]
  																		lowercase__							 =       checkpoint[f'''down.{i}.downsample.op.bias''']
  												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
  												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
  
  												if any("""block""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in down_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
  																		    for layer_id in range(lowercase__	)
  																		}
  
  																		if num_blocks > 0:
  																								for j in range(config["""layers_per_block"""]	):
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__	)
  
  												if any("""attn""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in down_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
  																		    for layer_id in range(lowercase__	)
  																		}
  
  																		if num_attn > 0:
  																								for j in range(config["""layers_per_block"""]	):
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		config=lowercase__	)
  
  						lowercase__							 =       [key for key in checkpoint if """mid.block_1""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.block_2""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.attn_1""" in key]
  
  						# Mid new 2
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]		,		)
  
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]		,		)
  
  						lowercase__							 =       renew_attention_paths(lowercase__		,		in_mid=lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]		,		)
  
  						for i in range(lowercase__	):
  												lowercase__							 =       num_up_blocks - 1 - i
  
  												if any("""upsample""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''up.{i}.upsample.conv.weight'''
  																		]
  																		lowercase__							 =       checkpoint[f'''up.{i}.upsample.conv.bias''']
  
  												if any("""block""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in up_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(lowercase__	)
  																		}
  
  																		if num_blocks > 0:
  																								for j in range(config["""layers_per_block"""] + 1	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  												if any("""attn""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in up_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(lowercase__	)
  																		}
  
  																		if num_attn > 0:
  																								for j in range(config["""layers_per_block"""] + 1	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  						lowercase__							 =       {k.replace("""mid_new_2"""		,		"""mid_block"""	): v for k, v in new_checkpoint.items()}
  						return new_checkpoint
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       {}
  
  						lowercase__							 =       checkpoint["""encoder.norm_out.weight"""]
  						lowercase__							 =       checkpoint["""encoder.norm_out.bias"""]
  
  						lowercase__							 =       checkpoint["""encoder.conv_in.weight"""]
  						lowercase__							 =       checkpoint["""encoder.conv_in.bias"""]
  						lowercase__							 =       checkpoint["""encoder.conv_out.weight"""]
  						lowercase__							 =       checkpoint["""encoder.conv_out.bias"""]
  
  						lowercase__							 =       checkpoint["""decoder.norm_out.weight"""]
  						lowercase__							 =       checkpoint["""decoder.norm_out.bias"""]
  
  						lowercase__							 =       checkpoint["""decoder.conv_in.weight"""]
  						lowercase__							 =       checkpoint["""decoder.conv_in.bias"""]
  						lowercase__							 =       checkpoint["""decoder.conv_out.weight"""]
  						lowercase__							 =       checkpoint["""decoder.conv_out.bias"""]
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:3]	) for layer in checkpoint if """down""" in layer}	)
  						lowercase__							 =       {
  						    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowercase__	)
  						}
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:3]	) for layer in checkpoint if """up""" in layer}	)
  						lowercase__							 =       {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(lowercase__	)}
  
  						for i in range(lowercase__	):
  												lowercase__							 =       (i - 1) // (config["""layers_per_block"""] + 1)
  
  												if any("""downsample""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''encoder.down.{i}.downsample.conv.weight'''
  																		]
  																		lowercase__							 =       checkpoint[
  																		    f'''encoder.down.{i}.downsample.conv.bias'''
  																		]
  
  												if any("""block""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in down_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
  																		    for layer_id in range(lowercase__	)
  																		}
  
  																		if num_blocks > 0:
  																								for j in range(config["""layers_per_block"""]	):
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__	)
  
  												if any("""attn""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in down_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
  																		    for layer_id in range(lowercase__	)
  																		}
  
  																		if num_attn > 0:
  																								for j in range(config["""layers_per_block"""]	):
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		config=lowercase__	)
  
  						lowercase__							 =       [key for key in checkpoint if """mid.block_1""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.block_2""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.attn_1""" in key]
  
  						# Mid new 2
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]		,		)
  
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]		,		)
  
  						lowercase__							 =       renew_attention_paths(lowercase__		,		in_mid=lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]		,		)
  
  						for i in range(lowercase__	):
  												lowercase__							 =       num_up_blocks - 1 - i
  
  												if any("""upsample""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''decoder.up.{i}.upsample.conv.weight'''
  																		]
  																		lowercase__							 =       checkpoint[
  																		    f'''decoder.up.{i}.upsample.conv.bias'''
  																		]
  
  												if any("""block""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in up_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(lowercase__	)
  																		}
  
  																		if num_blocks > 0:
  																								for j in range(config["""layers_per_block"""] + 1	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  												if any("""attn""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in up_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(lowercase__	)
  																		}
  
  																		if num_attn > 0:
  																								for j in range(config["""layers_per_block"""] + 1	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  						lowercase__							 =       {k.replace("""mid_new_2"""		,		"""mid_block"""	): v for k, v in new_checkpoint.items()}
  						lowercase__							 =       checkpoint["""quant_conv.weight"""]
  						lowercase__							 =       checkpoint["""quant_conv.bias"""]
  						if "quantize.embedding.weight" in checkpoint:
  												lowercase__							 =       checkpoint["""quantize.embedding.weight"""]
  						lowercase__							 =       checkpoint["""post_quant_conv.weight"""]
  						lowercase__							 =       checkpoint["""post_quant_conv.bias"""]
  
  						return new_checkpoint
  
  
  if __name__ == "__main__":
  				__A          =    argparse.ArgumentParser()
  
  				parser.add_argument(
  				    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  				)
  
  				parser.add_argument(
  				    "--config_file",
  				    default=None,
  				    type=str,
  				    required=True,
  				    help="The config json file corresponding to the architecture.",
  				)
  
  				parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
  
  				__A          =    parser.parse_args()
  				__A          =    torch.load(args.checkpoint_path)
  
  				with open(args.config_file) as f:
  								__A          =    json.loads(f.read())
  
  				# unet case
  				__A          =    {key.split(".")[0] for key in checkpoint.keys()}
  				if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  								__A          =    convert_vq_autoenc_checkpoint(checkpoint, config)
  				else:
  								__A          =    convert_ddpm_checkpoint(checkpoint, config)
  
  				if "ddpm" in config:
  								del config["ddpm"]
  
  				if config["_class_name"] == "VQModel":
  								__A          =    VQModel(**config)
  								model.load_state_dict(converted_checkpoint)
  								model.save_pretrained(args.dump_path)
  				elif config["_class_name"] == "AutoencoderKL":
  								__A          =    AutoencoderKL(**config)
  								model.load_state_dict(converted_checkpoint)
  								model.save_pretrained(args.dump_path)
  				else:
  								__A          =    UNetaDModel(**config)
  								model.load_state_dict(converted_checkpoint)
  
  								__A          =    DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
  
  								__A          =    DDPMPipeline(unet=model, scheduler=scheduler)
  								pipe.save_pretrained(args.dump_path)
  
  
  
  
  
  
  