





"""simple docstring"""




from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel




@require_tf
class 				_a		:
				UpperCamelCase			=					MarianConfig
				UpperCamelCase			=					{}
				UpperCamelCase			=					'''gelu'''






				def __init__(				self	:					Union[str, Any],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Dict=1_3,				lowerCAmelCase__	:					Union[str, Any]=7,				lowerCAmelCase__	:					int=True,				lowerCAmelCase__	:					Dict=False,				lowerCAmelCase__	:					Dict=9_9,				lowerCAmelCase__	:					Tuple=3_2,				lowerCAmelCase__	:					Optional[Any]=2,				lowerCAmelCase__	:					Tuple=4,				lowerCAmelCase__	:					List[str]=3_7,				lowerCAmelCase__	:					Any=0.1,				lowerCAmelCase__	:					Tuple=0.1,				lowerCAmelCase__	:					Dict=2_0,				lowerCAmelCase__	:					List[str]=2,				lowerCAmelCase__	:					Tuple=1,				lowerCAmelCase__	:					List[str]=0,				)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					parent
										_UpperCamelCase    :      List[Any]						     =					batch_size
										_UpperCamelCase    :      Tuple						     =					seq_length
										_UpperCamelCase    :      Dict						     =					is_training
										_UpperCamelCase    :      Union[str, Any]						     =					use_labels
										_UpperCamelCase    :      Tuple						     =					vocab_size
										_UpperCamelCase    :      Optional[int]						     =					hidden_size
										_UpperCamelCase    :      Any						     =					num_hidden_layers
										_UpperCamelCase    :      Union[str, Any]						     =					num_attention_heads
										_UpperCamelCase    :      Dict						     =					intermediate_size

										_UpperCamelCase    :      Tuple						     =					hidden_dropout_prob
										_UpperCamelCase    :      Optional[Any]						     =					attention_probs_dropout_prob
										_UpperCamelCase    :      Tuple						     =					max_position_embeddings
										_UpperCamelCase    :      Optional[Any]						     =					eos_token_id
										_UpperCamelCase    :      Optional[int]						     =					pad_token_id
										_UpperCamelCase    :      List[str]						     =					bos_token_id






				def        snake_case			(				self	:					List[Any]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					ids_tensor([self.batch_size, self.seq_length - 1],				self.vocab_size							)
										_UpperCamelCase    :      Any						     =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							),				1							)
										_UpperCamelCase    :      Optional[int]						     =					tf.concat([input_ids, eos_tensor],				axis=1							)

										_UpperCamelCase    :      Any						     =					ids_tensor([self.batch_size, self.seq_length],				self.vocab_size							)

										_UpperCamelCase    :      Dict						     =					self.config_cls(
										    vocab_size=self.vocab_size,				d_model=self.hidden_size,				encoder_layers=self.num_hidden_layers,				decoder_layers=self.num_hidden_layers,				encoder_attention_heads=self.num_attention_heads,				decoder_attention_heads=self.num_attention_heads,				encoder_ffn_dim=self.intermediate_size,				decoder_ffn_dim=self.intermediate_size,				dropout=self.hidden_dropout_prob,				attention_dropout=self.attention_probs_dropout_prob,				max_position_embeddings=self.max_position_embeddings,				eos_token_ids=[2],				bos_token_id=self.bos_token_id,				pad_token_id=self.pad_token_id,				decoder_start_token_id=self.pad_token_id,				**self.config_updates,				)
										_UpperCamelCase    :      Optional[int]						     =					prepare_marian_inputs_dict(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)
										return config, inputs_dict






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Any							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					TFMarianModel(config=lowerCAmelCase__							).get_decoder()
										_UpperCamelCase    :      Tuple						     =					inputs_dict['''input_ids''']

										_UpperCamelCase    :      Optional[Any]						     =					input_ids[:1, :]
										_UpperCamelCase    :      int						     =					inputs_dict['''attention_mask'''][:1, :]
										_UpperCamelCase    :      Dict						     =					inputs_dict['''head_mask''']
										_UpperCamelCase    :      int						     =					1

										# first forward pass
										_UpperCamelCase    :      Tuple						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				head_mask=lowerCAmelCase__,				use_cache=lowerCAmelCase__							)

										_UpperCamelCase     ,							_UpperCamelCase    :      Optional[Any]						     =					outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										_UpperCamelCase    :      Tuple						     =					ids_tensor((self.batch_size, 3),				config.vocab_size							)
										_UpperCamelCase    :      Union[str, Any]						     =					tf.cast(ids_tensor((self.batch_size, 3),				2							),				tf.inta							)

										# append to next input_ids and
										_UpperCamelCase    :      int						     =					tf.concat([input_ids, next_tokens],				axis=-1							)
										_UpperCamelCase    :      Optional[int]						     =					tf.concat([attention_mask, next_attn_mask],				axis=-1							)

										_UpperCamelCase    :      List[Any]						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__							)[0]
										_UpperCamelCase    :      Tuple						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				past_key_values=lowerCAmelCase__							)[0]

										self.parent.assertEqual(next_tokens.shape[1],				output_from_past.shape[1]							)

										# select random slice
										_UpperCamelCase    :      int						     =					int(ids_tensor((1,),				output_from_past.shape[-1]							)							)
										_UpperCamelCase    :      Any						     =					output_from_no_past[:, -3:, random_slice_idx]
										_UpperCamelCase    :      List[Any]						     =					output_from_past[:, :, random_slice_idx]

										# test that outputs are equal for slice
										tf.debugging.assert_near(lowerCAmelCase__,				lowerCAmelCase__,				rtol=1e-3							)





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase=None ,		_lowercase=None ,		_lowercase=None ,		_lowercase=None ,		_lowercase=None ,		):
						if attention_mask is None:
												_UpperCamelCase    :      List[Any]						     =					tf.cast(tf.math.not_equal(_lowercase ,		config.pad_token_id     ) ,		tf.inta     )
						if decoder_attention_mask is None:
												_UpperCamelCase    :      str						     =					tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape ,		dtype=tf.inta     ),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,		config.pad_token_id     ) ,		tf.inta     ),
												    ] ,		axis=-1 ,		)
						if head_mask is None:
												_UpperCamelCase    :      List[Any]						     =					tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
						if decoder_head_mask is None:
												_UpperCamelCase    :      int						     =					tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
						if cross_attn_head_mask is None:
												_UpperCamelCase    :      Union[str, Any]						     =					tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}




@require_tf
class 				_a		(   _lowerCAmelCase			,       _lowerCAmelCase			,       unittest.TestCase       ):
				UpperCamelCase			=					(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
				UpperCamelCase			=					(TFMarianMTModel,) if is_tf_available() else ()
				UpperCamelCase			=					(
				    {
				        '''conversational''': TFMarianMTModel,
				        '''feature-extraction''': TFMarianModel,
				        '''summarization''': TFMarianMTModel,
				        '''text2text-generation''': TFMarianMTModel,
				        '''translation''': TFMarianMTModel,
				    }
				    if is_tf_available()
				    else {}
				)
				UpperCamelCase			=					True
				UpperCamelCase			=					False
				UpperCamelCase			=					False






				def        snake_case			(				self	:					Tuple							)			->   Optional[int]:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					TFMarianModelTester(self							)
										_UpperCamelCase    :      Dict						     =					ConfigTester(self,				config_class=lowerCAmelCase__							)






				def        snake_case			(				self	:					List[str]							)			->   List[Any]:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def        snake_case			(				self	:					int							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__							)




@require_tf
class 				_a		(   unittest.TestCase       ):
				UpperCamelCase			=					1000  # show more chars for failing integration tests






				@classmethod
				def        snake_case			(				cls	:					List[str]							)			->   None:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
										return cls






				@cached_property
				def        snake_case			(				self	:					int							)			->   MarianTokenizer:




										'''simple docstring'''



										return AutoTokenizer.from_pretrained(self.model_name							)






				@property
				def        snake_case			(				self	:					Union[str, Any]							)			->   int:




										'''simple docstring'''



										return self.tokenizer.eos_token_id






				@cached_property
				def        snake_case			(				self	:					Any							)			->   Any:




										'''simple docstring'''



										warnings.simplefilter('''error'''							)
										_UpperCamelCase    :      TFMarianMTModel						     =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
										assert isinstance(lowerCAmelCase__,				lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					model.config
										self.assertListEqual(c.bad_words_ids,				[[c.pad_token_id]]							)
										self.assertEqual(c.max_length,				5_1_2							)
										self.assertEqual(c.decoder_start_token_id,				c.pad_token_id							)
										return model






				def        snake_case			(				self	:					str,				**lowerCAmelCase__	:					str							)			->   Optional[int]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					self.translate_src_text(**lowerCAmelCase__							)
										self.assertListEqual(self.expected_text,				lowerCAmelCase__							)






				def        snake_case			(				self	:					Dict,				**lowerCAmelCase__	:					Union[str, Any]							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					self.tokenizer(self.src_text,				**lowerCAmelCase__,				padding=lowerCAmelCase__,				return_tensors='''tf'''							)
										_UpperCamelCase    :      Optional[int]						     =					self.model.generate(
										    model_inputs.input_ids,				attention_mask=model_inputs.attention_mask,				num_beams=2,				max_length=1_2_8							)
										_UpperCamelCase    :      Union[str, Any]						     =					self.tokenizer.batch_decode(generated_ids.numpy(),				skip_special_tokens=lowerCAmelCase__							)
										return generated_words




@require_sentencepiece
@require_tokenizers
@require_tf
class 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					'''mt'''
				UpperCamelCase			=					'''en'''
				UpperCamelCase			=					['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
				UpperCamelCase			=					['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']






				@unittest.skip('''Skipping until #12647 is resolved.'''							)
				@slow
				def        snake_case			(				self	:					Union[str, Any]							)			->   Optional[Any]:




										'''simple docstring'''



										self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					'''en'''
				UpperCamelCase			=					'''zh'''
				UpperCamelCase			=					['''My name is Wolfgang and I live in Berlin''']
				UpperCamelCase			=					['''我叫沃尔夫冈 我住在柏林''']






				@unittest.skip('''Skipping until #12647 is resolved.'''							)
				@slow
				def        snake_case			(				self	:					Any							)			->   str:




										'''simple docstring'''



										self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					'''en'''
				UpperCamelCase			=					'''ROMANCE'''
				UpperCamelCase			=					[
				    '''>>fr<< Don\'t spend so much time watching TV.''',
				    '''>>pt<< Your message has been sent.''',
				    '''>>es<< He\'s two years older than me.''',
				]
				UpperCamelCase			=					[
				    '''Ne passez pas autant de temps à regarder la télé.''',
				    '''A sua mensagem foi enviada.''',
				    '''Es dos años más viejo que yo.''',
				]






				@unittest.skip('''Skipping until #12647 is resolved.'''							)
				@slow
				def        snake_case			(				self	:					Tuple							)			->   Tuple:




										'''simple docstring'''



										self._assert_generated_batch_equal_expected()






				@unittest.skip('''Skipping until #12647 is resolved.'''							)
				@slow
				def        snake_case			(				self	:					List[Any]							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					TranslationPipeline(self.model,				self.tokenizer,				framework='''tf'''							)
										_UpperCamelCase    :      List[Any]						     =					pipeline(self.src_text							)
										self.assertEqual(self.expected_text,				[x['''translation_text'''] for x in output]							)










"""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     ):
						_UpperCamelCase    :      Dict						     =					[]
						for old_item in old_list:
												_UpperCamelCase    :      Tuple						     =					old_item
												_UpperCamelCase    :      Optional[Any]						     =					new_item.replace('''block.''' ,		'''resnets.'''     )
												_UpperCamelCase    :      int						     =					new_item.replace('''conv_shorcut''' ,		'''conv1'''     )
												_UpperCamelCase    :      List[str]						     =					new_item.replace('''in_shortcut''' ,		'''conv_shortcut'''     )
												_UpperCamelCase    :      Union[str, Any]						     =					new_item.replace('''temb_proj''' ,		'''time_emb_proj'''     )

												_UpperCamelCase    :      List[Any]						     =					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     ):
						_UpperCamelCase    :      str						     =					[]
						for old_item in old_list:
												_UpperCamelCase    :      Tuple						     =					old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		_UpperCamelCase    :      str						     =					new_item.replace('''attn''' ,		'''attentions'''     )
												_UpperCamelCase    :      Dict						     =					new_item.replace('''.k.''' ,		'''.key.'''     )
												_UpperCamelCase    :      Optional[int]						     =					new_item.replace('''.v.''' ,		'''.value.'''     )
												_UpperCamelCase    :      Optional[int]						     =					new_item.replace('''.q.''' ,		'''.query.'''     )

												_UpperCamelCase    :      Optional[Any]						     =					new_item.replace('''proj_out''' ,		'''proj_attn'''     )
												_UpperCamelCase    :      Dict						     =					new_item.replace('''norm''' ,		'''group_norm'''     )

												_UpperCamelCase    :      str						     =					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():
																		_UpperCamelCase    :      Union[str, Any]						     =					old_checkpoint[path]
																		_UpperCamelCase    :      Any						     =					old_tensor.shape[0] // 3

																		_UpperCamelCase    :      List[str]						     =					(-1, channels) if len(old_tensor.shape     ) == 3 else (-1)

																		_UpperCamelCase    :      Dict						     =					old_tensor.shape[0] // config.get('''num_head_channels''' ,		1     ) // 3

																		_UpperCamelCase    :      List[Any]						     =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]     )
																		_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase    :      Optional[int]						     =					old_tensor.split(channels // num_heads ,		dim=1     )

																		_UpperCamelCase    :      Union[str, Any]						     =					query.reshape(_lowercase     ).squeeze()
																		_UpperCamelCase    :      Any						     =					key.reshape(_lowercase     ).squeeze()
																		_UpperCamelCase    :      str						     =					value.reshape(_lowercase     ).squeeze()

						for path in paths:
												_UpperCamelCase    :      List[str]						     =					path['''new''']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												_UpperCamelCase    :      List[str]						     =					new_path.replace('''down.''' ,		'''down_blocks.'''     )
												_UpperCamelCase    :      Optional[int]						     =					new_path.replace('''up.''' ,		'''up_blocks.'''     )

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								_UpperCamelCase    :      str						     =					new_path.replace(replacement['''old'''] ,		replacement['''new''']     )

												if "attentions" in new_path:
																		_UpperCamelCase    :      Optional[int]						     =					old_checkpoint[path['''old''']].squeeze()
												else:
																		_UpperCamelCase    :      Union[str, Any]						     =					old_checkpoint[path['''old''']]





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Union[str, Any]						     =					{}

						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.0.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.0.bias''']
						_UpperCamelCase    :      List[Any]						     =					checkpoint['''temb.dense.1.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.1.bias''']

						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''norm_out.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''norm_out.bias''']

						_UpperCamelCase    :      Dict						     =					checkpoint['''conv_in.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''conv_in.bias''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''conv_out.weight''']
						_UpperCamelCase    :      str						     =					checkpoint['''conv_out.bias''']

						_UpperCamelCase    :      Optional[int]						     =					len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''down''' in layer}     )
						_UpperCamelCase    :      Union[str, Any]						     =					{
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase     )
						}

						_UpperCamelCase    :      int						     =					len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''up''' in layer}     )
						_UpperCamelCase    :      Any						     =					{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     ):
												_UpperCamelCase    :      Optional[Any]						     =					(i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Tuple						     =					checkpoint[
																		    F"""down.{i}.downsample.op.weight"""
																		]
																		_UpperCamelCase    :      Union[str, Any]						     =					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]     ):
																		_UpperCamelCase    :      Optional[int]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) for layer in down_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      Optional[int]						     =					{
																		    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''']     ):
																														_UpperCamelCase    :      str						     =					renew_resnet_paths(blocks[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase     )

												if any('''attn''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Any						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) for layer in down_blocks[i] if '''attn''' in layer}     )
																		_UpperCamelCase    :      str						     =					{
																		    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''']     ):
																														_UpperCamelCase    :      Union[str, Any]						     =					renew_attention_paths(attns[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		config=_lowercase     )

						_UpperCamelCase    :      Dict						     =					[key for key in checkpoint if '''mid.block_1''' in key]
						_UpperCamelCase    :      int						     =					[key for key in checkpoint if '''mid.block_2''' in key]
						_UpperCamelCase    :      Tuple						     =					[key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						_UpperCamelCase    :      Tuple						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}] ,		)

						_UpperCamelCase    :      int						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}] ,		)

						_UpperCamelCase    :      str						     =					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     ):
												_UpperCamelCase    :      List[str]						     =					num_up_blocks - 1 - i

												if any('''upsample''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      List[str]						     =					checkpoint[
																		    F"""up.{i}.upsample.conv.weight"""
																		]
																		_UpperCamelCase    :      List[str]						     =					checkpoint[F"""up.{i}.upsample.conv.bias"""]

												if any('''block''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      Union[str, Any]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) for layer in up_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      Union[str, Any]						     =					{
																		    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     ):
																														_UpperCamelCase    :      Optional[int]						     =					{'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
																														_UpperCamelCase    :      Optional[int]						     =					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]     ):
																		_UpperCamelCase    :      Dict						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) for layer in up_blocks[i] if '''attn''' in layer}     )
																		_UpperCamelCase    :      Optional[Any]						     =					{
																		    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     ):
																														_UpperCamelCase    :      int						     =					{'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
																														_UpperCamelCase    :      List[Any]						     =					renew_attention_paths(attns[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[replace_indices]     )

						_UpperCamelCase    :      int						     =					{k.replace('''mid_new_2''' ,		'''mid_block'''     ): v for k, v in new_checkpoint.items()}
						return new_checkpoint





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      List[Any]						     =					{}

						_UpperCamelCase    :      List[str]						     =					checkpoint['''encoder.norm_out.weight''']
						_UpperCamelCase    :      Tuple						     =					checkpoint['''encoder.norm_out.bias''']

						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''encoder.conv_in.weight''']
						_UpperCamelCase    :      Any						     =					checkpoint['''encoder.conv_in.bias''']
						_UpperCamelCase    :      Optional[int]						     =					checkpoint['''encoder.conv_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''encoder.conv_out.bias''']

						_UpperCamelCase    :      Dict						     =					checkpoint['''decoder.norm_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.norm_out.bias''']

						_UpperCamelCase    :      str						     =					checkpoint['''decoder.conv_in.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.conv_in.bias''']
						_UpperCamelCase    :      List[Any]						     =					checkpoint['''decoder.conv_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.conv_out.bias''']

						_UpperCamelCase    :      Tuple						     =					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''down''' in layer}     )
						_UpperCamelCase    :      Optional[int]						     =					{
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase     )
						}

						_UpperCamelCase    :      List[str]						     =					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''up''' in layer}     )
						_UpperCamelCase    :      Tuple						     =					{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     ):
												_UpperCamelCase    :      List[Any]						     =					(i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Any						     =					checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.weight"""
																		]
																		_UpperCamelCase    :      Tuple						     =					checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.bias"""
																		]

												if any('''block''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Optional[Any]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		3     ).split('''.'''     )[:3]     ) for layer in down_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      int						     =					{
																		    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''']     ):
																														_UpperCamelCase    :      Optional[int]						     =					renew_resnet_paths(blocks[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase     )

												if any('''attn''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      List[Any]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		3     ).split('''.'''     )[:3]     ) for layer in down_blocks[i] if '''attn''' in layer}     )
																		_UpperCamelCase    :      Optional[Any]						     =					{
																		    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''']     ):
																														_UpperCamelCase    :      Tuple						     =					renew_attention_paths(attns[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		config=_lowercase     )

						_UpperCamelCase    :      List[Any]						     =					[key for key in checkpoint if '''mid.block_1''' in key]
						_UpperCamelCase    :      Union[str, Any]						     =					[key for key in checkpoint if '''mid.block_2''' in key]
						_UpperCamelCase    :      int						     =					[key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						_UpperCamelCase    :      int						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}] ,		)

						_UpperCamelCase    :      Any						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}] ,		)

						_UpperCamelCase    :      Any						     =					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     ):
												_UpperCamelCase    :      Dict						     =					num_up_blocks - 1 - i

												if any('''upsample''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      Tuple						     =					checkpoint[
																		    F"""decoder.up.{i}.upsample.conv.weight"""
																		]
																		_UpperCamelCase    :      Dict						     =					checkpoint[
																		    F"""decoder.up.{i}.upsample.conv.bias"""
																		]

												if any('''block''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      int						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		3     ).split('''.'''     )[:3]     ) for layer in up_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      str						     =					{
																		    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     ):
																														_UpperCamelCase    :      List[str]						     =					{'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
																														_UpperCamelCase    :      str						     =					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]     ):
																		_UpperCamelCase    :      Optional[int]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		3     ).split('''.'''     )[:3]     ) for layer in up_blocks[i] if '''attn''' in layer}     )
																		_UpperCamelCase    :      Any						     =					{
																		    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     ):
																														_UpperCamelCase    :      Dict						     =					{'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
																														_UpperCamelCase    :      Dict						     =					renew_attention_paths(attns[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[replace_indices]     )

						_UpperCamelCase    :      int						     =					{k.replace('''mid_new_2''' ,		'''mid_block'''     ): v for k, v in new_checkpoint.items()}
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''quant_conv.weight''']
						_UpperCamelCase    :      Tuple						     =					checkpoint['''quant_conv.bias''']
						if "quantize.embedding.weight" in checkpoint:
												_UpperCamelCase    :      Any						     =					checkpoint['''quantize.embedding.weight''']
						_UpperCamelCase    :      Tuple						     =					checkpoint['''post_quant_conv.weight''']
						_UpperCamelCase    :      int						     =					checkpoint['''post_quant_conv.bias''']

						return new_checkpoint


if __name__ == "__main__":
						UpperCamelCase_     =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.""")

						UpperCamelCase_     =parser.parse_args()
						UpperCamelCase_     =torch.load(args.checkpoint_path)

						with open(args.config_file) as f:
												UpperCamelCase_     =json.loads(f.read())

						# unet case
						UpperCamelCase_     ={key.split(""".""")[0] for key in checkpoint.keys()}
						if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
												UpperCamelCase_     =convert_vq_autoenc_checkpoint(checkpoint, config)
						else:
												UpperCamelCase_     =convert_ddpm_checkpoint(checkpoint, config)

						if "ddpm" in config:
												del config["ddpm"]

						if config["_class_name"] == "VQModel":
												UpperCamelCase_     =VQModel(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						elif config["_class_name"] == "AutoencoderKL":
												UpperCamelCase_     =AutoencoderKL(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						else:
												UpperCamelCase_     =UNetaDModel(**config)
												model.load_state_dict(converted_checkpoint)

												UpperCamelCase_     =DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

												UpperCamelCase_     =DDPMPipeline(unet=model, scheduler=scheduler)
												pipe.save_pretrained(args.dump_path)



