code: import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import OPTConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
  
  
  if is_flax_available():
   import os
  
   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   a     :			Dict					    =						"platform"
  
   import jax
   import jax.numpy as jnp
  
   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase=None       ,							lowercase=None				):
  
  
  
  
  
  
  
     if attention_mask is None:
        lowerCAmelCase_:       List[str]         =  np.where(input_ids != config.pad_token_id       ,							1       ,							0				)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }
  
  @require_flax
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=13					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__=99					,	lowerCamelCase__=16					,	lowerCamelCase__=2					,	lowerCamelCase__=4					,	lowerCamelCase__=4					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=20					,	lowerCamelCase__=2					,	lowerCamelCase__=1					,	lowerCamelCase__=0					,	lowerCamelCase__=16					,	lowerCamelCase__=16					,	lowerCamelCase__=0.0_2					,	):
         lowerCAmelCase_:       Optional[int]         =  parent
         lowerCAmelCase_:       str         =  batch_size
         lowerCAmelCase_:       Any         =  seq_length
         lowerCAmelCase_:       Union[str, Any]         =  is_training
         lowerCAmelCase_:       Union[str, Any]         =  use_labels
         lowerCAmelCase_:       Optional[int]         =  vocab_size
         lowerCAmelCase_:       int         =  hidden_size
         lowerCAmelCase_:       Optional[int]         =  num_hidden_layers
         lowerCAmelCase_:       Dict         =  num_attention_heads
         lowerCAmelCase_:       str         =  intermediate_size
         lowerCAmelCase_:       Optional[int]         =  hidden_act
         lowerCAmelCase_:       List[Any]         =  hidden_dropout_prob
         lowerCAmelCase_:       str         =  attention_probs_dropout_prob
         lowerCAmelCase_:       str         =  max_position_embeddings
         lowerCAmelCase_:       Dict         =  eos_token_id
         lowerCAmelCase_:       int         =  pad_token_id
         lowerCAmelCase_:       Any         =  bos_token_id
         lowerCAmelCase_:       Any         =  embed_dim
         lowerCAmelCase_:       Tuple         =  word_embed_proj_dim
         lowerCAmelCase_:       Optional[int]         =  initializer_range
         lowerCAmelCase_:       Any         =  False
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Union[str, Any]         =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,	self.vocab_size   )					,	3					,	self.vocab_size   )
         lowerCAmelCase_:       Tuple         =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,	dtype=np.intaa   ))					,	-1   )
  
         lowerCAmelCase_:       int         =  OPTConfig(
             vocab_size=self.vocab_size					,	hidden_size=self.hidden_size					,	num_hidden_layers=self.num_hidden_layers					,	num_attention_heads=self.num_attention_heads					,	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_id=self.eos_token_id					,	bos_token_id=self.bos_token_id					,	pad_token_id=self.pad_token_id					,	embed_dim=self.embed_dim					,	is_encoder_decoder=a_					,	word_embed_proj_dim=self.word_embed_proj_dim					,	initializer_range=self.initializer_range					,	use_cache=a_					,	)
         lowerCAmelCase_:       Optional[int]         =  prepare_opt_inputs_dict(a_					,	a_   )
         return config, inputs_dict
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[int]         =  self.prepare_config_and_inputs()
         return config, inputs_dict
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       List[Any]         =  20
         lowerCAmelCase_:       Optional[int]         =  model_class_name(a_   )
  
         lowerCAmelCase_:       Dict         =  inputs_dict["input_ids"]
         lowerCAmelCase_:       List[str]         =  inputs_dict["attention_mask"]
  
         lowerCAmelCase_:       Any         =  model.init_cache(input_ids.shape[0]					,	a_   )
         lowerCAmelCase_:       List[Any]         =  jnp.ones((input_ids.shape[0], max_length)					,	dtype="i4"   )
  
         lowerCAmelCase_:       List[str]         =  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)					,	)
         lowerCAmelCase_:       List[Any]         =  model(
             input_ids[:, :-1]					,	attention_mask=a_					,	past_key_values=a_					,	position_ids=a_					,	)
  
         lowerCAmelCase_:       Tuple         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
         lowerCAmelCase_:       str         =  model(
             input_ids[:, -1:]					,	attention_mask=a_					,	past_key_values=outputs_cache.past_key_values					,	position_ids=a_					,	)
  
         lowerCAmelCase_:       Dict         =  model(a_   )
  
         lowerCAmelCase_:       Optional[int]         =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
         self.parent.assertTrue(diff < 1E-3					,	msg=F'''Max diff is {diff}'''   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Union[str, Any]         =  20
         lowerCAmelCase_:       List[str]         =  model_class_name(a_   )
  
         lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  (
             inputs_dict["input_ids"],
             inputs_dict["attention_mask"],
         )
  
         lowerCAmelCase_:       Any         =  jnp.concatenate(
             [
                 attention_mask,
                 jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])   ),
             ]					,	axis=-1					,	)
  
         lowerCAmelCase_:       int         =  model.init_cache(input_ids.shape[0]					,	a_   )
         lowerCAmelCase_:       Any         =  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)					,	)
  
         lowerCAmelCase_:       str         =  model(
             input_ids[:, :-1]					,	attention_mask=a_					,	past_key_values=a_					,	position_ids=a_					,	)
         lowerCAmelCase_:       Tuple         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
         lowerCAmelCase_:       List[str]         =  model(
             input_ids[:, -1:]					,	past_key_values=outputs_cache.past_key_values					,	attention_mask=a_					,	position_ids=a_					,	)
  
         lowerCAmelCase_:       Optional[int]         =  model(a_					,	attention_mask=a_   )
  
         lowerCAmelCase_:       Any         =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
         self.parent.assertTrue(diff < 1E-3					,	msg=F'''Max diff is {diff}'''   )
  
  
  
  
  
  @require_flax
  class 				_lowercase						(    SCREAMING_SNAKE_CASE__   ,     unittest.TestCase   ,     SCREAMING_SNAKE_CASE__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							str        =						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      SCREAMING_SNAKE_CASE:							Optional[int]        =						() if is_flax_available() else ()
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Dict         =  FlaxOPTModelTester(self   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       Tuple         =  self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(a_					,	a_					,	a_   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       List[Any]         =  self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(a_					,	a_					,	a_   )
  
  
  
  
      @slow
      def     _a						(						self   ):
  
  
  
         for model_class_name in self.all_model_classes:
            lowerCAmelCase_:       List[Any]         =  model_class_name.from_pretrained("facebook/opt-125m"   )
            lowerCAmelCase_:       Union[str, Any]         =  np.ones((1, 1)   ) * model.config.eos_token_id
            lowerCAmelCase_:       List[str]         =  model(a_   )
            self.assertIsNotNone(a_   )
  
  
  
  
  
  @require_sentencepiece
  @require_flax
  class 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      @slow
      def     _a						(						self   ):
         lowerCAmelCase_:       Union[str, Any]         =  FlaxOPTModel.from_pretrained("facebook/opt-350m"   )
         lowerCAmelCase_:       Dict         =  jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   )
         lowerCAmelCase_:       Union[str, Any]         =  model(input_ids=a_   ).last_hidden_state
         lowerCAmelCase_:       Optional[Any]         =  (1, 11, 512)
         self.assertEqual(output.shape					,	a_   )
         lowerCAmelCase_:       List[str]         =  jnp.array(
             [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]   )
         self.assertTrue(jnp.allclose(output[:, :3, :3]					,	a_					,	atol=4E-2   )   )
  
  
  
  
  
  @require_flax
  @slow
  class 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self   ):
         super().setUp()
         lowerCAmelCase_:       Optional[Any]         =  "facebook/opt-350m"
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Tuple         =  FlaxOPTForCausalLM.from_pretrained(self.path_model   )
         lowerCAmelCase_:       Optional[int]         =  GPTaTokenizer.from_pretrained(self.path_model   )
  
         lowerCAmelCase_:       Tuple         =  [
             "Today is a beautiful day and I want to",
             "In the city of",
             "Paris is the capital of France and",
             "Computers and mobile phones have taken",
         ]
         # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
         lowerCAmelCase_:       int         =  tokenizer(a_					,	return_tensors="jax"					,	padding=a_					,	add_special_tokens=a_   )
         lowerCAmelCase_:       List[str]         =  model(inputs.input_ids					,	attention_mask=inputs.attention_mask   )[0].mean(axis=-1   )
         lowerCAmelCase_:       List[str]         =  jnp.array(
             [
                 [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                 [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                 [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                 [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
             ]   )
         self.assertTrue(jnp.allclose(a_					,	a_					,	atol=4E-2   )   )
  
         lowerCAmelCase_:       Optional[int]         =  jax.jit(a_   )
         lowerCAmelCase_:       str         =  model(inputs.input_ids					,	attention_mask=inputs.attention_mask   )[0].mean(axis=-1   )
         self.assertTrue(jnp.allclose(a_					,	a_					,	atol=4E-2   )   )
  
  
  
  
  
  @require_flax
  @slow
  class 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      @property
      def     _a						(						self   ):
         return [
             "Today is a beautiful day and I want",
             "In the city of",
             "Paris is the capital of France and",
             "Computers and mobile phones have taken",
         ]
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       int         =  "facebook/opt-125m"
  
         lowerCAmelCase_:       Any         =  [
             "Today is a beautiful day and I want to",
             "In the city of New York, the city",
             "Paris is the capital of France and the capital",
             "Computers and mobile phones have taken over the",
         ]
  
         lowerCAmelCase_:       List[str]         =  []
  
         lowerCAmelCase_:       Any         =  FlaxOPTForCausalLM.from_pretrained(a_   )
         lowerCAmelCase_:       List[str]         =  GPTaTokenizer.from_pretrained(a_   )
  
         for prompt in self.prompts:
            lowerCAmelCase_:       Union[str, Any]         =  tokenizer(a_					,	return_tensors="jax"   ).input_ids
  
            lowerCAmelCase_:       Any         =  model.generate(a_					,	max_length=10   )
            lowerCAmelCase_:       List[str]         =  generated_ids[0]
  
            lowerCAmelCase_:       Dict         =  tokenizer.batch_decode(a_					,	skip_special_tokens=a_   )
            predicted_outputs += generated_string
  
         self.assertListEqual(a_					,	a_   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Union[str, Any]         =  "facebook/opt-350m"
  
         lowerCAmelCase_:       Any         =  [
             "Today is a beautiful day and I want to",
             "In the city of San Francisco, the city",
             "Paris is the capital of France and the capital",
             "Computers and mobile phones have taken over the",
         ]
  
         lowerCAmelCase_:       str         =  []
         lowerCAmelCase_:       str         =  FlaxOPTForCausalLM.from_pretrained(a_   )
         lowerCAmelCase_:       List[str]         =  GPTaTokenizer.from_pretrained(a_   )
  
         for prompt in self.prompts:
            lowerCAmelCase_:       List[Any]         =  tokenizer(a_					,	return_tensors="jax"   ).input_ids
  
            lowerCAmelCase_:       Optional[int]         =  model.generate(a_					,	max_length=10   )
            lowerCAmelCase_:       Union[str, Any]         =  generated_ids[0]
  
            lowerCAmelCase_:       Any         =  tokenizer.batch_decode(a_					,	skip_special_tokens=a_   )
            predicted_outputs += generated_string
  
         self.assertListEqual(a_					,	a_   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Tuple         =  "facebook/opt-125m"
         lowerCAmelCase_:       int         =  [
             "Today is a beautiful day and I want to thank",
             "In the city of Rome Canaver Canaver Canaver Canaver",
         ]
         lowerCAmelCase_:       str         =  FlaxOPTForCausalLM.from_pretrained(a_   )
         lowerCAmelCase_:       List[str]         =  GPTaTokenizer.from_pretrained(a_   )
         lowerCAmelCase_:       str         =  tokenizer(
             [
                 "Today is a beautiful day and I want to",
                 "In the city of",
             ]					,	return_tensors="jax"					,	padding=a_					,	)
  
         lowerCAmelCase_:       int         =  jax.jit(model.generate   )
  
         lowerCAmelCase_:       str         =  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]   ).sequences
  
         lowerCAmelCase_:       Tuple         =  tokenizer.batch_decode(a_					,	skip_special_tokens=a_   )
  
         self.assertIsNotNone(a_					,	a_   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       str         =  "facebook/opt-350m"
  
         lowerCAmelCase_:       Any         =  GPTaTokenizer.from_pretrained(a_   )
         lowerCAmelCase_:       str         =  FlaxOPTForCausalLM.from_pretrained(a_   )
  
         lowerCAmelCase_:       str         =  "left"
  
         # use different length sentences to test batching
         lowerCAmelCase_:       Union[str, Any]         =  [
             "Hello, my dog is a little",
             "Today, I",
         ]
  
         lowerCAmelCase_:       int         =  tokenizer(a_					,	return_tensors="jax"					,	padding=a_   )
         lowerCAmelCase_:       List[str]         =  inputs["input_ids"]
  
         lowerCAmelCase_:       Dict         =  model.generate(input_ids=a_					,	attention_mask=inputs["attention_mask"]					,	trace=a_   )
  
         lowerCAmelCase_:       Union[str, Any]         =  tokenizer(sentences[0]					,	return_tensors="jax"   ).input_ids
         lowerCAmelCase_:       Dict         =  model.generate(input_ids=a_   )
  
         lowerCAmelCase_:       Any         =  inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
         lowerCAmelCase_:       Tuple         =  tokenizer(sentences[1]					,	return_tensors="jax"   ).input_ids
         lowerCAmelCase_:       Dict         =  model.generate(input_ids=a_					,	max_length=model.config.max_length - num_paddings   )
  
         lowerCAmelCase_:       str         =  tokenizer.batch_decode(outputs[0]					,	skip_special_tokens=a_   )
         lowerCAmelCase_:       Tuple         =  tokenizer.decode(output_non_padded[0][0]					,	skip_special_tokens=a_   )
         lowerCAmelCase_:       Optional[Any]         =  tokenizer.decode(output_padded[0][0]					,	skip_special_tokens=a_   )
  
         lowerCAmelCase_:       Optional[int]         =  [
             "Hello, my dog is a little bit of a dork.\nI'm a little bit",
             "Today, I was in the middle of a conversation with a friend about the",
         ]
         self.assertListEqual(a_					,	a_   )
         self.assertListEqual(a_					,	[non_padded_sentence, padded_sentence]   )
style_context: import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  def 		snake_case__  (		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 		snake_case__  (		lowercase       ,							lowercase=0				):
     lowerCAmelCase_:       List[Any]         =  []
     for old_item in old_list:
        lowerCAmelCase_:       Any         =  old_item
        lowerCAmelCase_:       Dict         =  new_item.replace("block."       ,							"resnets."				)
        lowerCAmelCase_:       Tuple         =  new_item.replace("conv_shorcut"       ,							"conv1"				)
        lowerCAmelCase_:       Optional[int]         =  new_item.replace("in_shortcut"       ,							"conv_shortcut"				)
        lowerCAmelCase_:       Any         =  new_item.replace("temb_proj"       ,							"time_emb_proj"				)
  
        lowerCAmelCase_:       Tuple         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)
  
        mapping.append({"old": old_item, "new": new_item}				)
  
     return mapping
  def 		snake_case__  (		lowercase       ,							lowercase=0       ,							lowercase=False				):
     lowerCAmelCase_:       Any         =  []
     for old_item in old_list:
        lowerCAmelCase_:       List[Any]         =  old_item
  
        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
           lowerCAmelCase_:       str         =  new_item.replace("attn"       ,							"attentions"				)
        lowerCAmelCase_:       str         =  new_item.replace(".k."       ,							".key."				)
        lowerCAmelCase_:       Tuple         =  new_item.replace(".v."       ,							".value."				)
        lowerCAmelCase_:       Any         =  new_item.replace(".q."       ,							".query."				)
  
        lowerCAmelCase_:       List[Any]         =  new_item.replace("proj_out"       ,							"proj_attn"				)
        lowerCAmelCase_:       List[str]         =  new_item.replace("norm"       ,							"group_norm"				)
  
        lowerCAmelCase_:       Dict         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)
        mapping.append({"old": old_item, "new": new_item}				)
  
     return mapping
  def 		snake_case__  (		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():
           lowerCAmelCase_:       List[Any]         =  old_checkpoint[path]
           lowerCAmelCase_:       Optional[Any]         =  old_tensor.shape[0] // 3
  
           lowerCAmelCase_:       Any         =  (-1, channels) if len(old_tensor.shape				) == 3 else (-1)
  
           lowerCAmelCase_:       Optional[int]         =  old_tensor.shape[0] // config.get("num_head_channels"       ,							1				) // 3
  
           lowerCAmelCase_:       Optional[int]         =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
           lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Tuple         =  old_tensor.split(channels // num_heads       ,							dim=1				)
  
           lowerCAmelCase_:       List[str]         =  query.reshape(lowercase				).squeeze()
           lowerCAmelCase_:       str         =  key.reshape(lowercase				).squeeze()
           lowerCAmelCase_:       Tuple         =  value.reshape(lowercase				).squeeze()
  
     for path in paths:
        lowerCAmelCase_:       str         =  path["new"]
  
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
           continue
  
        lowerCAmelCase_:       str         =  new_path.replace("down."       ,							"down_blocks."				)
        lowerCAmelCase_:       Tuple         =  new_path.replace("up."       ,							"up_blocks."				)
  
        if additional_replacements is not None:
           for replacement in additional_replacements:
              lowerCAmelCase_:       Union[str, Any]         =  new_path.replace(replacement["old"]       ,							replacement["new"]				)
  
        if "attentions" in new_path:
           lowerCAmelCase_:       List[Any]         =  old_checkpoint[path["old"]].squeeze()
        else:
           lowerCAmelCase_:       Union[str, Any]         =  old_checkpoint[path["old"]]
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Any         =  {}
  
     lowerCAmelCase_:       List[str]         =  checkpoint["temb.dense.0.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["temb.dense.0.bias"]
     lowerCAmelCase_:       Any         =  checkpoint["temb.dense.1.weight"]
     lowerCAmelCase_:       Optional[Any]         =  checkpoint["temb.dense.1.bias"]
  
     lowerCAmelCase_:       Optional[Any]         =  checkpoint["norm_out.weight"]
     lowerCAmelCase_:       List[str]         =  checkpoint["norm_out.bias"]
  
     lowerCAmelCase_:       int         =  checkpoint["conv_in.weight"]
     lowerCAmelCase_:       int         =  checkpoint["conv_in.bias"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["conv_out.weight"]
     lowerCAmelCase_:       Dict         =  checkpoint["conv_out.bias"]
  
     lowerCAmelCase_:       Dict         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "down" in layer}				)
     lowerCAmelCase_:       Tuple         =  {
         layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
     }
  
     lowerCAmelCase_:       str         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "up" in layer}				)
     lowerCAmelCase_:       Optional[int]         =  {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				):
        lowerCAmelCase_:       List[str]         =  (i - 1) // (config["layers_per_block"] + 1)
  
        if any("downsample" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Any         =  checkpoint[
               F'''down.{i}.downsample.op.weight'''
           ]
           lowerCAmelCase_:       List[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]				):
           lowerCAmelCase_:       Optional[Any]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       str         =  {
               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"]				):
                 lowerCAmelCase_:       str         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)
  
        if any("attn" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       List[Any]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       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"]				):
                 lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)
  
     lowerCAmelCase_:       List[str]         =  [key for key in checkpoint if "mid.block_1" in key]
     lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
     lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]
  
     # Mid new 2
     lowerCAmelCase_:       Optional[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"}]       ,							)
  
     lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)
  
     lowerCAmelCase_:       int         =  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				):
        lowerCAmelCase_:       Any         =  num_up_blocks - 1 - i
  
        if any("upsample" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       str         =  checkpoint[
               F'''up.{i}.upsample.conv.weight'''
           ]
           lowerCAmelCase_:       Union[str, Any]         =  checkpoint[F'''up.{i}.upsample.conv.bias''']
  
        if any("block" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       List[str]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       List[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				):
                 lowerCAmelCase_:       List[Any]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       List[Any]         =  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]				):
           lowerCAmelCase_:       Optional[int]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       List[str]         =  {
               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				):
                 lowerCAmelCase_:       Optional[int]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       Dict         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
     lowerCAmelCase_:       Tuple         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
     return new_checkpoint
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Optional[int]         =  {}
  
     lowerCAmelCase_:       Any         =  checkpoint["encoder.norm_out.weight"]
     lowerCAmelCase_:       Tuple         =  checkpoint["encoder.norm_out.bias"]
  
     lowerCAmelCase_:       Optional[int]         =  checkpoint["encoder.conv_in.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_in.bias"]
     lowerCAmelCase_:       List[str]         =  checkpoint["encoder.conv_out.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_out.bias"]
  
     lowerCAmelCase_:       Dict         =  checkpoint["decoder.norm_out.weight"]
     lowerCAmelCase_:       List[str]         =  checkpoint["decoder.norm_out.bias"]
  
     lowerCAmelCase_:       Dict         =  checkpoint["decoder.conv_in.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_in.bias"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_out.weight"]
     lowerCAmelCase_:       List[str]         =  checkpoint["decoder.conv_out.bias"]
  
     lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "down" in layer}				)
     lowerCAmelCase_:       Any         =  {
         layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
     }
  
     lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "up" in layer}				)
     lowerCAmelCase_:       Union[str, 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				):
        lowerCAmelCase_:       Any         =  (i - 1) // (config["layers_per_block"] + 1)
  
        if any("downsample" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Optional[Any]         =  checkpoint[
               F'''encoder.down.{i}.downsample.conv.weight'''
           ]
           lowerCAmelCase_:       Any         =  checkpoint[
               F'''encoder.down.{i}.downsample.conv.bias'''
           ]
  
        if any("block" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Union[str, Any]         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       Optional[Any]         =  {
               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"]				):
                 lowerCAmelCase_:       Dict         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)
  
        if any("attn" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Dict         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       List[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"]				):
                 lowerCAmelCase_:       List[str]         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)
  
     lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.block_1" in key]
     lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
     lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]
  
     # Mid new 2
     lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,							)
  
     lowerCAmelCase_:       List[str]         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)
  
     lowerCAmelCase_:       List[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				):
        lowerCAmelCase_:       str         =  num_up_blocks - 1 - i
  
        if any("upsample" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       Optional[Any]         =  checkpoint[
               F'''decoder.up.{i}.upsample.conv.weight'''
           ]
           lowerCAmelCase_:       str         =  checkpoint[
               F'''decoder.up.{i}.upsample.conv.bias'''
           ]
  
        if any("block" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       str         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       Optional[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				):
                 lowerCAmelCase_:       Dict         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       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]				):
           lowerCAmelCase_:       Dict         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       Optional[int]         =  {
               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				):
                 lowerCAmelCase_:       Any         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
     lowerCAmelCase_:       Optional[Any]         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
     lowerCAmelCase_:       Optional[int]         =  checkpoint["quant_conv.weight"]
     lowerCAmelCase_:       Optional[Any]         =  checkpoint["quant_conv.bias"]
     if "quantize.embedding.weight" in checkpoint:
        lowerCAmelCase_:       List[Any]         =  checkpoint["quantize.embedding.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["post_quant_conv.weight"]
     lowerCAmelCase_:       Any         =  checkpoint["post_quant_conv.bias"]
  
     return new_checkpoint
  
  
  if __name__ == "__main__":
   a     :			Any					    =						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     :			Union[str, Any]					    =						parser.parse_args()
   a     :			List[Any]					    =						torch.load(args.checkpoint_path)
  
   with open(args.config_file) as f:
    a     :			Any					    =						json.loads(f.read())
  
   # unet case
   a     :			List[str]					    =						{key.split(""".""")[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    a     :			Optional[Any]					    =						convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
    a     :			Optional[Any]					    =						convert_ddpm_checkpoint(checkpoint, config)
  
   if "ddpm" in config:
    del config["ddpm"]
  
   if config["_class_name"] == "VQModel":
    a     :			Any					    =						VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
    a     :			Tuple					    =						AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   else:
    a     :			Optional[Any]					    =						UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)
  
    a     :			Dict					    =						DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  
    a     :			int					    =						DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)