
"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

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


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertConfig,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertWithLMHeadModel,
      )

class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	parent
      __UpperCamelCase      =	13
      __UpperCamelCase      =	7
      __UpperCamelCase      =	True
      __UpperCamelCase      =	True
      __UpperCamelCase      =	True
      __UpperCamelCase      =	True
      __UpperCamelCase      =	True
      __UpperCamelCase      =	False
      __UpperCamelCase      =	False
      __UpperCamelCase      =	False
      __UpperCamelCase      =	2
      __UpperCamelCase      =	99
      __UpperCamelCase      =	0
      __UpperCamelCase      =	32
      __UpperCamelCase      =	2
      __UpperCamelCase      =	4
      __UpperCamelCase      =	0.1
      __UpperCamelCase      =	0.1
      __UpperCamelCase      =	512
      __UpperCamelCase      =	16
      __UpperCamelCase      =	2
      __UpperCamelCase      =	0.0_2
      __UpperCamelCase      =	3
      __UpperCamelCase      =	4
      __UpperCamelCase      =	'last'
      __UpperCamelCase      =	True
      __UpperCamelCase      =	None
      __UpperCamelCase      =	0





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size				)
      __UpperCamelCase      =	random_attention_mask([self.batch_size, self.seq_length]     ,					dtype=tf.floataa				)

      __UpperCamelCase      =	None
      if self.use_input_lengths:
       __UpperCamelCase      =	(
           ids_tensor([self.batch_size]     ,					vocab_size=2				) + self.seq_length - 2
       )  # small variation of seq_length

      __UpperCamelCase      =	None
      if self.use_token_type_ids:
       __UpperCamelCase      =	ids_tensor([self.batch_size, self.seq_length]     ,					self.n_langs				)

      __UpperCamelCase      =	None
      __UpperCamelCase      =	None
      __UpperCamelCase      =	None
      if self.use_labels:
       __UpperCamelCase      =	ids_tensor([self.batch_size]     ,					self.type_sequence_label_size				)
       __UpperCamelCase      =	ids_tensor([self.batch_size, self.seq_length]     ,					self.num_labels				)
       __UpperCamelCase      =	ids_tensor([self.batch_size]     ,					2     ,					dtype=tf.floataa				)
       __UpperCamelCase      =	ids_tensor([self.batch_size]     ,					self.num_choices				)

      __UpperCamelCase      =	FlaubertConfig(
          vocab_size=self.vocab_size     ,					n_special=self.n_special     ,					emb_dim=self.hidden_size     ,					n_layers=self.num_hidden_layers     ,					n_heads=self.num_attention_heads     ,					dropout=self.hidden_dropout_prob     ,					attention_dropout=self.attention_probs_dropout_prob     ,					gelu_activation=self.gelu_activation     ,					sinusoidal_embeddings=self.sinusoidal_embeddings     ,					asm=self.asm     ,					causal=self.causal     ,					n_langs=self.n_langs     ,					max_position_embeddings=self.max_position_embeddings     ,					initializer_range=self.initializer_range     ,					summary_type=self.summary_type     ,					use_proj=self.use_proj     ,					bos_token_id=self.bos_token_id     ,					)

      return (
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      )





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	TFFlaubertModel(config=__UpperCAmelCase				)
      __UpperCamelCase      =	{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
      __UpperCamelCase      =	model(__UpperCAmelCase				)

      __UpperCamelCase      =	[input_ids, input_mask]
      __UpperCamelCase      =	model(__UpperCAmelCase				)
      self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	TFFlaubertWithLMHeadModel(__UpperCAmelCase				)

      __UpperCamelCase      =	{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
      __UpperCamelCase      =	model(__UpperCAmelCase				)

      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.vocab_size)				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	TFFlaubertForQuestionAnsweringSimple(__UpperCAmelCase				)

      __UpperCamelCase      =	{'input_ids': input_ids, 'lengths': input_lengths}

      __UpperCamelCase      =	model(__UpperCAmelCase				)

      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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	TFFlaubertForSequenceClassification(__UpperCAmelCase				)

      __UpperCamelCase      =	{'input_ids': input_ids, 'lengths': input_lengths}

      __UpperCamelCase      =	model(__UpperCAmelCase				)

      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	self.num_labels
      __UpperCamelCase      =	TFFlaubertForTokenClassification(config=__UpperCAmelCase				)
      __UpperCamelCase      =	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __UpperCamelCase      =	model(__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.num_labels)				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	self.num_choices
      __UpperCamelCase      =	TFFlaubertForMultipleChoice(config=__UpperCAmelCase				)
      __UpperCamelCase      =	tf.tile(tf.expand_dims(__UpperCAmelCase     ,					1				)     ,					(1, self.num_choices, 1)				)
      __UpperCamelCase      =	tf.tile(tf.expand_dims(__UpperCAmelCase     ,					1				)     ,					(1, self.num_choices, 1)				)
      __UpperCamelCase      =	tf.tile(tf.expand_dims(__UpperCAmelCase     ,					1				)     ,					(1, self.num_choices, 1)				)
      __UpperCamelCase      =	{
          'input_ids': multiple_choice_inputs_ids,
          'attention_mask': multiple_choice_input_mask,
          'token_type_ids': multiple_choice_token_type_ids,
      }
      __UpperCamelCase      =	model(__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_choices)				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.prepare_config_and_inputs()
      (
          (
          __UpperCamelCase
      )  ,     (
          __UpperCamelCase
      )  ,     (
          __UpperCamelCase
      )  ,     (
          __UpperCamelCase
      )  ,     (
          __UpperCamelCase
      )  ,     (
          __UpperCamelCase
      )  ,     (
          __UpperCamelCase
      )  ,     (
          __UpperCamelCase
      )  ,     (
          __UpperCamelCase
      )  ,     
      )      =	config_and_inputs
      __UpperCamelCase      =	{
          'input_ids': input_ids,
          'token_type_ids': token_type_ids,
          'langs': token_type_ids,
          'lengths': input_lengths,
      }
      return config, inputs_dict


@require_tf
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						(
         (
             TFFlaubertModel,
             TFFlaubertWithLMHeadModel,
             TFFlaubertForSequenceClassification,
             TFFlaubertForQuestionAnsweringSimple,
             TFFlaubertForTokenClassification,
             TFFlaubertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     lowercase						       =						(
         (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     lowercase						       =						(
         {
             "feature-extraction": TFFlaubertModel,
             "fill-mask": TFFlaubertWithLMHeadModel,
             "question-answering": TFFlaubertForQuestionAnsweringSimple,
             "text-classification": TFFlaubertForSequenceClassification,
             "token-classification": TFFlaubertForTokenClassification,
             "zero-shot": TFFlaubertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowercase						       =						False
     lowercase						       =						False





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      if (
          pipeline_test_casse_name == "QAPipelineTests"
          and tokenizer_name is not None
          and not tokenizer_name.endswith('Fast'				)
      ):
       # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
       # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
       # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
       return True

      return False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFFlaubertModelTester(self				)
      __UpperCamelCase      =	ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					emb_dim=37				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_model(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_lm_head(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_qa(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_sequence_classif(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_token_classification(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_multiple_choice(*__UpperCAmelCase				)







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase      =	TFFlaubertModel.from_pretrained(__UpperCAmelCase				)
       self.assertIsNotNone(__UpperCAmelCase				)


@require_tf
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase		(				unittest.TestCase      ):





     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'				)

      __UpperCamelCase      =	tf.convert_to_tensor(
          [[0, 158, 735, 2592, 1424, 6727, 82, 1]]     ,					dtype=tf.intaa     ,					)  # "J'aime flaubert !"

      __UpperCamelCase      =	model(__UpperCAmelCase				)[0]
      __UpperCamelCase      =	tf.TensorShape((1, 8, 512)				)
      self.assertEqual(output.shape     ,					__UpperCAmelCase				)
      # compare the actual values for a slice.
      __UpperCamelCase      =	tf.convert_to_tensor(
          [
              [
                  [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
                  [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
                  [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
              ]
          ]     ,					dtype=tf.floataa     ,					)

      self.assertTrue(np.allclose(output[:, :3, :3].numpy()     ,					expected_slice.numpy()     ,					atol=1E-4				)				)





"""simple docstring"""

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 							A						(    snake_case				:List[Any]    , snake_case				:Dict=1		)				->     Optional[int]:
 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						(    snake_case				:Dict    , snake_case				:int=0		)				->     Optional[int]:
 __UpperCamelCase      =	[]
 for old_item in old_list:
  __UpperCamelCase      =	old_item.replace('in_layers.0'    , 'norm1'		)
  __UpperCamelCase      =	new_item.replace('in_layers.2'    , 'conv1'		)

  __UpperCamelCase      =	new_item.replace('out_layers.0'    , 'norm2'		)
  __UpperCamelCase      =	new_item.replace('out_layers.3'    , 'conv2'		)

  __UpperCamelCase      =	new_item.replace('emb_layers.1'    , 'time_emb_proj'		)
  __UpperCamelCase      =	new_item.replace('skip_connection'    , 'conv_shortcut'		)

  __UpperCamelCase      =	shave_segments(snake_case    , n_shave_prefix_segments=snake_case		)

  mapping.append({'old': old_item, 'new': new_item}		)

 return mapping






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Tuple=0		)				->     Tuple:
 __UpperCamelCase      =	[]
 for old_item in old_list:
  __UpperCamelCase      =	old_item

  __UpperCamelCase      =	new_item.replace('norm.weight'    , 'group_norm.weight'		)
  __UpperCamelCase      =	new_item.replace('norm.bias'    , 'group_norm.bias'		)

  __UpperCamelCase      =	new_item.replace('proj_out.weight'    , 'proj_attn.weight'		)
  __UpperCamelCase      =	new_item.replace('proj_out.bias'    , 'proj_attn.bias'		)

  __UpperCamelCase      =	shave_segments(snake_case    , n_shave_prefix_segments=snake_case		)

  mapping.append({'old': old_item, 'new': new_item}		)

 return mapping






def 							A						(    snake_case				:int    , snake_case				:List[str]    , snake_case				:List[str]    , snake_case				:Any=None    , snake_case				:Optional[int]=None    , snake_case				:Union[str, Any]=None		)				->     Optional[int]:
 assert isinstance(snake_case    , snake_case		), "Paths should be a list of dicts containing 'old' and 'new' keys."

 # Splits the attention layers into three variables.
 if attention_paths_to_split is not None:
  for path, path_map in attention_paths_to_split.items():
   __UpperCamelCase      =	old_checkpoint[path]
   __UpperCamelCase      =	old_tensor.shape[0] // 3

   __UpperCamelCase      =	(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

   __UpperCamelCase      =	old_tensor.shape[0] // config['num_head_channels'] // 3

   __UpperCamelCase      =	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
   __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	old_tensor.split(channels // num_heads    , dim=1		)

   __UpperCamelCase      =	query.reshape(snake_case		)
   __UpperCamelCase      =	key.reshape(snake_case		)
   __UpperCamelCase      =	value.reshape(snake_case		)

 for path in paths:
  __UpperCamelCase      =	path['new']

  # These have already been assigned
  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  # Global renaming happens here
  __UpperCamelCase      =	new_path.replace('middle_block.0'    , 'mid_block.resnets.0'		)
  __UpperCamelCase      =	new_path.replace('middle_block.1'    , 'mid_block.attentions.0'		)
  __UpperCamelCase      =	new_path.replace('middle_block.2'    , 'mid_block.resnets.1'		)

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

        # proj_attn.weight has to be converted from conv 1D to linear
  if "proj_attn.weight" in new_path:
   __UpperCamelCase      =	old_checkpoint[path['old']][:, :, 0]
  else:
   __UpperCamelCase      =	old_checkpoint[path['old']]






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Dict		)				->     Optional[Any]:
 __UpperCamelCase      =	{}

 __UpperCamelCase      =	checkpoint['time_embed.0.weight']
 __UpperCamelCase      =	checkpoint['time_embed.0.bias']
 __UpperCamelCase      =	checkpoint['time_embed.2.weight']
 __UpperCamelCase      =	checkpoint['time_embed.2.bias']

 __UpperCamelCase      =	checkpoint['input_blocks.0.0.weight']
 __UpperCamelCase      =	checkpoint['input_blocks.0.0.bias']

 __UpperCamelCase      =	checkpoint['out.0.weight']
 __UpperCamelCase      =	checkpoint['out.0.bias']
 __UpperCamelCase      =	checkpoint['out.2.weight']
 __UpperCamelCase      =	checkpoint['out.2.bias']

 # Retrieves the keys for the input blocks only
 __UpperCamelCase      =	len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'input_blocks' in layer}		)
 __UpperCamelCase      =	{
     layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
     for layer_id in range(snake_case		)
 }

 # Retrieves the keys for the middle blocks only
 __UpperCamelCase      =	len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'middle_block' in layer}		)
 __UpperCamelCase      =	{
     layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
     for layer_id in range(snake_case		)
 }

 # Retrieves the keys for the output blocks only
 __UpperCamelCase      =	len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'output_blocks' in layer}		)
 __UpperCamelCase      =	{
     layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
     for layer_id in range(snake_case		)
 }

 for i in range(1    , snake_case		):
  __UpperCamelCase      =	(i - 1) // (config['num_res_blocks'] + 1)
  __UpperCamelCase      =	(i - 1) % (config['num_res_blocks'] + 1)

  __UpperCamelCase      =	[key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
  __UpperCamelCase      =	[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

  if f'input_blocks.{i}.0.op.weight' in checkpoint:
   __UpperCamelCase      =	checkpoint[
       f'input_blocks.{i}.0.op.weight'
   ]
   __UpperCamelCase      =	checkpoint[
       f'input_blocks.{i}.0.op.bias'
   ]
   continue

  __UpperCamelCase      =	renew_resnet_paths(snake_case		)
  __UpperCamelCase      =	{'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
  __UpperCamelCase      =	{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
  assign_to_checkpoint(
      snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path, resnet_op]    , config=snake_case		)

  if len(snake_case		):
   __UpperCamelCase      =	renew_attention_paths(snake_case		)
   __UpperCamelCase      =	{
       'old': f'input_blocks.{i}.1',
       'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
   }
   __UpperCamelCase      =	{
       f'input_blocks.{i}.1.qkv.bias': {
           'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
           'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
           'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
       },
       f'input_blocks.{i}.1.qkv.weight': {
           'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
           'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
           'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
       },
   }
   assign_to_checkpoint(
       snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path]    , attention_paths_to_split=snake_case    , config=snake_case    , )

 __UpperCamelCase      =	middle_blocks[0]
 __UpperCamelCase      =	middle_blocks[1]
 __UpperCamelCase      =	middle_blocks[2]

 __UpperCamelCase      =	renew_resnet_paths(snake_case		)
 assign_to_checkpoint(snake_case    , snake_case    , snake_case    , config=snake_case		)

 __UpperCamelCase      =	renew_resnet_paths(snake_case		)
 assign_to_checkpoint(snake_case    , snake_case    , snake_case    , config=snake_case		)

 __UpperCamelCase      =	renew_attention_paths(snake_case		)
 __UpperCamelCase      =	{
     'middle_block.1.qkv.bias': {
         'key': 'mid_block.attentions.0.key.bias',
         'query': 'mid_block.attentions.0.query.bias',
         'value': 'mid_block.attentions.0.value.bias',
     },
     'middle_block.1.qkv.weight': {
         'key': 'mid_block.attentions.0.key.weight',
         'query': 'mid_block.attentions.0.query.weight',
         'value': 'mid_block.attentions.0.value.weight',
     },
 }
 assign_to_checkpoint(
     snake_case    , snake_case    , snake_case    , attention_paths_to_split=snake_case    , config=snake_case		)

 for i in range(snake_case		):
  __UpperCamelCase      =	i // (config['num_res_blocks'] + 1)
  __UpperCamelCase      =	i % (config['num_res_blocks'] + 1)
  __UpperCamelCase      =	[shave_segments(snake_case    , 2		) for name in output_blocks[i]]
  __UpperCamelCase      =	{}

  for layer in output_block_layers:
   __UpperCamelCase  ,     __UpperCamelCase      =	layer.split('.'		)[0], shave_segments(snake_case    , 1		)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(snake_case		)
   else:
    __UpperCamelCase      =	[layer_name]

  if len(snake_case		) > 1:
   __UpperCamelCase      =	[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
   __UpperCamelCase      =	[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

   __UpperCamelCase      =	renew_resnet_paths(snake_case		)
   __UpperCamelCase      =	renew_resnet_paths(snake_case		)

   __UpperCamelCase      =	{'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
   assign_to_checkpoint(snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path]    , config=snake_case		)

   if ["conv.weight", "conv.bias"] in output_block_list.values():
    __UpperCamelCase      =	list(output_block_list.values()		).index(['conv.weight', 'conv.bias']		)
    __UpperCamelCase      =	checkpoint[
        f'output_blocks.{i}.{index}.conv.weight'
    ]
    __UpperCamelCase      =	checkpoint[
        f'output_blocks.{i}.{index}.conv.bias'
    ]

    # Clear attentions as they have been attributed above.
    if len(snake_case		) == 2:
     __UpperCamelCase      =	[]

   if len(snake_case		):
    __UpperCamelCase      =	renew_attention_paths(snake_case		)
    __UpperCamelCase      =	{
        'old': f'output_blocks.{i}.1',
        'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
    }
    __UpperCamelCase      =	{
        f'output_blocks.{i}.1.qkv.bias': {
            'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
            'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
            'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
        },
        f'output_blocks.{i}.1.qkv.weight': {
            'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
            'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
            'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
        },
    }
    assign_to_checkpoint(
        snake_case    , snake_case    , snake_case    , additional_replacements=[meta_path]    , attention_paths_to_split=to_split if any('qkv' in key for key in attentions		) else None    , config=snake_case    , )
  else:
   __UpperCamelCase      =	renew_resnet_paths(snake_case    , n_shave_prefix_segments=1		)
   for path in resnet_0_paths:
    __UpperCamelCase      =	'.'.join(['output_blocks', str(snake_case		), path['old']]		)
    __UpperCamelCase      =	'.'.join(['up_blocks', str(snake_case		), 'resnets', str(snake_case		), path['new']]		)

    __UpperCamelCase      =	checkpoint[old_path]

 return new_checkpoint


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

      UpperCamelCase     :    Dict       =			parser.parse_args()

      UpperCamelCase     :    Optional[Any]       =			torch.load(args.checkpoint_path)

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

      UpperCamelCase     :    Dict       =			convert_ldm_checkpoint(checkpoint, config)

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

      UpperCamelCase     :    Optional[Any]       =			UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            UpperCamelCase     :    List[Any]       =			DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
            UpperCamelCase     :    Optional[int]       =			VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

            UpperCamelCase     :    Dict       =			LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)



