






from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, 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 tensorflow as tf

					from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
					from transformers.models.albert.modeling_tf_albert import (
					    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFAlbertForMaskedLM,
					    TFAlbertForMultipleChoice,
					    TFAlbertForPreTraining,
					    TFAlbertForQuestionAnswering,
					    TFAlbertForSequenceClassification,
					    TFAlbertForTokenClassification,
					    TFAlbertModel,
					)


class      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase=13  ,    _UpperCamelCase=7  ,    _UpperCamelCase=True  ,    _UpperCamelCase=True  ,    _UpperCamelCase=True  ,    _UpperCamelCase=True  ,    _UpperCamelCase=99  ,    _UpperCamelCase=16  ,    _UpperCamelCase=32  ,    _UpperCamelCase=2  ,    _UpperCamelCase=4  ,    _UpperCamelCase=37  ,    _UpperCamelCase="gelu"  ,    _UpperCamelCase=0.1  ,    _UpperCamelCase=0.1  ,    _UpperCamelCase=5_12  ,    _UpperCamelCase=16  ,    _UpperCamelCase=2  ,    _UpperCamelCase=0.02  ,    _UpperCamelCase=3  ,    _UpperCamelCase=4  ,    _UpperCamelCase=None  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    parent
												lowerCAmelCase__         =    13
												lowerCAmelCase__         =    7
												lowerCAmelCase__         =    True
												lowerCAmelCase__         =    True
												lowerCAmelCase__         =    True
												lowerCAmelCase__         =    True
												lowerCAmelCase__         =    99
												lowerCAmelCase__         =    16
												lowerCAmelCase__         =    32
												lowerCAmelCase__         =    2
												lowerCAmelCase__         =    4
												lowerCAmelCase__         =    37
												lowerCAmelCase__         =    'gelu'
												lowerCAmelCase__         =    0.1
												lowerCAmelCase__         =    0.1
												lowerCAmelCase__         =    5_12
												lowerCAmelCase__         =    16
												lowerCAmelCase__         =    2
												lowerCAmelCase__         =    0.02
												lowerCAmelCase__         =    3
												lowerCAmelCase__         =    4
												lowerCAmelCase__         =    None






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size )

												lowerCAmelCase__         =    None
												if self.use_input_mask:
																		lowerCAmelCase__         =    random_attention_mask([self.batch_size, self.seq_length] )

												lowerCAmelCase__         =    None
												if self.use_token_type_ids:
																		lowerCAmelCase__         =    ids_tensor([self.batch_size, self.seq_length]  ,    self.type_vocab_size )

												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None
												if self.use_labels:
																		lowerCAmelCase__         =    ids_tensor([self.batch_size]  ,    self.type_sequence_label_size )
																		lowerCAmelCase__         =    ids_tensor([self.batch_size, self.seq_length]  ,    self.num_labels )
																		lowerCAmelCase__         =    ids_tensor([self.batch_size]  ,    self.num_choices )

												lowerCAmelCase__         =    AlbertConfig(
												    vocab_size=self.vocab_size  ,    hidden_size=self.hidden_size  ,    embedding_size=self.embedding_size  ,    num_hidden_layers=self.num_hidden_layers  ,    num_attention_heads=self.num_attention_heads  ,    intermediate_size=self.intermediate_size  ,    hidden_act=self.hidden_act  ,    hidden_dropout_prob=self.hidden_dropout_prob  ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,    max_position_embeddings=self.max_position_embeddings  ,    type_vocab_size=self.type_vocab_size  ,    initializer_range=self.initializer_range  ,    )

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    TFAlbertModel(config=_UpperCamelCase )
												# inputs = {'input_ids': input_ids,
												#           'attention_mask': input_mask,
												#           'token_type_ids': token_type_ids}
												# sequence_output, pooled_output = model(**inputs)
												lowerCAmelCase__         =    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
												lowerCAmelCase__         =    model(_UpperCamelCase )

												lowerCAmelCase__         =    [input_ids, input_mask]
												lowerCAmelCase__         =    model(_UpperCamelCase )

												lowerCAmelCase__         =    model(_UpperCamelCase )

												self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size) )
												self.parent.assertEqual(result.pooler_output.shape  ,    (self.batch_size, self.hidden_size) )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.num_labels
												lowerCAmelCase__         =    TFAlbertForPreTraining(config=_UpperCamelCase )
												lowerCAmelCase__         =    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
												lowerCAmelCase__         =    model(_UpperCamelCase )
												self.parent.assertEqual(result.prediction_logits.shape  ,    (self.batch_size, self.seq_length, self.vocab_size) )
												self.parent.assertEqual(result.sop_logits.shape  ,    (self.batch_size, self.num_labels) )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    TFAlbertForMaskedLM(config=_UpperCamelCase )
												lowerCAmelCase__         =    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
												lowerCAmelCase__         =    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 ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.num_labels
												lowerCAmelCase__         =    TFAlbertForSequenceClassification(config=_UpperCamelCase )
												lowerCAmelCase__         =    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
												lowerCAmelCase__         =    model(_UpperCamelCase )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_labels) )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    TFAlbertForQuestionAnswering(config=_UpperCamelCase )
												lowerCAmelCase__         =    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
												lowerCAmelCase__         =    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 ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.num_choices
												lowerCAmelCase__         =    TFAlbertForMultipleChoice(config=_UpperCamelCase )
												lowerCAmelCase__         =    tf.tile(tf.expand_dims(_UpperCamelCase  ,    1 )  ,    (1, self.num_choices, 1) )
												lowerCAmelCase__         =    tf.tile(tf.expand_dims(_UpperCamelCase  ,    1 )  ,    (1, self.num_choices, 1) )
												lowerCAmelCase__         =    tf.tile(tf.expand_dims(_UpperCamelCase  ,    1 )  ,    (1, self.num_choices, 1) )
												lowerCAmelCase__         =    {
												    'input_ids': multiple_choice_inputs_ids,
												    'attention_mask': multiple_choice_input_mask,
												    'token_type_ids': multiple_choice_token_type_ids,
												}
												lowerCAmelCase__         =    model(_UpperCamelCase )
												self.parent.assertListEqual(list(result['logits'].shape )  ,    [self.batch_size, self.num_choices] )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.num_labels
												lowerCAmelCase__         =    TFAlbertForTokenClassification(config=_UpperCamelCase )
												lowerCAmelCase__         =    {
												    'input_ids': input_ids,
												    'attention_mask': input_mask,
												    'token_type_ids': token_type_ids,
												}
												lowerCAmelCase__         =    model(_UpperCamelCase )
												self.parent.assertListEqual(list(result['logits'].shape )  ,    [self.batch_size, self.seq_length, self.num_labels] )







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.prepare_config_and_inputs()
												(
												    (
												    lowerCAmelCase__
												)    ,							(
												    lowerCAmelCase__
												)    ,							(
												    lowerCAmelCase__
												)    ,							(
												    lowerCAmelCase__
												)    ,							(
												    lowerCAmelCase__
												)    ,							(
												    lowerCAmelCase__
												)    ,							(
												    lowerCAmelCase__
												)    ,							
												)         =    config_and_inputs
												lowerCAmelCase__         =    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
												return config, inputs_dict




@require_tf
class      __SCREAMING_SNAKE_CASE       ( __lowercase		,       __lowercase		,       unittest.TestCase):
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       (
						    (
						        TFAlbertModel,
						        TFAlbertForPreTraining,
						        TFAlbertForMaskedLM,
						        TFAlbertForSequenceClassification,
						        TFAlbertForQuestionAnswering,
						        TFAlbertForTokenClassification,
						        TFAlbertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       (
						    {
						        '''feature-extraction''': TFAlbertModel,
						        '''fill-mask''': TFAlbertForMaskedLM,
						        '''question-answering''': TFAlbertForQuestionAnswering,
						        '''text-classification''': TFAlbertForSequenceClassification,
						        '''token-classification''': TFAlbertForTokenClassification,
						        '''zero-shot''': TFAlbertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_SCREAMING_SNAKE_CASE						:							Tuple				=       False
						_SCREAMING_SNAKE_CASE						:							Any				=       False






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase=False ):






												"""simple docstring"""
												lowerCAmelCase__         =    super()._prepare_for_class(_UpperCamelCase  ,    _UpperCamelCase  ,    return_labels=_UpperCamelCase )

												if return_labels:
																		if model_class in get_values(_UpperCamelCase ):
																								lowerCAmelCase__         =    tf.zeros(self.model_tester.batch_size  ,    dtype=tf.intaa )

												return inputs_dict






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    TFAlbertModelTester(self )
												lowerCAmelCase__         =    ConfigTester(self  ,    config_class=_UpperCamelCase  ,    hidden_size=37 )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												self.config_tester.run_common_tests()






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_model(*_UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_pretraining(*_UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_masked_lm(*_UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_multiple_choice(*_UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_sequence_classification(*_UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_question_answering(*_UpperCamelCase )







						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowerCAmelCase__         =    TFAlbertModel.from_pretrained(_UpperCamelCase )
																		self.assertIsNotNone(_UpperCamelCase )




@require_tf
class      __SCREAMING_SNAKE_CASE       ( unittest.TestCase):






						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    TFAlbertForPreTraining.from_pretrained('albert-base-v2' )
												lowerCAmelCase__         =    tf.constant([[0, 1, 2, 3, 4, 5]] )
												lowerCAmelCase__         =    model(_UpperCamelCase )[0]

												lowerCAmelCase__         =    [1, 6, 3_00_00]
												self.assertEqual(output.shape  ,    _UpperCamelCase )

												lowerCAmelCase__         =    tf.constant(
												    [
												        [
												            [4.59_56_68, 0.74_46_27_54, -1.81_81_47],
												            [4.5_95_43_47, 0.7_45_41_84, -1.8_18_82_58],
												            [4.5_95_49_05, 0.7_44_82_35, -1.8_18_23_16],
												        ]
												    ] )
												tf.debugging.assert_near(output[:, :3, :3]  ,    _UpperCamelCase  ,    atol=1E-4 )













import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       Optional[Any]=1		)	-> Optional[int]:

						"""simple docstring"""



						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('.'		)[n_shave_prefix_segments:]		)
						else:
												return ".".join(path.split('.'		)[:n_shave_prefix_segments]		)







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int=0		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						for old_item in old_list:
												lowerCAmelCase__         =    old_item
												lowerCAmelCase__         =    new_item.replace('block.'     ,							'resnets.'		)
												lowerCAmelCase__         =    new_item.replace('conv_shorcut'     ,							'conv1'		)
												lowerCAmelCase__         =    new_item.replace('in_shortcut'     ,							'conv_shortcut'		)
												lowerCAmelCase__         =    new_item.replace('temb_proj'     ,							'time_emb_proj'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)

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

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Union[str, Any]=0     ,							UpperCamelCase_							:       Dict=False		)	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						for old_item in old_list:
												lowerCAmelCase__         =    old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		lowerCAmelCase__         =    new_item.replace('attn'     ,							'attentions'		)
												lowerCAmelCase__         =    new_item.replace('.k.'     ,							'.key.'		)
												lowerCAmelCase__         =    new_item.replace('.v.'     ,							'.value.'		)
												lowerCAmelCase__         =    new_item.replace('.q.'     ,							'.query.'		)

												lowerCAmelCase__         =    new_item.replace('proj_out'     ,							'proj_attn'		)
												lowerCAmelCase__         =    new_item.replace('norm'     ,							'group_norm'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)
												mapping.append({'old': old_item, 'new': new_item}		)

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       Any=None     ,							UpperCamelCase_							:       str=None     ,							UpperCamelCase_							:       str=None		)	-> Dict:

						"""simple docstring"""



						assert isinstance(UpperCamelCase_     ,							UpperCamelCase_		), "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__         =    old_checkpoint[path]
																		lowerCAmelCase__         =    old_tensor.shape[0] // 3

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

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

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

																		lowerCAmelCase__         =    query.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    key.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    value.reshape(UpperCamelCase_		).squeeze()

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

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase__         =    new_path.replace('down.'     ,							'down_blocks.'		)
												lowerCAmelCase__         =    new_path.replace('up.'     ,							'up_blocks.'		)

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

												if "attentions" in new_path:
																		lowerCAmelCase__         =    old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase__         =    old_checkpoint[path['old']]







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Dict		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['temb.dense.0.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.0.bias']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.bias']

						lowerCAmelCase__         =    checkpoint['norm_out.weight']
						lowerCAmelCase__         =    checkpoint['norm_out.bias']

						lowerCAmelCase__         =    checkpoint['conv_in.weight']
						lowerCAmelCase__         =    checkpoint['conv_in.bias']
						lowerCAmelCase__         =    checkpoint['conv_out.weight']
						lowerCAmelCase__         =    checkpoint['conv_out.bias']

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"down.{i}.downsample.op.weight"
																		]
																		lowerCAmelCase__         =    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__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							config=UpperCamelCase_		)

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

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

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

						lowerCAmelCase__         =    renew_attention_paths(UpperCamelCase_     ,							in_mid=UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,							)

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

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

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

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







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       int		)	-> Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['encoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['encoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.bias']

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

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

												if any('block' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							config=UpperCamelCase_		)

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

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

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

						lowerCAmelCase__         =    renew_attention_paths(UpperCamelCase_     ,							in_mid=UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,							)

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

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

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

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

						return new_checkpoint


if __name__ == "__main__":
					__snake_case     :     Tuple       =  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.""")

					__snake_case     :     str       =  parser.parse_args()
					__snake_case     :     Any       =  torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										__snake_case     :     Optional[int]       =  json.loads(f.read())

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

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

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

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

										__snake_case     :     Tuple       =  DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)





