






'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import is_tf_available
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 numpy
		import tensorflow as tf

		from transformers import (
		    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BertConfig,
		    DPRConfig,
		    TFDPRContextEncoder,
		    TFDPRQuestionEncoder,
		    TFDPRReader,
		)





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      str=13	,    __SCREAMING_SNAKE_CASE   :      List[Any]=7	,    __SCREAMING_SNAKE_CASE   :      Dict=True	,    __SCREAMING_SNAKE_CASE   :      str=True	,    __SCREAMING_SNAKE_CASE   :      Dict=True	,    __SCREAMING_SNAKE_CASE   :      List[str]=True	,    __SCREAMING_SNAKE_CASE   :      List[str]=99	,    __SCREAMING_SNAKE_CASE   :      str=32	,    __SCREAMING_SNAKE_CASE   :      Dict=2	,    __SCREAMING_SNAKE_CASE   :      List[Any]=4	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=37	,    __SCREAMING_SNAKE_CASE   :      Tuple="gelu"	,    __SCREAMING_SNAKE_CASE   :      Tuple=0.1	,    __SCREAMING_SNAKE_CASE   :      Dict=0.1	,    __SCREAMING_SNAKE_CASE   :      List[str]=512	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=16	,    __SCREAMING_SNAKE_CASE   :      str=2	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=0.02	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=3	,    __SCREAMING_SNAKE_CASE   :      Dict=4	,    __SCREAMING_SNAKE_CASE   :      Dict=None	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=0	,    )      ->      str:
							a_		:	Union[str, Any]									=   parent
							a_		:	str									=   batch_size
							a_		:	Dict									=   seq_length
							a_		:	Optional[Any]									=   is_training
							a_		:	str									=   use_input_mask
							a_		:	Dict									=   use_token_type_ids
							a_		:	str									=   use_labels
							a_		:	int									=   vocab_size
							a_		:	Union[str, Any]									=   hidden_size
							a_		:	List[str]									=   num_hidden_layers
							a_		:	List[Any]									=   num_attention_heads
							a_		:	Optional[int]									=   intermediate_size
							a_		:	List[Any]									=   hidden_act
							a_		:	int									=   hidden_dropout_prob
							a_		:	str									=   attention_probs_dropout_prob
							a_		:	int									=   max_position_embeddings
							a_		:	int									=   type_vocab_size
							a_		:	Optional[int]									=   type_sequence_label_size
							a_		:	int									=   initializer_range
							a_		:	Optional[Any]									=   num_labels
							a_		:	int									=   num_choices
							a_		:	Any									=   scope
							a_		:	Any									=   projection_dim






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Optional[int]:
							a_		:	Optional[Any]									=   ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size       )

							a_		:	List[str]									=   None
							if self.use_input_mask:
											# follow test_modeling_tf_ctrl.py
											a_		:	Tuple									=   random_attention_mask([self.batch_size, self.seq_length]       )

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

							a_		:	int									=   None
							a_		:	str									=   None
							a_		:	Optional[int]									=   None
							if self.use_labels:
											a_		:	str									=   ids_tensor([self.batch_size]	,    self.type_sequence_label_size       )
											a_		:	List[str]									=   ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels       )
											a_		:	str									=   ids_tensor([self.batch_size]	,    self.num_choices       )

							a_		:	List[str]									=   BertConfig(
							    vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    is_decoder=__SCREAMING_SNAKE_CASE	,    initializer_range=self.initializer_range	,    )
							a_		:	Any									=   DPRConfig(projection_dim=self.projection_dim	,    **config.to_dict()       )

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






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      List[str]:
							a_		:	List[str]									=   TFDPRContextEncoder(config=__SCREAMING_SNAKE_CASE       )
							a_		:	List[str]									=   model(__SCREAMING_SNAKE_CASE	,    attention_mask=__SCREAMING_SNAKE_CASE	,    token_type_ids=__SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   model(__SCREAMING_SNAKE_CASE	,    token_type_ids=__SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   model(__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(result.pooler_output.shape	,    (self.batch_size, self.projection_dim or self.hidden_size)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      Optional[Any]:
							a_		:	str									=   TFDPRQuestionEncoder(config=__SCREAMING_SNAKE_CASE       )
							a_		:	List[str]									=   model(__SCREAMING_SNAKE_CASE	,    attention_mask=__SCREAMING_SNAKE_CASE	,    token_type_ids=__SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   model(__SCREAMING_SNAKE_CASE	,    token_type_ids=__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[Any]									=   model(__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(result.pooler_output.shape	,    (self.batch_size, self.projection_dim or self.hidden_size)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      Optional[Any]:
							a_		:	Optional[Any]									=   TFDPRReader(config=__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   model(__SCREAMING_SNAKE_CASE	,    attention_mask=__SCREAMING_SNAKE_CASE       )

							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)       )
							self.parent.assertEqual(result.relevance_logits.shape	,    (self.batch_size,)       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      str:
							a_		:	Union[str, Any]									=   self.prepare_config_and_inputs()
							(
							    (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      
							)		:	str									=   config_and_inputs
							a_		:	List[str]									=   {'''input_ids''': input_ids}
							return config, inputs_dict





@require_tf
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					(
			    (
			        TFDPRContextEncoder,
			        TFDPRQuestionEncoder,
			        TFDPRReader,
			    )
			    if is_tf_available()
			    else ()
			)
			snake_case__	=					{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

			snake_case__	=					False
			snake_case__	=					False
			snake_case__	=					False
			snake_case__	=					False
			snake_case__	=					False






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]       )      ->      str:
							a_		:	int									=   TFDPRModelTester(self       )
							a_		:	Dict									=   ConfigTester(self	,    config_class=__SCREAMING_SNAKE_CASE	,    hidden_size=37       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Any:
							self.config_tester.run_common_tests()






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Dict:
							a_		:	Union[str, Any]									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_context_encoder(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	Dict									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_question_encoder(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Tuple:
							a_		:	str									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_reader(*__SCREAMING_SNAKE_CASE       )







			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_		:	Any									=   TFDPRContextEncoder.from_pretrained(__SCREAMING_SNAKE_CASE       )
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )

							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_		:	Tuple									=   TFDPRContextEncoder.from_pretrained(__SCREAMING_SNAKE_CASE       )
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )

							for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_		:	str									=   TFDPRQuestionEncoder.from_pretrained(__SCREAMING_SNAKE_CASE       )
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )

							for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_		:	Optional[Any]									=   TFDPRReader.from_pretrained(__SCREAMING_SNAKE_CASE       )
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )





@require_tf
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[Any]:
							a_		:	Tuple									=   TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''       )

							a_		:	int									=   tf.constant(
							    [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]       )  # [CLS] hello, is my dog cute? [SEP]
							a_		:	List[Any]									=   model(__SCREAMING_SNAKE_CASE       )[0]  # embedding shape = (1, 768)
							# compare the actual values for a slice.
							a_		:	Tuple									=   tf.constant(
							    [
							        [
							            0.0323_6253,
							            0.1275_3335,
							            0.1681_8509,
							            0.0027_9786,
							            0.389_6933,
							            0.2426_4945,
							            0.217_8971,
							            -0.0233_5227,
							            -0.0848_1959,
							            -0.1432_4117,
							        ]
							    ]       )
							self.assertTrue(numpy.allclose(output[:, :10].numpy()	,    expected_slice.numpy()	,    atol=1e-4       )       )












'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase									=   {
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_000,
    'block_out_channels': [32, 64],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_000,
    'block_out_channels': [192, 192 * 2, 192 * 3, 192 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'sample_size': 256,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def 		_UpperCAmelCase (				__A				: Tuple   ):
				if isinstance(__A    ,		__A   ):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise argparse.ArgumentTypeError('''boolean value expected'''   )





def 		_UpperCAmelCase (				__A				: str    ,		__A				: Tuple    ,		__A				: Optional[int]    ,		__A				: int    ,		__A				: Optional[Any]=False   ):
				a_		:	str									=   checkpoint[f'{old_prefix}.in_layers.0.weight']
				a_		:	Dict									=   checkpoint[f'{old_prefix}.in_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.in_layers.2.weight']
				a_		:	Any									=   checkpoint[f'{old_prefix}.in_layers.2.bias']
				a_		:	Any									=   checkpoint[f'{old_prefix}.emb_layers.1.weight']
				a_		:	int									=   checkpoint[f'{old_prefix}.emb_layers.1.bias']
				a_		:	Union[str, Any]									=   checkpoint[f'{old_prefix}.out_layers.0.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.weight']
				a_		:	List[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.bias']

				if has_skip:
								a_		:	int									=   checkpoint[f'{old_prefix}.skip_connection.weight']
								a_		:	List[Any]									=   checkpoint[f'{old_prefix}.skip_connection.bias']

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: Union[str, Any]    ,		__A				: Optional[int]    ,		__A				: List[str]=None   ):
				a_					,      a_					,      a_		:	Any									=   checkpoint[f'{old_prefix}.qkv.weight'].chunk(3    ,		dim=0   )
				a_					,      a_					,      a_		:	List[Any]									=   checkpoint[f'{old_prefix}.qkv.bias'].chunk(3    ,		dim=0   )

				a_		:	List[str]									=   checkpoint[f'{old_prefix}.norm.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.norm.bias']

				a_		:	Any									=   weight_q.squeeze(-1   ).squeeze(-1   )
				a_		:	int									=   bias_q.squeeze(-1   ).squeeze(-1   )
				a_		:	Any									=   weight_k.squeeze(-1   ).squeeze(-1   )
				a_		:	Optional[int]									=   bias_k.squeeze(-1   ).squeeze(-1   )
				a_		:	str									=   weight_v.squeeze(-1   ).squeeze(-1   )
				a_		:	Optional[Any]									=   bias_v.squeeze(-1   ).squeeze(-1   )

				a_		:	Union[str, Any]									=   (
				    checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1   ).squeeze(-1   )
				)
				a_		:	Any									=   checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1   ).squeeze(-1   )

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: str    ,		__A				: str   ):
				a_		:	int									=   torch.load(__A    ,		map_location='''cpu'''   )
				a_		:	Union[str, Any]									=   {}

				a_		:	int									=   checkpoint['''time_embed.0.weight''']
				a_		:	Tuple									=   checkpoint['''time_embed.0.bias''']
				a_		:	List[Any]									=   checkpoint['''time_embed.2.weight''']
				a_		:	Union[str, Any]									=   checkpoint['''time_embed.2.bias''']

				if unet_config["num_class_embeds"] is not None:
								a_		:	List[Any]									=   checkpoint['''label_emb.weight''']

				a_		:	Optional[int]									=   checkpoint['''input_blocks.0.0.weight''']
				a_		:	int									=   checkpoint['''input_blocks.0.0.bias''']

				a_		:	Optional[int]									=   unet_config['''down_block_types''']
				a_		:	Union[str, Any]									=   unet_config['''layers_per_block''']
				a_		:	Optional[Any]									=   unet_config['''attention_head_dim''']
				a_		:	str									=   unet_config['''block_out_channels''']
				a_		:	Any									=   1
				a_		:	Union[str, Any]									=   channels_list[0]

				for i, layer_type in enumerate(__A   ):
								a_		:	Any									=   channels_list[i]
								a_		:	Tuple									=   current_channels != prev_channels
								if layer_type == "ResnetDownsampleBlock2D":
												for j in range(__A   ):
																a_		:	Union[str, Any]									=   f'down_blocks.{i}.resnets.{j}'
																a_		:	int									=   f'input_blocks.{current_layer}.0'
																a_		:	List[Any]									=   True if j == 0 and downsample_block_has_skip else False
																a_		:	List[str]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																current_layer += 1

								elif layer_type == "AttnDownBlock2D":
												for j in range(__A   ):
																a_		:	Union[str, Any]									=   f'down_blocks.{i}.resnets.{j}'
																a_		:	Optional[int]									=   f'input_blocks.{current_layer}.0'
																a_		:	Optional[int]									=   True if j == 0 and downsample_block_has_skip else False
																a_		:	Any									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																a_		:	Optional[int]									=   f'down_blocks.{i}.attentions.{j}'
																a_		:	List[Any]									=   f'input_blocks.{current_layer}.1'
																a_		:	int									=   convert_attention(
																    __A    ,		__A    ,		__A    ,		__A    ,		__A   )
																current_layer += 1

								if i != len(__A   ) - 1:
												a_		:	Dict									=   f'down_blocks.{i}.downsamplers.0'
												a_		:	Optional[int]									=   f'input_blocks.{current_layer}.0'
												a_		:	Union[str, Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
												current_layer += 1

								a_		:	Any									=   current_channels

				# hardcoded the mid-block for now
				a_		:	Tuple									=   '''mid_block.resnets.0'''
				a_		:	int									=   '''middle_block.0'''
				a_		:	Tuple									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
				a_		:	List[Any]									=   '''mid_block.attentions.0'''
				a_		:	Optional[int]									=   '''middle_block.1'''
				a_		:	Optional[int]									=   convert_attention(__A    ,		__A    ,		__A    ,		__A    ,		__A   )
				a_		:	Any									=   '''mid_block.resnets.1'''
				a_		:	Tuple									=   '''middle_block.2'''
				a_		:	Tuple									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )

				a_		:	str									=   0
				a_		:	Optional[int]									=   unet_config['''up_block_types''']

				for i, layer_type in enumerate(__A   ):
								if layer_type == "ResnetUpsampleBlock2D":
												for j in range(layers_per_block + 1   ):
																a_		:	Tuple									=   f'up_blocks.{i}.resnets.{j}'
																a_		:	Optional[int]									=   f'output_blocks.{current_layer}.0'
																a_		:	Optional[Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																current_layer += 1

												if i != len(__A   ) - 1:
																a_		:	int									=   f'up_blocks.{i}.upsamplers.0'
																a_		:	Union[str, Any]									=   f'output_blocks.{current_layer-1}.1'
																a_		:	int									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
								elif layer_type == "AttnUpBlock2D":
												for j in range(layers_per_block + 1   ):
																a_		:	Optional[Any]									=   f'up_blocks.{i}.resnets.{j}'
																a_		:	str									=   f'output_blocks.{current_layer}.0'
																a_		:	List[Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																a_		:	List[Any]									=   f'up_blocks.{i}.attentions.{j}'
																a_		:	List[str]									=   f'output_blocks.{current_layer}.1'
																a_		:	str									=   convert_attention(
																    __A    ,		__A    ,		__A    ,		__A    ,		__A   )
																current_layer += 1

												if i != len(__A   ) - 1:
																a_		:	Dict									=   f'up_blocks.{i}.upsamplers.0'
																a_		:	Any									=   f'output_blocks.{current_layer-1}.2'
																a_		:	int									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )

				a_		:	Dict									=   checkpoint['''out.0.weight''']
				a_		:	int									=   checkpoint['''out.0.bias''']
				a_		:	int									=   checkpoint['''out.2.weight''']
				a_		:	str									=   checkpoint['''out.2.bias''']

				return new_checkpoint


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()

		parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
		parser.add_argument(
		    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
		)
		parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

		__lowerCAmelCase									=   parser.parse_args()
		__lowerCAmelCase									=   strabool(args.class_cond)

		__lowerCAmelCase									=   os.path.basename(args.unet_path)
		print(F"""Checkpoint: {ckpt_name}""")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				__lowerCAmelCase									=   IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCAmelCase									=   LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				__lowerCAmelCase									=   TEST_UNET_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		if not args.class_cond:
				__lowerCAmelCase									=   None

		__lowerCAmelCase									=   con_pt_to_diffuser(args.unet_path, unet_config)

		__lowerCAmelCase									=   UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				__lowerCAmelCase									=   CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__lowerCAmelCase									=   CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCAmelCase									=   CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		__lowerCAmelCase									=   CMStochasticIterativeScheduler(**scheduler_config)

		__lowerCAmelCase									=   ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)




