


'''simple docstring'''






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__A	:	Tuple     =     {
    "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",
}

__A	:	Optional[Any]     =     {
    "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",
}

__A	:	Optional[Any]     =     {
    "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",
}

__A	:	Optional[Any]     =     {
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__A	:	List[str]     =     {
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__A	:	str     =     {
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}





def      UpperCAmelCase			(		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''


		if isinstance(lowerCamelCase__				,      lowerCamelCase__ ):
				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			(		lowerCamelCase_  :Optional[int]				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :Tuple				,      lowerCamelCase_  :int=False ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						checkpoint[F'''{old_prefix}.in_layers.0.weight''']
		snake_case_		:							Any			       =						checkpoint[F'''{old_prefix}.in_layers.0.bias''']
		snake_case_		:							Optional[Any]			       =						checkpoint[F'''{old_prefix}.in_layers.2.weight''']
		snake_case_		:							List[Any]			       =						checkpoint[F'''{old_prefix}.in_layers.2.bias''']
		snake_case_		:							Dict			       =						checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
		snake_case_		:							List[str]			       =						checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
		snake_case_		:							List[str]			       =						checkpoint[F'''{old_prefix}.out_layers.0.weight''']
		snake_case_		:							Optional[int]			       =						checkpoint[F'''{old_prefix}.out_layers.0.bias''']
		snake_case_		:							Optional[int]			       =						checkpoint[F'''{old_prefix}.out_layers.3.weight''']
		snake_case_		:							Union[str, Any]			       =						checkpoint[F'''{old_prefix}.out_layers.3.bias''']

		if has_skip:
				snake_case_		:							List[str]			       =						checkpoint[F'''{old_prefix}.skip_connection.weight''']
				snake_case_		:							int			       =						checkpoint[F'''{old_prefix}.skip_connection.bias''']

		return new_checkpoint





def      UpperCAmelCase			(		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Tuple				,      lowerCamelCase_  :str				,      lowerCamelCase_  :Optional[Any]=None ):







		'''simple docstring'''
		snake_case_		:							int			       =						checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3				,      dim=0 )
		snake_case_		:							List[str]			       =						checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3				,      dim=0 )

		snake_case_		:							Any			       =						checkpoint[F'''{old_prefix}.norm.weight''']
		snake_case_		:							int			       =						checkpoint[F'''{old_prefix}.norm.bias''']

		snake_case_		:							Union[str, Any]			       =						weight_q.squeeze(-1 ).squeeze(-1 )
		snake_case_		:							int			       =						bias_q.squeeze(-1 ).squeeze(-1 )
		snake_case_		:							List[Any]			       =						weight_k.squeeze(-1 ).squeeze(-1 )
		snake_case_		:							Any			       =						bias_k.squeeze(-1 ).squeeze(-1 )
		snake_case_		:							List[str]			       =						weight_v.squeeze(-1 ).squeeze(-1 )
		snake_case_		:							Any			       =						bias_v.squeeze(-1 ).squeeze(-1 )

		snake_case_		:							Dict			       =						(
		    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
		)
		snake_case_		:							List[str]			       =						checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )

		return new_checkpoint





def      UpperCAmelCase			(		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Dict ):







		'''simple docstring'''
		snake_case_		:							int			       =						torch.load(lowerCamelCase__				,      map_location="""cpu""" )
		snake_case_		:							Union[str, Any]			       =						{}

		snake_case_		:							Tuple			       =						checkpoint["time_embed.0.weight"]
		snake_case_		:							Any			       =						checkpoint["time_embed.0.bias"]
		snake_case_		:							Union[str, Any]			       =						checkpoint["time_embed.2.weight"]
		snake_case_		:							List[Any]			       =						checkpoint["time_embed.2.bias"]

		if unet_config["num_class_embeds"] is not None:
				snake_case_		:							str			       =						checkpoint["label_emb.weight"]

		snake_case_		:							Union[str, Any]			       =						checkpoint["input_blocks.0.0.weight"]
		snake_case_		:							Optional[int]			       =						checkpoint["input_blocks.0.0.bias"]

		snake_case_		:							Tuple			       =						unet_config["down_block_types"]
		snake_case_		:							Optional[Any]			       =						unet_config["layers_per_block"]
		snake_case_		:							Optional[Any]			       =						unet_config["attention_head_dim"]
		snake_case_		:							int			       =						unet_config["block_out_channels"]
		snake_case_		:							int			       =						1
		snake_case_		:							List[Any]			       =						channels_list[0]

		for i, layer_type in enumerate(lowerCamelCase__ ):
				snake_case_		:							Tuple			       =						channels_list[i]
				snake_case_		:							Tuple			       =						current_channels != prev_channels
				if layer_type == "ResnetDownsampleBlock2D":
						for j in range(lowerCamelCase__ ):
								snake_case_		:							Dict			       =						F'''down_blocks.{i}.resnets.{j}'''
								snake_case_		:							Dict			       =						F'''input_blocks.{current_layer}.0'''
								snake_case_		:							Optional[Any]			       =						True if j == 0 and downsample_block_has_skip else False
								snake_case_		:							Dict			       =						convert_resnet(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      has_skip=lowerCamelCase__ )
								current_layer += 1

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

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

				snake_case_		:							Union[str, Any]			       =						current_channels

		# hardcoded the mid-block for now
		snake_case_		:							str			       =						"mid_block.resnets.0"
		snake_case_		:							Optional[Any]			       =						"middle_block.0"
		snake_case_		:							Optional[int]			       =						convert_resnet(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ )
		snake_case_		:							str			       =						"mid_block.attentions.0"
		snake_case_		:							int			       =						"middle_block.1"
		snake_case_		:							Union[str, Any]			       =						convert_attention(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ )
		snake_case_		:							List[str]			       =						"mid_block.resnets.1"
		snake_case_		:							int			       =						"middle_block.2"
		snake_case_		:							Any			       =						convert_resnet(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ )

		snake_case_		:							Dict			       =						0
		snake_case_		:							List[str]			       =						unet_config["up_block_types"]

		for i, layer_type in enumerate(lowerCamelCase__ ):
				if layer_type == "ResnetUpsampleBlock2D":
						for j in range(layers_per_block + 1 ):
								snake_case_		:							Union[str, Any]			       =						F'''up_blocks.{i}.resnets.{j}'''
								snake_case_		:							Optional[int]			       =						F'''output_blocks.{current_layer}.0'''
								snake_case_		:							Union[str, Any]			       =						convert_resnet(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      has_skip=lowerCamelCase__ )
								current_layer += 1

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

						if i != len(lowerCamelCase__ ) - 1:
								snake_case_		:							Any			       =						F'''up_blocks.{i}.upsamplers.0'''
								snake_case_		:							List[Any]			       =						F'''output_blocks.{current_layer-1}.2'''
								snake_case_		:							Optional[int]			       =						convert_resnet(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ )

		snake_case_		:							Union[str, Any]			       =						checkpoint["out.0.weight"]
		snake_case_		:							Optional[int]			       =						checkpoint["out.0.bias"]
		snake_case_		:							Optional[int]			       =						checkpoint["out.2.weight"]
		snake_case_		:							List[Any]			       =						checkpoint["out.2.bias"]

		return new_checkpoint


if __name__ == "__main__":
							__A	:	Optional[int]     =     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.')

							__A	:	Any     =     parser.parse_args()
							__A	:	Optional[Any]     =     strabool(args.class_cond)

							__A	:	Any     =     os.path.basename(args.unet_path)
							print(F'Checkpoint: {ckpt_name}')

							# Get U-Net config
							if "imagenet64" in ckpt_name:
														__A	:	Optional[int]     =     IMAGENET_64_UNET_CONFIG
							elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														__A	:	str     =     LSUN_256_UNET_CONFIG
							elif "test" in ckpt_name:
														__A	:	List[Any]     =     TEST_UNET_CONFIG
							else:
														raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

							if not args.class_cond:
														__A	:	List[str]     =     None

							__A	:	str     =     con_pt_to_diffuser(args.unet_path, unet_config)

							__A	:	str     =     UNetaDModel(**unet_config)
							image_unet.load_state_dict(converted_unet_ckpt)

							# Get scheduler config
							if "cd" in ckpt_name or "test" in ckpt_name:
														__A	:	Union[str, Any]     =     CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														__A	:	List[Any]     =     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)):
														__A	:	List[str]     =     CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

							__A	:	Optional[int]     =     CMStochasticIterativeScheduler(**scheduler_config)

							__A	:	Optional[Any]     =     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)



'''simple docstring'''






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A	:	str     =     logging.get_logger(__name__)

__A	:	Dict     =     {
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Tuple   		=  'pix2struct_text_model'
							lowercase			:		int   		=  ['past_key_values']
							lowercase			:		int   		=  {
							    'hidden_size': 'hidden_size',
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}





							def __init__(  self		:int  ,_UpperCamelCase		:List[str]=5_0_2_4_4  ,_UpperCamelCase		:Optional[Any]=7_6_8  ,_UpperCamelCase		:Dict=6_4  ,_UpperCamelCase		:Dict=2_0_4_8  ,_UpperCamelCase		:Optional[int]=1_2  ,_UpperCamelCase		:Union[str, Any]=1_2  ,_UpperCamelCase		:List[str]=3_2  ,_UpperCamelCase		:Union[str, Any]=1_2_8  ,_UpperCamelCase		:Tuple=0.1  ,_UpperCamelCase		:List[str]=1E-6  ,_UpperCamelCase		:List[Any]=1.0  ,_UpperCamelCase		:Optional[int]="gelu_new"  ,_UpperCamelCase		:Dict=0  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:Optional[int]=0  ,_UpperCamelCase		:Dict=1  ,_UpperCamelCase		:List[Any]=False  ,_UpperCamelCase		:Tuple=True  ,**_UpperCamelCase		:List[Any]  ,):
									snake_case_		:							List[str]			       =						vocab_size
									snake_case_		:							Any			       =						hidden_size
									snake_case_		:							Any			       =						d_kv
									snake_case_		:							List[Any]			       =						d_ff
									snake_case_		:							Union[str, Any]			       =						num_layers
									snake_case_		:							Union[str, Any]			       =						num_heads
									snake_case_		:							str			       =						relative_attention_num_buckets
									snake_case_		:							Optional[int]			       =						relative_attention_max_distance
									snake_case_		:							Tuple			       =						dropout_rate
									snake_case_		:							Tuple			       =						layer_norm_epsilon
									snake_case_		:							Any			       =						initializer_factor
									snake_case_		:							List[Any]			       =						use_cache

									snake_case_		:							Optional[int]			       =						eos_token_id
									snake_case_		:							List[Any]			       =						decoder_start_token_id

									# for backwards compatibility
									snake_case_		:							List[str]			       =						dense_act_fn

									super().__init__(
									    pad_token_id=_UpperCamelCase  ,eos_token_id=_UpperCamelCase  ,decoder_start_token_id=_UpperCamelCase  ,tie_word_embeddings=_UpperCamelCase  ,is_decoder=_UpperCamelCase  ,**_UpperCamelCase  ,)





							@classmethod
							def 		a__   (  cls		:Optional[int]  ,_UpperCamelCase		:Union[str, os.PathLike]  ,**_UpperCamelCase		:Optional[int]			):
									cls._set_token_in_kwargs(_UpperCamelCase			)

									snake_case_       ,      snake_case_		:							Optional[Any]			       =						cls.get_config_dict(_UpperCamelCase  ,**_UpperCamelCase			)

									# get the text config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""			) == "pix2struct":
											snake_case_		:							Optional[int]			       =						config_dict["""text_config"""]

									if "model_type" in config_dict and hasattr(cls  ,"""model_type"""			) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
											    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

									return cls.from_dict(_UpperCamelCase  ,**_UpperCamelCase			)





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Dict   		=  'pix2struct_vision_model'





							def __init__(  self		:List[str]  ,_UpperCamelCase		:Any=7_6_8  ,_UpperCamelCase		:List[str]=7_6_8  ,_UpperCamelCase		:List[Any]=2_0_4_8  ,_UpperCamelCase		:Union[str, Any]=6_4  ,_UpperCamelCase		:int=1_2  ,_UpperCamelCase		:int=1_2  ,_UpperCamelCase		:Any="gelu_new"  ,_UpperCamelCase		:Optional[int]=1E-6  ,_UpperCamelCase		:List[Any]=0.0  ,_UpperCamelCase		:Union[str, Any]=0.0  ,_UpperCamelCase		:int=1E-1_0  ,_UpperCamelCase		:str=1.0  ,_UpperCamelCase		:Optional[int]=4_0_9_6  ,_UpperCamelCase		:str=3_2  ,_UpperCamelCase		:Union[str, Any]=1_2_8  ,**_UpperCamelCase		:Union[str, Any]  ,):
									super().__init__(**_UpperCamelCase			)

									snake_case_		:							str			       =						hidden_size
									snake_case_		:							Tuple			       =						patch_embed_hidden_size
									snake_case_		:							Optional[int]			       =						d_ff
									snake_case_		:							Dict			       =						dropout_rate
									snake_case_		:							Optional[int]			       =						num_hidden_layers
									snake_case_		:							Dict			       =						num_attention_heads
									snake_case_		:							Union[str, Any]			       =						initializer_range
									snake_case_		:							Optional[Any]			       =						initializer_factor
									snake_case_		:							List[str]			       =						attention_dropout
									snake_case_		:							List[str]			       =						layer_norm_eps
									snake_case_		:							List[str]			       =						dense_act_fn
									snake_case_		:							int			       =						seq_len
									snake_case_		:							str			       =						relative_attention_num_buckets
									snake_case_		:							Tuple			       =						relative_attention_max_distance
									snake_case_		:							List[str]			       =						d_kv





							@classmethod
							def 		a__   (  cls		:Optional[Any]  ,_UpperCamelCase		:Union[str, os.PathLike]  ,**_UpperCamelCase		:List[str]			):
									cls._set_token_in_kwargs(_UpperCamelCase			)

									snake_case_       ,      snake_case_		:							Tuple			       =						cls.get_config_dict(_UpperCamelCase  ,**_UpperCamelCase			)

									# get the vision config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""			) == "pix2struct":
											snake_case_		:							Dict			       =						config_dict["""vision_config"""]

									if "model_type" in config_dict and hasattr(cls  ,"""model_type"""			) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
											    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

									return cls.from_dict(_UpperCamelCase  ,**_UpperCamelCase			)





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Union[str, Any]   		=  'pix2struct'
							lowercase			:		int   		=  True





							def __init__(  self		:int  ,_UpperCamelCase		:Tuple=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Dict=1.0  ,_UpperCamelCase		:Optional[int]=0.02  ,_UpperCamelCase		:Tuple=False  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:Dict=True  ,**_UpperCamelCase		:Union[str, Any]  ,):
									super().__init__(tie_word_embeddings=_UpperCamelCase  ,is_encoder_decoder=_UpperCamelCase  ,**_UpperCamelCase			)

									if text_config is None:
											snake_case_		:							Optional[int]			       =						{}
											logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""			)

									if vision_config is None:
											snake_case_		:							List[str]			       =						{}
											logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""			)

									snake_case_		:							Optional[int]			       =						PixaStructTextConfig(**_UpperCamelCase			)
									snake_case_		:							List[str]			       =						PixaStructVisionConfig(**_UpperCamelCase			)

									snake_case_		:							Any			       =						self.text_config.decoder_start_token_id
									snake_case_		:							List[Any]			       =						self.text_config.pad_token_id
									snake_case_		:							Optional[int]			       =						self.text_config.eos_token_id

									snake_case_		:							Optional[int]			       =						initializer_factor
									snake_case_		:							List[str]			       =						initializer_range

									snake_case_		:							Union[str, Any]			       =						self.initializer_range
									snake_case_		:							str			       =						self.initializer_range

									snake_case_		:							Dict			       =						is_vqa





							@classmethod
							def 		a__   (  cls		:Any  ,_UpperCamelCase		:PixaStructTextConfig  ,_UpperCamelCase		:PixaStructVisionConfig  ,**_UpperCamelCase		:Any			):
									return cls(text_config=text_config.to_dict()  ,vision_config=vision_config.to_dict()  ,**_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							int			       =						copy.deepcopy(self.__dict__			)
									snake_case_		:							str			       =						self.text_config.to_dict()
									snake_case_		:							Tuple			       =						self.vision_config.to_dict()
									snake_case_		:							Any			       =						self.__class__.model_type
									return output