





import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase   				=			logging.get_logger(__name__)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]    ):
		print("""Loading config file..."""    )

		def flatten_yaml_as_dict(UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							List[Any]=""					,   UpperCamelCase__:							Union[str, Any]="."    ):
				SCREAMING_SNAKE_CASE__			=      []
				for k, v in d.items():
						SCREAMING_SNAKE_CASE__			=      parent_key + sep + k if parent_key else k
						if isinstance(UpperCamelCase__					,   collections.abc.MutableMapping    ):
								items.extend(flatten_yaml_as_dict(UpperCamelCase__					,   UpperCamelCase__					,   sep=UpperCamelCase__    ).items()    )
						else:
								items.append((new_key, v)    )
				return dict(UpperCamelCase__    )

		SCREAMING_SNAKE_CASE__			=      argparse.Namespace()
		with open(UpperCamelCase__					,   """r"""    ) as yaml_file:
				try:
						SCREAMING_SNAKE_CASE__			=      yaml.load(UpperCamelCase__					,   Loader=yaml.FullLoader    )

						SCREAMING_SNAKE_CASE__			=      flatten_yaml_as_dict(UpperCamelCase__    )
						for k, v in flat_cfg.items():
								setattr(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
				except yaml.YAMLError as exc:
						logger.error("""Error while loading config file: {}. Error message: {}""".format(UpperCamelCase__					,   str(UpperCamelCase__    )    )    )
		return config






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str					,   UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      MobileViTVaConfig()

		SCREAMING_SNAKE_CASE__			=      False

		# dataset
		if task_name.startswith("""imagenet1k_"""    ):
				SCREAMING_SNAKE_CASE__			=      1_000
				if int(task_name.strip().split("""_"""    )[-1]    ) == 384:
						SCREAMING_SNAKE_CASE__			=      384
				else:
						SCREAMING_SNAKE_CASE__			=      256
				SCREAMING_SNAKE_CASE__			=      """imagenet-1k-id2label.json"""
		elif task_name.startswith("""imagenet21k_to_1k_"""    ):
				SCREAMING_SNAKE_CASE__			=      21_000
				if int(task_name.strip().split("""_"""    )[-1]    ) == 384:
						SCREAMING_SNAKE_CASE__			=      384
				else:
						SCREAMING_SNAKE_CASE__			=      256
				SCREAMING_SNAKE_CASE__			=      """imagenet-22k-id2label.json"""
		elif task_name.startswith("""ade20k_"""    ):
				SCREAMING_SNAKE_CASE__			=      151
				SCREAMING_SNAKE_CASE__			=      512
				SCREAMING_SNAKE_CASE__			=      """ade20k-id2label.json"""
				SCREAMING_SNAKE_CASE__			=      True
		elif task_name.startswith("""voc_"""    ):
				SCREAMING_SNAKE_CASE__			=      21
				SCREAMING_SNAKE_CASE__			=      512
				SCREAMING_SNAKE_CASE__			=      """pascal-voc-id2label.json"""
				SCREAMING_SNAKE_CASE__			=      True

		# orig_config
		SCREAMING_SNAKE_CASE__			=      load_orig_config_file(UpperCamelCase__    )
		assert getattr(UpperCamelCase__					,   """model.classification.name"""					,   -1    ) == "mobilevit_v2", "Invalid model"
		SCREAMING_SNAKE_CASE__			=      getattr(UpperCamelCase__					,   """model.classification.mitv2.width_multiplier"""					,   1.0    )
		assert (
		    getattr(UpperCamelCase__					,   """model.classification.mitv2.attn_norm_layer"""					,   -1    ) == "layer_norm_2d"
		), "Norm layers other than layer_norm_2d is not supported"
		SCREAMING_SNAKE_CASE__			=      getattr(UpperCamelCase__					,   """model.classification.activation.name"""					,   """swish"""    )
		# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

		if is_segmentation_model:
				SCREAMING_SNAKE_CASE__			=      getattr(UpperCamelCase__					,   """model.segmentation.output_stride"""					,   16    )
				if "_deeplabv3" in task_name:
						SCREAMING_SNAKE_CASE__			=      getattr(UpperCamelCase__					,   """model.segmentation.deeplabv3.aspp_rates"""					,   [12, 24, 36]    )
						SCREAMING_SNAKE_CASE__			=      getattr(UpperCamelCase__					,   """model.segmentation.deeplabv3.aspp_out_channels"""					,   512    )
						SCREAMING_SNAKE_CASE__			=      getattr(UpperCamelCase__					,   """model.segmentation.deeplabv3.aspp_dropout"""					,   0.1    )

    # id2label
		SCREAMING_SNAKE_CASE__			=      """huggingface/label-files"""
		SCREAMING_SNAKE_CASE__			=      json.load(open(hf_hub_download(UpperCamelCase__					,   UpperCamelCase__					,   repo_type="""dataset"""    )					,   """r"""    )    )
		SCREAMING_SNAKE_CASE__			=      {int(UpperCamelCase__    ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE__			=      idalabel
		SCREAMING_SNAKE_CASE__			=      {v: k for k, v in idalabel.items()}

		return config






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Dict					,   UpperCamelCase__:							Dict					,   UpperCamelCase__:							int    ):
		SCREAMING_SNAKE_CASE__			=      dct.pop(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      val






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Tuple=False    ):
		if base_model:
				SCREAMING_SNAKE_CASE__			=      """"""
		else:
				SCREAMING_SNAKE_CASE__			=      """mobilevitv2."""

		SCREAMING_SNAKE_CASE__			=      []
		for k in state_dict.keys():
				if k[:8] == "encoder.":
						SCREAMING_SNAKE_CASE__			=      k[8:]
				else:
						SCREAMING_SNAKE_CASE__			=      k

				if ".block." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace(""".block."""					,   """."""    )
				if ".conv." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace(""".conv."""					,   """.convolution."""    )
				if ".norm." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace(""".norm."""					,   """.normalization."""    )

				if "conv_1." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace("""conv_1."""					,   f'''{model_prefix}conv_stem.'''    )
				for i in [1, 2]:
						if f'''layer_{i}.''' in k:
								SCREAMING_SNAKE_CASE__			=      k_new.replace(f'''layer_{i}.'''					,   f'''{model_prefix}encoder.layer.{i-1}.layer.'''    )
				if ".exp_1x1." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace(""".exp_1x1."""					,   """.expand_1x1."""    )
				if ".red_1x1." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace(""".red_1x1."""					,   """.reduce_1x1."""    )

				for i in [3, 4, 5]:
						if f'''layer_{i}.0.''' in k:
								SCREAMING_SNAKE_CASE__			=      k_new.replace(f'''layer_{i}.0.'''					,   f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''    )
						if f'''layer_{i}.1.local_rep.0.''' in k:
								SCREAMING_SNAKE_CASE__			=      k_new.replace(f'''layer_{i}.1.local_rep.0.'''					,   f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''    )
						if f'''layer_{i}.1.local_rep.1.''' in k:
								SCREAMING_SNAKE_CASE__			=      k_new.replace(f'''layer_{i}.1.local_rep.1.'''					,   f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''    )

				for i in [3, 4, 5]:
						if i == 3:
								SCREAMING_SNAKE_CASE__			=      [0, 1]
						elif i == 4:
								SCREAMING_SNAKE_CASE__			=      [0, 1, 2, 3]
						elif i == 5:
								SCREAMING_SNAKE_CASE__			=      [0, 1, 2]

						for j in j_in:
								if f'''layer_{i}.1.global_rep.{j}.''' in k:
										SCREAMING_SNAKE_CASE__			=      k_new.replace(
										    f'''layer_{i}.1.global_rep.{j}.'''					,   f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''    )
						if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
								SCREAMING_SNAKE_CASE__			=      k_new.replace(
								    f'''layer_{i}.1.global_rep.{j+1}.'''					,   f'''{model_prefix}encoder.layer.{i-1}.layernorm.'''    )

						if f'''layer_{i}.1.conv_proj.''' in k:
								SCREAMING_SNAKE_CASE__			=      k_new.replace(f'''layer_{i}.1.conv_proj.'''					,   f'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''    )

				if "pre_norm_attn.0." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace("""pre_norm_attn.0."""					,   """layernorm_before."""    )
				if "pre_norm_attn.1." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace("""pre_norm_attn.1."""					,   """attention."""    )
				if "pre_norm_ffn.0." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace("""pre_norm_ffn.0."""					,   """layernorm_after."""    )
				if "pre_norm_ffn.1." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace("""pre_norm_ffn.1."""					,   """ffn.conv1."""    )
				if "pre_norm_ffn.3." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace("""pre_norm_ffn.3."""					,   """ffn.conv2."""    )

				if "classifier.1." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace("""classifier.1."""					,   """classifier."""    )

				if "seg_head." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace("""seg_head."""					,   """segmentation_head."""    )
				if ".aspp_layer." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace(""".aspp_layer."""					,   """."""    )
				if ".aspp_pool." in k:
						SCREAMING_SNAKE_CASE__			=      k_new.replace(""".aspp_pool."""					,   """."""    )

				rename_keys.append((k, k_new)    )
		return rename_keys






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for k in state_dict.keys():
				if k.startswith("""seg_head.aux_head."""    ):
						keys_to_ignore.append(UpperCamelCase__    )
		for k in keys_to_ignore:
				state_dict.pop(UpperCamelCase__					,   UpperCamelCase__    )






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
		# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
		SCREAMING_SNAKE_CASE__			=      Image.open(requests.get(UpperCamelCase__					,   stream=UpperCamelCase__    ).raw    )
		return im






@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Dict					,   UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Union[str, Any]    ):
		SCREAMING_SNAKE_CASE__			=      get_mobilevitva_config(UpperCamelCase__					,   UpperCamelCase__    )

		# load original state_dict
		SCREAMING_SNAKE_CASE__			=      torch.load(UpperCamelCase__					,   map_location="""cpu"""    )

		# load huggingface model
		if task_name.startswith("""ade20k_"""    ) or task_name.startswith("""voc_"""    ):
				SCREAMING_SNAKE_CASE__			=      MobileViTVaForSemanticSegmentation(UpperCamelCase__    ).eval()
				SCREAMING_SNAKE_CASE__			=      False
		else:
				SCREAMING_SNAKE_CASE__			=      MobileViTVaForImageClassification(UpperCamelCase__    ).eval()
				SCREAMING_SNAKE_CASE__			=      False

		# remove and rename some keys of load the original model
		SCREAMING_SNAKE_CASE__			=      checkpoint
		remove_unused_keys(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      create_rename_keys(UpperCamelCase__					,   base_model=UpperCamelCase__    )
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )

		# load modified state_dict
		model.load_state_dict(UpperCamelCase__    )

		# Check outputs on an image, prepared by MobileViTImageProcessor
		SCREAMING_SNAKE_CASE__			=      MobileViTImageProcessor(crop_size=config.image_size					,   size=config.image_size + 32    )
		SCREAMING_SNAKE_CASE__			=      image_processor(images=prepare_img()					,   return_tensors="""pt"""    )
		SCREAMING_SNAKE_CASE__			=      model(**UpperCamelCase__    )

		# verify classification model
		if task_name.startswith("""imagenet"""    ):
				SCREAMING_SNAKE_CASE__			=      outputs.logits
				SCREAMING_SNAKE_CASE__			=      logits.argmax(-1    ).item()
				print("""Predicted class:"""					,   model.config.idalabel[predicted_class_idx]    )
				if task_name.startswith("""imagenet1k_256"""    ) and config.width_multiplier == 1.0:
						# expected_logits for base variant
						SCREAMING_SNAKE_CASE__			=      torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1]    )
						assert torch.allclose(logits[0, :3]					,   UpperCamelCase__					,   atol=1e-4    )

		Path(UpperCamelCase__    ).mkdir(exist_ok=UpperCamelCase__    )
		print(f'''Saving model {task_name} to {pytorch_dump_folder_path}'''    )
		model.save_pretrained(UpperCamelCase__    )
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
		image_processor.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
	_lowerCamelCase   				=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--task',
	    default='imagenet1k_256',
	    type=str,
	    help=(
	        'Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '
	        '\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '
	    ),
	    choices=[
	        'imagenet1k_256',
	        'imagenet1k_384',
	        'imagenet21k_to_1k_256',
	        'imagenet21k_to_1k_384',
	        'ade20k_deeplabv3',
	        'voc_deeplabv3',
	    ],
	)

	parser.add_argument(
	    '--orig_checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
	)
	parser.add_argument('--orig_config_path', required=True, type=str, help='Path to the original config file.')
	parser.add_argument(
	    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
	)

	_lowerCamelCase   				=			parser.parse_args()
	convert_mobilevitva_checkpoint(
	    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
	)






import copy
import os
from typing import Union

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


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct_text_model"
							lowerCamelCase_           =					["past_key_values"]
							lowerCamelCase_           =					{
							    "hidden_size": "hidden_size",
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(							self    :Union[str, Any]							,				__A    :Any=5_0244							,				__A    :Optional[Any]=768							,				__A    :Tuple=64							,				__A    :List[str]=2048							,				__A    :int=12							,				__A    :str=12							,				__A    :Any=32							,				__A    :Tuple=128							,				__A    :int=0.1							,				__A    :str=1E-6							,				__A    :Optional[Any]=1.0							,				__A    :Union[str, Any]="gelu_new"							,				__A    :Any=0							,				__A    :List[str]=False							,				__A    :Optional[Any]=0							,				__A    :int=1							,				__A    :Optional[int]=False							,				__A    :Optional[Any]=True							,				**__A    :List[Any]							,				)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      d_kv
									SCREAMING_SNAKE_CASE__			=      d_ff
									SCREAMING_SNAKE_CASE__			=      num_layers
									SCREAMING_SNAKE_CASE__			=      num_heads
									SCREAMING_SNAKE_CASE__			=      relative_attention_num_buckets
									SCREAMING_SNAKE_CASE__			=      relative_attention_max_distance
									SCREAMING_SNAKE_CASE__			=      dropout_rate
									SCREAMING_SNAKE_CASE__			=      layer_norm_epsilon
									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      use_cache

									SCREAMING_SNAKE_CASE__			=      eos_token_id
									SCREAMING_SNAKE_CASE__			=      decoder_start_token_id

									# for backwards compatibility
									SCREAMING_SNAKE_CASE__			=      dense_act_fn

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


							@classmethod
							def      _snake_case  (							cls    :Optional[int]							,				__A    :Union[str, os.PathLike]							,				**__A    :Optional[int]		)   ->   "PretrainedConfig":



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

									# get the text config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""		) == "pix2struct":
											SCREAMING_SNAKE_CASE__			=      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(__A							,				**__A		)




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct_vision_model"


							def __init__(							self    :Optional[int]							,				__A    :int=768							,				__A    :Optional[Any]=768							,				__A    :Union[str, Any]=2048							,				__A    :int=64							,				__A    :Union[str, Any]=12							,				__A    :str=12							,				__A    :Any="gelu_new"							,				__A    :List[Any]=1E-6							,				__A    :Dict=0.0							,				__A    :int=0.0							,				__A    :int=1E-10							,				__A    :Dict=1.0							,				__A    :int=4096							,				__A    :int=32							,				__A    :int=128							,				**__A    :Tuple							,				)   ->   str:



									"""simple docstring"""



									super().__init__(**__A		)

									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      patch_embed_hidden_size
									SCREAMING_SNAKE_CASE__			=      d_ff
									SCREAMING_SNAKE_CASE__			=      dropout_rate
									SCREAMING_SNAKE_CASE__			=      num_hidden_layers
									SCREAMING_SNAKE_CASE__			=      num_attention_heads
									SCREAMING_SNAKE_CASE__			=      initializer_range
									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      attention_dropout
									SCREAMING_SNAKE_CASE__			=      layer_norm_eps
									SCREAMING_SNAKE_CASE__			=      dense_act_fn
									SCREAMING_SNAKE_CASE__			=      seq_len
									SCREAMING_SNAKE_CASE__			=      relative_attention_num_buckets
									SCREAMING_SNAKE_CASE__			=      relative_attention_max_distance
									SCREAMING_SNAKE_CASE__			=      d_kv


							@classmethod
							def      _snake_case  (							cls    :str							,				__A    :Union[str, os.PathLike]							,				**__A    :str		)   ->   "PretrainedConfig":



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

									# get the vision config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""		) == "pix2struct":
											SCREAMING_SNAKE_CASE__			=      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(__A							,				**__A		)




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct"
							lowerCamelCase_           =					True


							def __init__(							self    :str							,				__A    :Optional[Any]=None							,				__A    :List[str]=None							,				__A    :Optional[Any]=1.0							,				__A    :Optional[Any]=0.0_2							,				__A    :Any=False							,				__A    :Tuple=False							,				__A    :Any=True							,				**__A    :Dict							,				)   ->   Union[str, Any]:



									"""simple docstring"""



									super().__init__(tie_word_embeddings=__A							,				is_encoder_decoder=__A							,				**__A		)

									if text_config is None:
											SCREAMING_SNAKE_CASE__			=      {}
											logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""		)

									if vision_config is None:
											SCREAMING_SNAKE_CASE__			=      {}
											logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""		)

									SCREAMING_SNAKE_CASE__			=      PixaStructTextConfig(**__A		)
									SCREAMING_SNAKE_CASE__			=      PixaStructVisionConfig(**__A		)

									SCREAMING_SNAKE_CASE__			=      self.text_config.decoder_start_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.pad_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.eos_token_id

									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      initializer_range

									SCREAMING_SNAKE_CASE__			=      self.initializer_range
									SCREAMING_SNAKE_CASE__			=      self.initializer_range

									SCREAMING_SNAKE_CASE__			=      is_vqa


							@classmethod
							def      _snake_case  (							cls    :Union[str, Any]							,				__A    :PixaStructTextConfig							,				__A    :PixaStructVisionConfig							,				**__A    :Optional[int]		)   ->   Optional[Any]:



									"""simple docstring"""



									return cls(text_config=text_config.to_dict()							,				vision_config=vision_config.to_dict()							,				**__A		)


							def      _snake_case  (							self    :str		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=      self.text_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.vision_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.__class__.model_type
									return output