

'''simple docstring'''
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_UpperCAmelCase  :		Union[str, Any]  =		logging.get_logger('''transformers.models.speecht5''')

_UpperCAmelCase  :		Union[str, Any]  =		{
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
_UpperCAmelCase  :		int  =		{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_UpperCAmelCase  :		List[Any]  =		{
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
_UpperCAmelCase  :		List[Any]  =		{
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
_UpperCAmelCase  :		Union[str, Any]  =		{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_UpperCAmelCase  :		Any  =		{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_UpperCAmelCase  :		List[str]  =		{
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
_UpperCAmelCase  :		List[Any]  =		{
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
_UpperCAmelCase  :		Tuple  =		{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCAmelCase  :		int  =		{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase  :		Any  =		{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase  :		int  =		[]
_UpperCAmelCase  :		Optional[int]  =		[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
_UpperCAmelCase  :		Any  =		IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
_UpperCAmelCase  :		List[str]  =		IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
_UpperCAmelCase  :		Union[str, Any]  =		IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]




def 	UpperCamelCase   (       lowercase_						:    Union[str, Any]     ,					lowercase_						:    str     ,					lowercase_						:    List[str]     ,					lowercase_						:    Dict     ,					lowercase_						:    Union[str, Any]	)		->	List[str]:




  '''simple docstring'''



  for attribute in key.split('''.'''	):
    lowercase									=getattr(lowercase_     ,					lowercase_	)

  if weight_type is not None:
    lowercase									=getattr(lowercase_     ,					lowercase_	).shape
  else:
    lowercase									=hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
        f' {value.shape} for {full_name}'	)

  if weight_type == "weight":
    lowercase									=value
  elif weight_type == "weight_g":
    lowercase									=value
  elif weight_type == "weight_v":
    lowercase									=value
  elif weight_type == "bias":
    lowercase									=value
  elif weight_type == "running_mean":
    lowercase									=value
  elif weight_type == "running_var":
    lowercase									=value
  elif weight_type == "num_batches_tracked":
    lowercase									=value
  else:
    lowercase									=value

  logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'	)




def 	UpperCamelCase   (       lowercase_						:    Tuple     ,					lowercase_						:    Optional[int]	)		->	List[str]:




  '''simple docstring'''



  for key in ignore_keys:
    if key.endswith('''.*'''	):
      if name.startswith(key[:-1]	):
        return True
    elif ".*." in key:
      lowercase									=key.split('''.*.'''	)
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False




def 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    Dict     ,					lowercase_						:    List[Any]	)		->	Tuple:




  '''simple docstring'''



  lowercase									=[]

  if task == "s2t":
    lowercase									=hf_model.speechta.encoder.prenet.feature_encoder
    lowercase									=MAPPING_S2T
    lowercase									=IGNORE_KEYS_S2T
  elif task == "t2s":
    lowercase									=None
    lowercase									=MAPPING_T2S
    lowercase									=IGNORE_KEYS_T2S
  elif task == "s2s":
    lowercase									=hf_model.speechta.encoder.prenet.feature_encoder
    lowercase									=MAPPING_S2S
    lowercase									=IGNORE_KEYS_S2S
  else:
    raise ValueError(f'Unsupported task: {task}'	)

  for name, value in fairseq_dict.items():
    if should_ignore(lowercase_     ,					lowercase_	):
      logger.info(f'{name} was ignored'	)
      continue

    lowercase									=False
    if "conv_layers" in name:
      load_conv_layer(
          lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					hf_model.config.feat_extract_norm == '''group'''     ,					)
      lowercase									=True
    else:
      for key, mapped_key in MAPPING.items():
        # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

        if "*" in key:
          lowercase									=key.split('''.*.'''	)
          if prefix in name and suffix in name:
            lowercase									=suffix

           # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          lowercase									=True
          if "*" in mapped_key:
            lowercase									=name.split(lowercase_	)[0].split('''.'''	)[-2]
            lowercase									=mapped_key.replace('''*'''     ,					lowercase_	)
          if "weight_g" in name:
            lowercase									="weight_g"
          elif "weight_v" in name:
            lowercase									="weight_v"
          elif "bias" in name:
            lowercase									="bias"
          elif "weight" in name:
            lowercase									="weight"
          elif "running_mean" in name:
            lowercase									="running_mean"
          elif "running_var" in name:
            lowercase									="running_var"
          elif "num_batches_tracked" in name:
            lowercase									="num_batches_tracked"
          else:
            lowercase									=None
          set_recursively(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
        continue
    if not is_used:
      unused_weights.append(lowercase_	)

  logger.warning(f'Unused weights: {unused_weights}'	)




def 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    Optional[Any]     ,					lowercase_						:    Dict     ,					lowercase_						:    List[str]     ,					lowercase_						:    Dict	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=full_name.split('''conv_layers.'''	)[-1]
  lowercase									=name.split('''.'''	)
  lowercase									=int(items[0]	)
  lowercase									=int(items[1]	)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'	)
      lowercase									=value
      logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'	)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'	)
      lowercase									=value
      logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'	)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'	)
      lowercase									=value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'	)
      lowercase									=value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
  else:
    unused_weights.append(lowercase_	)




@torch.no_grad()
def 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    Optional[int]     ,					lowercase_						:    Union[str, Any]     ,					lowercase_						:    Tuple=None     ,					lowercase_						:    Tuple=None     ,					lowercase_						:    List[Any]=None     ,					)		->	Tuple:




  '''simple docstring'''



  if config_path is not None:
    lowercase									=SpeechTaConfig.from_pretrained(lowercase_	)
  else:
    lowercase									=SpeechTaConfig()

  if task == "s2t":
    lowercase									=config.max_text_positions
    lowercase									=SpeechTaForSpeechToText(lowercase_	)
  elif task == "t2s":
    lowercase									=1_8_7_6
    lowercase									=6_0_0
    lowercase									=config.max_speech_positions
    lowercase									=SpeechTaForTextToSpeech(lowercase_	)
  elif task == "s2s":
    lowercase									=1_8_7_6
    lowercase									=config.max_speech_positions
    lowercase									=SpeechTaForSpeechToSpeech(lowercase_	)
  else:
    raise ValueError(f'Unknown task name: {task}'	)

  if vocab_path:
    lowercase									=SpeechTaTokenizer(lowercase_     ,					model_max_length=config.max_text_positions	)

    # Mask token behaves like a normal word, i.e. include the space before it
    lowercase									=AddedToken('''<mask>'''     ,					lstrip=lowercase_     ,					rstrip=lowercase_	)
    lowercase									=mask_token
    tokenizer.add_special_tokens({'''mask_token''': mask_token}	)
    tokenizer.add_tokens(['''<ctc_blank>''']	)

  lowercase									=SpeechTaFeatureExtractor()
  lowercase									=SpeechTaProcessor(tokenizer=lowercase_     ,					feature_extractor=lowercase_	)
  processor.save_pretrained(lowercase_	)

  lowercase									=torch.load(lowercase_	)
  recursively_load_weights(fairseq_checkpoint['''model''']     ,					lowercase_     ,					lowercase_	)

  model.save_pretrained(lowercase_	)

  if repo_id:
    print('''Pushing to the hub...'''	)
    processor.push_to_hub(lowercase_	)
    model.push_to_hub(lowercase_	)


if __name__ == "__main__":
     _UpperCAmelCase  :		Dict  =		argparse.ArgumentParser()
     parser.add_argument(
         '''--task''',
         default='''s2t''',
         type=str,
         help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
     )
     parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
     parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
     parser.add_argument(
         '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
     )

     _UpperCAmelCase  :		Optional[int]  =		parser.parse_args()
     convert_speechta_checkpoint(
         args.task,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.vocab_path,
         args.push_to_hub,
     )







import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE__							:      Tuple         =    logging.get_logger(__name__)

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







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Optional[int]  		=					"""pix2struct_text_model"""
   _UpperCamelCase			:    Dict  		=					["""past_key_values"""]
   _UpperCamelCase			:    Union[str, Any]  		=					{
       """hidden_size""": """hidden_size""",
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }





   def __init__(			self       ,      snake_case=50_244       ,      snake_case=768       ,      snake_case=64       ,      snake_case=2_048       ,      snake_case=12       ,      snake_case=12       ,      snake_case=32       ,      snake_case=128       ,      snake_case=0.1       ,      snake_case=1E-6       ,      snake_case=1.0       ,      snake_case="gelu_new"       ,      snake_case=0       ,      snake_case=False       ,      snake_case=0       ,      snake_case=1       ,      snake_case=False       ,      snake_case=True       ,      **snake_case       ,      )					->	Dict:
      """simple docstring"""





      a__						:    Tuple   		=	vocab_size
      a__						:    List[str]   		=	hidden_size
      a__						:    Optional[Any]   		=	d_kv
      a__						:    List[str]   		=	d_ff
      a__						:    Optional[int]   		=	num_layers
      a__						:    Tuple   		=	num_heads
      a__						:    List[Any]   		=	relative_attention_num_buckets
      a__						:    Optional[int]   		=	relative_attention_max_distance
      a__						:    Optional[int]   		=	dropout_rate
      a__						:    List[Any]   		=	layer_norm_epsilon
      a__						:    List[str]   		=	initializer_factor
      a__						:    Any   		=	use_cache

      a__						:    Any   		=	eos_token_id
      a__						:    List[Any]   		=	decoder_start_token_id

      # for backwards compatibility
      a__						:    Tuple   		=	dense_act_fn

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





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      **snake_case   )					->	"PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(snake_case   )

      a__		,  a__						:    Optional[Any]   		=	cls.get_config_dict(snake_case       ,      **snake_case   )

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







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					"""pix2struct_vision_model"""





   def __init__(			self       ,      snake_case=768       ,      snake_case=768       ,      snake_case=2_048       ,      snake_case=64       ,      snake_case=12       ,      snake_case=12       ,      snake_case="gelu_new"       ,      snake_case=1E-6       ,      snake_case=0.0       ,      snake_case=0.0       ,      snake_case=1E-10       ,      snake_case=1.0       ,      snake_case=4_096       ,      snake_case=32       ,      snake_case=128       ,      **snake_case       ,      )					->	Optional[Any]:
      """simple docstring"""





      super().__init__(**snake_case   )

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





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      **snake_case   )					->	"PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(snake_case   )

      a__		,  a__						:    int   		=	cls.get_config_dict(snake_case       ,      **snake_case   )

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







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					"""pix2struct"""
   _UpperCamelCase			:    Dict  		=					True





   def __init__(			self       ,      snake_case=None       ,      snake_case=None       ,      snake_case=1.0       ,      snake_case=0.02       ,      snake_case=False       ,      snake_case=False       ,      snake_case=True       ,      **snake_case       ,      )					->	Optional[int]:
      """simple docstring"""





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

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

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

      a__						:    List[str]   		=	PixaStructTextConfig(**snake_case   )
      a__						:    List[str]   		=	PixaStructVisionConfig(**snake_case   )

      a__						:    Tuple   		=	self.text_config.decoder_start_token_id
      a__						:    int   		=	self.text_config.pad_token_id
      a__						:    List[Any]   		=	self.text_config.eos_token_id

      a__						:    str   		=	initializer_factor
      a__						:    Dict   		=	initializer_range

      a__						:    Optional[int]   		=	self.initializer_range
      a__						:    Union[str, Any]   		=	self.initializer_range

      a__						:    Union[str, Any]   		=	is_vqa





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      snake_case       ,      **snake_case   )					->	Dict:
      """simple docstring"""





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





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    Tuple   		=	copy.deepcopy(self.__dict__   )
      a__						:    List[str]   		=	self.text_config.to_dict()
      a__						:    Union[str, Any]   		=	self.vision_config.to_dict()
      a__						:    List[Any]   		=	self.__class__.model_type
      return output

