import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE__		:       Union[str, Any]       						=       logging.get_logger(__name__)

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

class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'pix2struct_text_model'
  lowercase_								=  ['past_key_values']
  lowercase_								=  {
      'hidden_size': 'hidden_size',
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
  }




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		vocab_size
       SCREAMING_SNAKE_CASE__					:					int      =		hidden_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		d_kv
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		d_ff
       SCREAMING_SNAKE_CASE__					:					List[str]      =		num_layers
       SCREAMING_SNAKE_CASE__					:					List[str]      =		num_heads
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		relative_attention_num_buckets
       SCREAMING_SNAKE_CASE__					:					Any      =		relative_attention_max_distance
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		dropout_rate
       SCREAMING_SNAKE_CASE__					:					List[str]      =		layer_norm_epsilon
       SCREAMING_SNAKE_CASE__					:					str      =		initializer_factor
       SCREAMING_SNAKE_CASE__					:					List[str]      =		use_cache

       SCREAMING_SNAKE_CASE__					:					Any      =		eos_token_id
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		decoder_start_token_id

       # for backwards compatibility
       SCREAMING_SNAKE_CASE__					:					str      =		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 						__lowercase(   cls  :						Tuple  ,			a_  :						Union[str, os.PathLike]  ,			**a_  :						int					)->							"PretrainedConfig":




       """simple docstring"""
       cls._set_token_in_kwargs(a_					)

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[int]      =		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__					:					str      =		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    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'pix2struct_vision_model'




  def __init__(   self  :						Optional[Any]  ,			a_  :						str=768  ,			a_  :						str=768  ,			a_  :						str=2048  ,			a_  :						Optional[Any]=64  ,			a_  :						Union[str, Any]=12  ,			a_  :						Any=12  ,			a_  :						str="gelu_new"  ,			a_  :						Optional[int]=1e-6  ,			a_  :						Union[str, Any]=0.0  ,			a_  :						Dict=0.0  ,			a_  :						Any=1e-1_0  ,			a_  :						Union[str, Any]=1.0  ,			a_  :						List[Any]=4096  ,			a_  :						Union[str, Any]=32  ,			a_  :						Tuple=128  ,			**a_  :						Optional[Any]  ,			)->							Optional[int]:




       """simple docstring"""
       super().__init__(**a_					)

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




  @classmethod
  def 						__lowercase(   cls  :						Tuple  ,			a_  :						Union[str, os.PathLike]  ,			**a_  :						Dict					)->							"PretrainedConfig":




       """simple docstring"""
       cls._set_token_in_kwargs(a_					)

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		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__					:					List[Any]      =		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    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'pix2struct'
  lowercase_								=  True




  def __init__(   self  :						str  ,			a_  :						List[Any]=None  ,			a_  :						Dict=None  ,			a_  :						int=1.0  ,			a_  :						Optional[int]=0.02  ,			a_  :						Any=False  ,			a_  :						Tuple=False  ,			a_  :						List[Any]=True  ,			**a_  :						List[str]  ,			)->							int:




       """simple docstring"""
       super().__init__(tie_word_embeddings=a_  ,			is_encoder_decoder=a_  ,			**a_					)

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

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

       SCREAMING_SNAKE_CASE__					:					int      =		PixaStructTextConfig(**a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		PixaStructVisionConfig(**a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		self.text_config.decoder_start_token_id
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.text_config.pad_token_id
       SCREAMING_SNAKE_CASE__					:					int      =		self.text_config.eos_token_id

       SCREAMING_SNAKE_CASE__					:					Dict      =		initializer_factor
       SCREAMING_SNAKE_CASE__					:					str      =		initializer_range

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

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		is_vqa




  @classmethod
  def 						__lowercase(   cls  :						Any  ,			a_  :						PixaStructTextConfig  ,			a_  :						PixaStructVisionConfig  ,			**a_  :						Any					)->							Tuple:




       """simple docstring"""
       return cls(text_config=text_config.to_dict()  ,			vision_config=vision_config.to_dict()  ,			**a_					)




  def 						__lowercase(   self  :						Optional[Any]					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		copy.deepcopy(self.__dict__					)
       SCREAMING_SNAKE_CASE__					:					Any      =		self.text_config.to_dict()
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.vision_config.to_dict()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.__class__.model_type
       return output




'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       [
      """encoder.version""",
      """decoder.version""",
      """model.encoder.version""",
      """model.decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """encoder.embed_positions._float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  lowercase_     =       list(s_dict.keys()						)
  for key in keys:
    if "transformer_layers" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)
    elif "subsample" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     ,	lowercase_     =       emb.weight.shape
  lowercase_     =       nn.Linear(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	bias=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       emb.weight.data
  return lin_layer

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)
  lowercase_     =       mam_aaa["""args"""]
  lowercase_     =       mam_aaa["""model"""]
  lowercase_     =       state_dict["""decoder.output_projection.weight"""]

  remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
  rename_keys(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  lowercase_     =       args.share_decoder_input_output_embed

  lowercase_     =       [int(SCREAMING_SNAKE_CASE_						) for i in args.conv_kernel_sizes.split(""","""						)]
  lowercase_     =       SpeechaTextConfig(
      vocab_size=SCREAMING_SNAKE_CASE_ ,	max_source_positions=args.max_source_positions ,	max_target_positions=args.max_target_positions ,	encoder_layers=args.encoder_layers ,	decoder_layers=args.decoder_layers ,	encoder_attention_heads=args.encoder_attention_heads ,	decoder_attention_heads=args.decoder_attention_heads ,	encoder_ffn_dim=args.encoder_ffn_embed_dim ,	decoder_ffn_dim=args.decoder_ffn_embed_dim ,	d_model=args.encoder_embed_dim ,	dropout=args.dropout ,	attention_dropout=args.attention_dropout ,	activation_dropout=args.activation_dropout ,	activation_function="""relu""" ,	num_conv_layers=len(SCREAMING_SNAKE_CASE_						) ,	conv_channels=args.conv_channels ,	conv_kernel_sizes=SCREAMING_SNAKE_CASE_ ,	input_feat_per_channel=args.input_feat_per_channel ,	input_channels=args.input_channels ,	tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,	num_beams=5 ,	max_length=2_00 ,	use_cache=SCREAMING_SNAKE_CASE_ ,	decoder_start_token_id=2 ,	early_stopping=SCREAMING_SNAKE_CASE_ ,	)

  lowercase_     =       SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       model.model.load_state_dict(SCREAMING_SNAKE_CASE_ ,	strict=SCREAMING_SNAKE_CASE_						)
  if len(SCREAMING_SNAKE_CASE_						) > 0 and not set(SCREAMING_SNAKE_CASE_						) <= {
      "encoder.embed_positions.weights",
      "decoder.embed_positions.weights",
  }:
    raise ValueError(
        """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
        f""" but all the following weights are missing {missing}"""						)

  if tie_embeds:
    lowercase_     =       make_linear_from_emb(model.model.decoder.embed_tokens						)
  else:
    lowercase_     =       lm_head_weights

  model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __snake_case						  =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
       parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       __snake_case						  =		parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

