

'''simple docstring'''







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   [
           """decoder.version""",
           """decoder.output_projection.weight""",
           """_float_tensor""",
           """decoder.embed_positions._float_tensor""",
       ]
       for k in ignore_keys:
              state_dict.pop(UpperCAmelCase__,							UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      str:
       A_       ,      A_						       =   emb.weight.shape
       A_						       =   nn.Linear(UpperCAmelCase__,							UpperCAmelCase__,							bias=UpperCAmelCase__					)
       A_						       =   emb.weight.data
       return lin_layer
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   torch.load(UpperCAmelCase__,							map_location="""cpu"""					)
       A_						       =   Namespace(**checkpoint["""cfg"""]["""model"""]					)
       A_						       =   checkpoint["""model"""]
       remove_ignore_keys_(UpperCAmelCase__					)
       A_						       =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

       A_						       =   {key.replace("""decoder""",							"""model"""					): val for key, val in state_dict.items()}

       A_						       =   XGLMConfig(
           vocab_size=UpperCAmelCase__,							max_position_embeddings=args.max_target_positions,							num_layers=args.decoder_layers,							attention_heads=args.decoder_attention_heads,							ffn_dim=args.decoder_ffn_embed_dim,							d_model=args.decoder_embed_dim,							layerdrop=args.decoder_layerdrop,							dropout=args.dropout,							attention_dropout=args.attention_dropout,							activation_dropout=args.activation_dropout,							activation_function="""gelu""",							scale_embedding=not args.no_scale_embedding,							tie_word_embeddings=args.share_decoder_input_output_embed,							)

       A_						       =   XGLMForCausalLM(UpperCAmelCase__					)
       A_						       =   model.load_state_dict(UpperCAmelCase__,							strict=UpperCAmelCase__					)
       print(UpperCAmelCase__					)
       A_						       =   make_linear_from_emb(model.model.embed_tokens					)

       return model


if __name__ == "__main__":
      __lowerCamelCase									=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
      parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      __lowerCamelCase									=     parser.parse_args()
      __lowerCamelCase									=     convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
      model.save_pretrained(args.pytorch_dump_folder_path)



'''simple docstring'''







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        A__   (   _snake_case      ):
    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		,					UpperCamelCase__=50244		,					UpperCamelCase__=768		,					UpperCamelCase__=64		,					UpperCamelCase__=2048		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					UpperCamelCase__=0.1		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=1.0		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=0		,					UpperCamelCase__=False		,					UpperCamelCase__=0		,					UpperCamelCase__=1		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   d_kv
           A_						       =   d_ff
           A_						       =   num_layers
           A_						       =   num_heads
           A_						       =   relative_attention_num_buckets
           A_						       =   relative_attention_max_distance
           A_						       =   dropout_rate
           A_						       =   layer_norm_epsilon
           A_						       =   initializer_factor
           A_						       =   use_cache

           A_						       =   eos_token_id
           A_						       =   decoder_start_token_id

           # for backwards compatibility
           A_						       =   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        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__							)

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

           # get the text config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""							) == "pix2struct":
                  A_						       =   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        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct_vision_model"



    def __init__(       self		,					UpperCamelCase__=768		,					UpperCamelCase__=768		,					UpperCamelCase__=2048		,					UpperCamelCase__=64		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=0.0		,					UpperCamelCase__=0.0		,					UpperCamelCase__=1e-1_0		,					UpperCamelCase__=1.0		,					UpperCamelCase__=4096		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					**UpperCamelCase__		,					)    ->   int:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__							)

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







    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__							)

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

           # get the vision config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""							) == "pix2struct":
                  A_						       =   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        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct"
    lowercase						       =		True



    def __init__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=1.0		,					UpperCamelCase__=0.02		,					UpperCamelCase__=False		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   str:





           '''simple docstring'''
           super().__init__(tie_word_embeddings=UpperCamelCase__		,					is_encoder_decoder=UpperCamelCase__		,					**UpperCamelCase__							)

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

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

           A_						       =   PixaStructTextConfig(**UpperCamelCase__							)
           A_						       =   PixaStructVisionConfig(**UpperCamelCase__							)

           A_						       =   self.text_config.decoder_start_token_id
           A_						       =   self.text_config.pad_token_id
           A_						       =   self.text_config.eos_token_id

           A_						       =   initializer_factor
           A_						       =   initializer_range

           A_						       =   self.initializer_range
           A_						       =   self.initializer_range

           A_						       =   is_vqa



    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   Optional[int]:





           '''simple docstring'''
           return cls(text_config=text_config.to_dict()		,					vision_config=vision_config.to_dict()		,					**UpperCamelCase__							)







    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   copy.deepcopy(self.__dict__							)
           A_						       =   self.text_config.to_dict()
           A_						       =   self.vision_config.to_dict()
           A_						       =   self.__class__.model_type
           return output
