import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def  _lowerCamelCase   (				lowerCamelCase_ :							List[Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				[
         'decoder.version',
         'decoder.output_projection.weight',
         '_float_tensor',
         'decoder.embed_positions._float_tensor',
     ]
     for k in ignore_keys:
          state_dict.pop(lowerCamelCase_     ,					lowerCamelCase_       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Union[str, Any]       =				emb.weight.shape
     UpperCAmelCase_							:       Dict       =				nn.Linear(lowerCamelCase_     ,					lowerCamelCase_     ,					bias=lowerCamelCase_       )
     UpperCAmelCase_							:       Any       =				emb.weight.data
     return lin_layer


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



     """simple docstring"""





     UpperCAmelCase_							:       List[Any]       =				torch.load(lowerCamelCase_     ,					map_location='cpu'       )
     UpperCAmelCase_							:       List[str]       =				Namespace(**checkpoint['cfg']['model']       )
     UpperCAmelCase_							:       int       =				checkpoint['model']
     remove_ignore_keys_(lowerCamelCase_       )
     UpperCAmelCase_							:       Optional[int]       =				state_dict['decoder.embed_tokens.weight'].shape[0]

     UpperCAmelCase_							:       List[Any]       =				{key.replace('decoder'     ,					'model'       ): val for key, val in state_dict.items()}

     UpperCAmelCase_							:       str       =				XGLMConfig(
         vocab_size=lowerCamelCase_     ,					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     ,					)

     UpperCAmelCase_							:       Dict       =				XGLMForCausalLM(lowerCamelCase_       )
     UpperCAmelCase_							:       int       =				model.load_state_dict(lowerCamelCase_     ,					strict=lowerCamelCase_       )
     print(lowerCamelCase_       )
     UpperCAmelCase_							:       int       =				make_linear_from_emb(model.model.embed_tokens       )

     return model


if __name__ == "__main__":
 snake_case__					:      int				      =  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.''')
 snake_case__					:      int				      =  parser.parse_args()
 snake_case__					:      str				      =  convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
 model.save_pretrained(args.pytorch_dump_folder_path)







'''simple docstring'''


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		['''image_processor''', '''tokenizer''']
  lowerCamelCase_		:Optional[Any]						   =		'''ViTImageProcessor'''
  lowerCamelCase_		:int						   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				None
       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'				,		snake_case_				,		)
            UpperCAmelCase_							:       int       =				kwargs.pop('feature_extractor'						)

       UpperCAmelCase_							:       str       =				image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError('You need to specify an `image_processor`.'						)
       if tokenizer is None:
            raise ValueError('You need to specify a `tokenizer`.'						)

       super().__init__(snake_case_				,		snake_case_						)


  def __call__(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       if text is None and visual_prompt is None and images is None:
            raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
            raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
            UpperCAmelCase_							:       Optional[int]       =				self.tokenizer(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None:
            UpperCAmelCase_							:       Optional[Any]       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if images is not None:
            UpperCAmelCase_							:       int       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None and images is not None:
            UpperCAmelCase_							:       Tuple       =				{
                'pixel_values': image_features.pixel_values,
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       elif text is not None and images is not None:
            UpperCAmelCase_							:       Optional[int]       =				image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       elif visual_prompt is not None:
            UpperCAmelCase_							:       Dict       =				{
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       else:
            return BatchEncoding(data=dict(**snake_case_						)				,		tensor_type=snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.batch_decode(*snake_case_				,		**snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.decode(*snake_case_				,		**snake_case_						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,		snake_case_				,		)
       return self.image_processor_class






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,		snake_case_				,		)
       return self.image_processor






