


'''simple docstring'''



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def    lowerCamelCase__   ( A     :    Any   ,     A     :    List[Any]  ):




						'''simple docstring'''






						UpperCAmelCase         =			torch.load(A   ,     map_location='''cpu'''  )

						UpperCAmelCase         =			chkpt['''model''']

						# We have the base model one level deeper than the original XLM repository
						UpperCAmelCase         =			{}
						for k, v in state_dict.items():
												if "pred_layer" in k:
																		UpperCAmelCase         =			v
												else:
																		UpperCAmelCase         =			v

						UpperCAmelCase         =			chkpt['''params''']
						UpperCAmelCase         =			{n: v for n, v in config.items() if not isinstance(A   ,     (torch.FloatTensor, numpy.ndarray)  )}

						UpperCAmelCase         =			chkpt['''dico_word2id''']
						UpperCAmelCase         =			{s + '''</w>''' if s.find('''@@'''  ) == -1 and i > 13 else s.replace('''@@'''   ,     ''''''  ): i for s, i in vocab.items()}

						# Save pytorch-model
						UpperCAmelCase         =			pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
						UpperCAmelCase         =			pytorch_dump_folder_path + '''/''' + CONFIG_NAME
						UpperCAmelCase         =			pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

						print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""  )
						torch.save(A   ,     A  )

						print(f"""Save configuration file to {pytorch_config_dump_path}"""  )
						with open(A   ,     '''w'''   ,     encoding='''utf-8'''  ) as f:
												f.write(json.dumps(A   ,     indent=2  ) + '''\n'''  )

						print(f"""Save vocab file to {pytorch_config_dump_path}"""  )
						with open(A   ,     '''w'''   ,     encoding='''utf-8'''  ) as f:
												f.write(json.dumps(A   ,     indent=2  ) + '''\n'''  )


if __name__ == "__main__":
				_lowercase    :    Any			     =  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				_lowercase    :    Optional[int]			     =  parser.parse_args()
				convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''



import warnings

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






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




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

										UpperCAmelCase         =			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__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""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         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None and images is not None:
																UpperCAmelCase         =			{
																    '''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         =			image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase         =			{
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




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




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




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




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




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





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




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





