
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE						       =						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						       =						{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			_lowercase      =							'perceiver'







			def __init__(	self    ,							__UpperCAmelCase=256    ,							__UpperCAmelCase=1_280    ,							__UpperCAmelCase=768    ,							__UpperCAmelCase=1    ,							__UpperCAmelCase=26    ,							__UpperCAmelCase=8    ,							__UpperCAmelCase=8    ,							__UpperCAmelCase=None    ,							__UpperCAmelCase=None    ,							__UpperCAmelCase="kv"    ,							__UpperCAmelCase=1    ,							__UpperCAmelCase=1    ,							__UpperCAmelCase="gelu"    ,							__UpperCAmelCase=0.1    ,							__UpperCAmelCase=0.02    ,							__UpperCAmelCase=1E-12    ,							__UpperCAmelCase=True    ,							__UpperCAmelCase=262    ,							__UpperCAmelCase=2_048    ,							__UpperCAmelCase=56    ,							__UpperCAmelCase=[368, 496]    ,							__UpperCAmelCase=16    ,							__UpperCAmelCase=1_920    ,							__UpperCAmelCase=16    ,							__UpperCAmelCase=[1, 16, 224, 224]    ,							**__UpperCAmelCase    ,							):
										super().__init__(**__SCREAMING_SNAKE_CASE  )

										SCREAMING_SNAKE_CASE_	:							List[str]											=num_latents
										SCREAMING_SNAKE_CASE_	:							int											=d_latents
										SCREAMING_SNAKE_CASE_	:							str											=d_model
										SCREAMING_SNAKE_CASE_	:							List[Any]											=num_blocks
										SCREAMING_SNAKE_CASE_	:							Tuple											=num_self_attends_per_block
										SCREAMING_SNAKE_CASE_	:							Optional[int]											=num_self_attention_heads
										SCREAMING_SNAKE_CASE_	:							str											=num_cross_attention_heads
										SCREAMING_SNAKE_CASE_	:							Any											=qk_channels
										SCREAMING_SNAKE_CASE_	:							Tuple											=v_channels
										SCREAMING_SNAKE_CASE_	:							str											=cross_attention_shape_for_attention
										SCREAMING_SNAKE_CASE_	:							List[str]											=self_attention_widening_factor
										SCREAMING_SNAKE_CASE_	:							Optional[int]											=cross_attention_widening_factor
										SCREAMING_SNAKE_CASE_	:							List[str]											=hidden_act
										SCREAMING_SNAKE_CASE_	:							Dict											=attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_	:							List[str]											=initializer_range
										SCREAMING_SNAKE_CASE_	:							Optional[int]											=layer_norm_eps
										SCREAMING_SNAKE_CASE_	:							Optional[int]											=use_query_residual
										# masked language modeling attributes
										SCREAMING_SNAKE_CASE_	:							List[Any]											=vocab_size
										SCREAMING_SNAKE_CASE_	:							Dict											=max_position_embeddings
										# image classification attributes
										SCREAMING_SNAKE_CASE_	:							Dict											=image_size
										# flow attributes
										SCREAMING_SNAKE_CASE_	:							Any											=train_size
										# multimodal autoencoding attributes
										SCREAMING_SNAKE_CASE_	:							List[str]											=num_frames
										SCREAMING_SNAKE_CASE_	:							str											=audio_samples_per_frame
										SCREAMING_SNAKE_CASE_	:							List[str]											=samples_per_patch
										SCREAMING_SNAKE_CASE_	:							List[str]											=output_shape





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			@property
			def      __lowerCamelCase    (	self  ):
										if self.task == "multiple-choice":
																	SCREAMING_SNAKE_CASE_	:							Optional[Any]											={0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																	SCREAMING_SNAKE_CASE_	:							List[Any]											={0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('inputs', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										    ]  )







			@property
			def      __lowerCamelCase    (	self  ):
										return 1E-4







			def      __lowerCamelCase    (	self    ,							__UpperCAmelCase    ,							__UpperCAmelCase = -1    ,							__UpperCAmelCase = -1    ,							__UpperCAmelCase = -1    ,							__UpperCAmelCase = False    ,							__UpperCAmelCase = None    ,							__UpperCAmelCase = 3    ,							__UpperCAmelCase = 40    ,							__UpperCAmelCase = 40    ,							):
										# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

										if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE  ):
																	# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																	SCREAMING_SNAKE_CASE_	:							List[Any]											=compute_effective_axis_dimension(
																	    __SCREAMING_SNAKE_CASE    ,							fixed_dimension=OnnxConfig.default_fixed_batch    ,							num_token_to_add=0  )
																	# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																	SCREAMING_SNAKE_CASE_	:							int											=preprocessor.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE  )
																	SCREAMING_SNAKE_CASE_	:							List[Any]											=compute_effective_axis_dimension(
																	    __SCREAMING_SNAKE_CASE    ,							fixed_dimension=OnnxConfig.default_fixed_sequence    ,							num_token_to_add=__SCREAMING_SNAKE_CASE  )
																	# Generate dummy inputs according to compute batch and sequence
																	SCREAMING_SNAKE_CASE_	:							List[Any]											=[' '.join(['a']  ) * seq_length] * batch_size
																	SCREAMING_SNAKE_CASE_	:							List[str]											=dict(preprocessor(__SCREAMING_SNAKE_CASE    ,							return_tensors=__SCREAMING_SNAKE_CASE  )  )
																	SCREAMING_SNAKE_CASE_	:							Optional[int]											=inputs.pop('input_ids'  )
																	return inputs
										elif isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE  ) and preprocessor.model_input_names[0] == "pixel_values":
																	# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																	SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=compute_effective_axis_dimension(__SCREAMING_SNAKE_CASE    ,							fixed_dimension=OnnxConfig.default_fixed_batch  )
																	SCREAMING_SNAKE_CASE_	:							str											=self._generate_dummy_images(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE  )
																	SCREAMING_SNAKE_CASE_	:							Any											=dict(preprocessor(images=__SCREAMING_SNAKE_CASE    ,							return_tensors=__SCREAMING_SNAKE_CASE  )  )
																	SCREAMING_SNAKE_CASE_	:							Dict											=inputs.pop('pixel_values'  )
																	return inputs
										else:
																	raise ValueError(
																	    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'  )






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 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )

 __a							=	chkpt["model"]

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

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

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

 # Save pytorch-model
 __a							=	pytorch_dump_folder_path + "/" + WEIGHTS_NAME
 __a							=	pytorch_dump_folder_path + "/" + CONFIG_NAME
 __a							=	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__":
  SCREAMING_SNAKE_CASE			:						Union[str, 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."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


