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			:						Optional[int]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Tuple				  =		{
    """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  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """perceiver"""

  def __init__(		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Any=2_56  ,				__SCREAMING_SNAKE_CASE			:   Dict=12_80  ,				__SCREAMING_SNAKE_CASE			:   Tuple=7_68  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1  ,				__SCREAMING_SNAKE_CASE			:   Any=26  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=8  ,				__SCREAMING_SNAKE_CASE			:   str=8  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				__SCREAMING_SNAKE_CASE			:   int="kv"  ,				__SCREAMING_SNAKE_CASE			:   Any=1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1  ,				__SCREAMING_SNAKE_CASE			:   str="gelu"  ,				__SCREAMING_SNAKE_CASE			:   str=0.1  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.02  ,				__SCREAMING_SNAKE_CASE			:   List[str]=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Tuple=True  ,				__SCREAMING_SNAKE_CASE			:   str=2_62  ,				__SCREAMING_SNAKE_CASE			:   str=20_48  ,				__SCREAMING_SNAKE_CASE			:   List[str]=56  ,				__SCREAMING_SNAKE_CASE			:   Any=[3_68, 4_96]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=16  ,				__SCREAMING_SNAKE_CASE			:   str=19_20  ,				__SCREAMING_SNAKE_CASE			:   List[str]=16  ,				__SCREAMING_SNAKE_CASE			:   int=[1, 16, 2_24, 2_24]  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	num_latents
   __a							=	d_latents
   __a							=	d_model
   __a							=	num_blocks
   __a							=	num_self_attends_per_block
   __a							=	num_self_attention_heads
   __a							=	num_cross_attention_heads
   __a							=	qk_channels
   __a							=	v_channels
   __a							=	cross_attention_shape_for_attention
   __a							=	self_attention_widening_factor
   __a							=	cross_attention_widening_factor
   __a							=	hidden_act
   __a							=	attention_probs_dropout_prob
   __a							=	initializer_range
   __a							=	layer_norm_eps
   __a							=	use_query_residual
   # masked language modeling attributes
   __a							=	vocab_size
   __a							=	max_position_embeddings
   # image classification attributes
   __a							=	image_size
   # flow attributes
   __a							=	train_size
   # multimodal autoencoding attributes
   __a							=	num_frames
   __a							=	audio_samples_per_frame
   __a							=	samples_per_patch
   __a							=	output_shape
class  A_			(							a_			):

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

  @property
  def 		_UpperCAmelCase (		self			:   List[str]							):
   return 1E-4

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				__SCREAMING_SNAKE_CASE			:   Optional[TensorType] = None  ,				__SCREAMING_SNAKE_CASE			:   int = 3  ,				__SCREAMING_SNAKE_CASE			:   int = 40  ,				__SCREAMING_SNAKE_CASE			:   int = 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
    __a							=	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
    __a							=	preprocessor.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE							)
    __a							=	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
    __a							=	[" ".join(["a"]							) * seq_length] * batch_size
    __a							=	dict(preprocessor(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE							)							)
    __a							=	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
    __a							=	compute_effective_axis_dimension(__SCREAMING_SNAKE_CASE  ,				fixed_dimension=OnnxConfig.default_fixed_batch							)
    __a							=	self._generate_dummy_images(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    __a							=	dict(preprocessor(images=__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE							)							)
    __a							=	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)


