
'''simple docstring'''





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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__		: Dict  							=							logging.get_logger(__name__)

a__		: Optional[int]  							=							{
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     Union[str, Any]    					=       'mbart'
 UpperCAmelCase__     :     Union[str, Any]    					=       ['past_key_values']
 UpperCAmelCase__     :     Any    					=       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






 def __init__(      self	:Any						,					_A	:Optional[int]=50_265						,					_A	:Optional[int]=1_024						,					_A	:List[str]=12						,					_A	:int=4_096						,					_A	:int=16						,					_A	:List[Any]=12						,					_A	:Optional[Any]=4_096						,					_A	:List[str]=16						,					_A	:List[Any]=0.0						,					_A	:str=0.0						,					_A	:Tuple=True						,					_A	:int=True						,					_A	:Union[str, Any]="gelu"						,					_A	:str=1_024						,					_A	:Dict=0.1						,					_A	:List[str]=0.0						,					_A	:Dict=0.0						,					_A	:List[Any]=0.02						,					_A	:Union[str, Any]=0.0						,					_A	:int=False						,					_A	:Any=1						,					_A	:List[Any]=0						,					_A	:Dict=2						,					_A	:List[str]=2						,					**_A	:str						,					)					->      str:







       '''simple docstring'''



       __A													=      vocab_size
       __A													=      max_position_embeddings
       __A													=      d_model
       __A													=      encoder_ffn_dim
       __A													=      encoder_layers
       __A													=      encoder_attention_heads
       __A													=      decoder_ffn_dim
       __A													=      decoder_layers
       __A													=      decoder_attention_heads
       __A													=      dropout
       __A													=      attention_dropout
       __A													=      activation_dropout
       __A													=      activation_function
       __A													=      init_std
       __A													=      encoder_layerdrop
       __A													=      decoder_layerdrop
       __A													=      classifier_dropout
       __A													=      use_cache
       __A													=      encoder_layers
       __A													=      scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=_A						,					bos_token_id=_A						,					eos_token_id=_A						,					is_encoder_decoder=_A						,					forced_eos_token_id=_A						,					**_A						,					)


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 @property
 def 						lowercase_    (      self	:int      )					->      Mapping[str, Mapping[int, str]]:







       '''simple docstring'''



       if self.task in ["default", "seq2seq-lm"]:
             __A													=      OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]      )

             if self.use_past:
                   __A													=      {0: 'batch'}
                   __A													=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
             else:
                   __A													=      {0: 'batch', 1: 'decoder_sequence'}
                   __A													=      {0: 'batch', 1: 'decoder_sequence'}

             if self.use_past:
                   self.fill_with_past_key_values_(_A						,					direction='inputs'      )
       elif self.task == "causal-lm":
             # TODO: figure this case out.
             __A													=      OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]      )
             if self.use_past:
                   __A						,   __A													=      self.num_layers
                   for i in range(_A      ):
                         __A													=      {0: 'batch', 2: 'past_sequence + sequence'}
                         __A													=      {0: 'batch', 2: 'past_sequence + sequence'}
       else:
             __A													=      OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                     ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                 ]      )

       return common_inputs






 @property
 def 						lowercase_    (      self	:Tuple      )					->      Mapping[str, Mapping[int, str]]:







       '''simple docstring'''



       if self.task in ["default", "seq2seq-lm"]:
             __A													=      super().outputs
       else:
             __A													=      super(_A						,					self      ).outputs
             if self.use_past:
                   __A						,   __A													=      self.num_layers
                   for i in range(_A      ):
                         __A													=      {0: 'batch', 2: 'past_sequence + sequence'}
                         __A													=      {0: 'batch', 2: 'past_sequence + sequence'}
       return common_outputs






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:PreTrainedTokenizer						,					_A	:int = -1						,					_A	:int = -1						,					_A	:bool = False						,					_A	:Optional[TensorType] = None						,					)					->      Mapping[str, Any]:







       '''simple docstring'''



       __A													=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _A						,					_A						,					_A						,					_A						,					_A      )

       # Generate decoder inputs
       __A													=      seq_length if not self.use_past else 1
       __A													=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _A						,					_A						,					_A						,					_A						,					_A      )
       __A													=      {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
       __A													=      dict(**_A						,					**_A      )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
             else:
                   import torch
             __A						,   __A													=      common_inputs['input_ids'].shape
             __A													=      common_inputs['decoder_input_ids'].shape[1]
             __A						,   __A													=      self.num_attention_heads
             __A													=      (
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             __A													=      decoder_seq_length + 3
             __A													=      (
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             __A													=      torch.cat(
                 [common_inputs['decoder_attention_mask'], torch.ones(_A						,					_A      )]						,					dim=1      )

             __A													=      []
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             __A						,   __A													=      self.num_layers
             __A													=      min(_A						,					_A      )
             __A													=      max(_A						,					_A      ) - min_num_layers
             __A													=      'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

             for _ in range(_A      ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(_A      ),
                           torch.zeros(_A      ),
                           torch.zeros(_A      ),
                           torch.zeros(_A      ),
                       )      )
             # TODO: test this.
             __A													=      encoder_shape if remaining_side_name == 'encoder' else decoder_shape
             for _ in range(_A						,					_A      ):
                   common_inputs["past_key_values"].append((torch.zeros(_A      ), torch.zeros(_A      ))      )
       return common_inputs






 def 						lowercase_    (      self	:Optional[int]						,					_A	:PreTrainedTokenizer						,					_A	:int = -1						,					_A	:int = -1						,					_A	:bool = False						,					_A	:Optional[TensorType] = None						,					)					->      Mapping[str, Any]:







       '''simple docstring'''



       __A													=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _A						,					_A						,					_A						,					_A						,					_A      )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
             else:
                   import torch
             __A						,   __A													=      common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             __A													=      seqlen + 2
             __A						,   __A													=      self.num_layers
             __A						,   __A													=      self.num_attention_heads
             __A													=      (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             __A													=      common_inputs['attention_mask'].dtype
             __A													=      torch.cat(
                 [common_inputs['attention_mask'], torch.ones(_A						,					_A						,					dtype=_A      )]						,					dim=1      )
             __A													=      [
                 (torch.zeros(_A      ), torch.zeros(_A      )) for _ in range(_A      )
             ]
       return common_inputs






 def 						lowercase_    (      self	:Tuple						,					_A	:PreTrainedTokenizer						,					_A	:int = -1						,					_A	:int = -1						,					_A	:bool = False						,					_A	:Optional[TensorType] = None						,					)					->      Mapping[str, Any]:







       '''simple docstring'''



       __A													=      compute_effective_axis_dimension(
           _A						,					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													=      tokenizer.num_special_tokens_to_add(_A      )
       __A													=      compute_effective_axis_dimension(
           _A						,					fixed_dimension=OnnxConfig.default_fixed_sequence						,					num_token_to_add=_A      )

       # Generate dummy inputs according to compute batch and sequence
       __A													=      [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
       __A													=      dict(tokenizer(_A						,					return_tensors=_A      )      )
       return common_inputs






 def 						lowercase_    (      self	:str						,					_A	:PreTrainedTokenizer						,					_A	:int = -1						,					_A	:int = -1						,					_A	:bool = False						,					_A	:Optional[TensorType] = None						,					)					->      Mapping[str, Any]:







       '''simple docstring'''



       if self.task in ["default", "seq2seq-lm"]:
             __A													=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _A						,					batch_size=_A						,					seq_length=_A						,					is_pair=_A						,					framework=_A      )

       elif self.task == "causal-lm":
             __A													=      self._generate_dummy_inputs_for_causal_lm(
                 _A						,					batch_size=_A						,					seq_length=_A						,					is_pair=_A						,					framework=_A      )
       else:
             __A													=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _A						,					batch_size=_A						,					seq_length=_A						,					is_pair=_A						,					framework=_A      )

       return common_inputs






 def 						lowercase_    (      self	:Tuple						,					_A	:Any						,					_A	:List[str]						,					_A	:Optional[int]						,					_A	:Optional[Any]      )					->      Optional[Any]:







       '''simple docstring'''



       if self.task in ["default", "seq2seq-lm"]:
             __A													=      super()._flatten_past_key_values_(_A						,					_A						,					_A						,					_A      )
       else:
             __A													=      super(_A						,					self      )._flatten_past_key_values_(
                 _A						,					_A						,					_A						,					_A      )








'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      torch.save(model.state_dict()   ,	UpperCAmelCase   ,	_use_new_zipfile_serialization=UpperCAmelCase						)


if __name__ == "__main__":
 a__		: Tuple  							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






