





"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


lowerCAmelCase:	Dict							      =logging.get_logger(__name__)

lowerCAmelCase:	Tuple							      ={
    "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""m2m_100"""
   __UpperCAmelCase        =					["""past_key_values"""]
   __UpperCAmelCase        =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



   def __init__(       self      ,   snake_case=1_2_8_1_1_2      ,   snake_case=1_0_2_4      ,   snake_case=1_2      ,   snake_case=4_0_9_6      ,   snake_case=1_6      ,   snake_case=1_2      ,   snake_case=4_0_9_6      ,   snake_case=1_6      ,   snake_case=0.05      ,   snake_case=0.05      ,   snake_case=True      ,   snake_case=True      ,   snake_case="relu"      ,   snake_case=1_0_2_4      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=0.0      ,   snake_case=0.02      ,   snake_case=2      ,   snake_case=True      ,   snake_case=1      ,   snake_case=0      ,   snake_case=2      ,   **snake_case      ,   ) ->    str:







      """simple docstring"""
      lowercase    :			Optional[int]  =       vocab_size
      lowercase    :			int  =       max_position_embeddings
      lowercase    :			str  =       d_model
      lowercase    :			Union[str, Any]  =       encoder_ffn_dim
      lowercase    :			int  =       encoder_layers
      lowercase    :			Union[str, Any]  =       encoder_attention_heads
      lowercase    :			int  =       decoder_ffn_dim
      lowercase    :			Dict  =       decoder_layers
      lowercase    :			Tuple  =       decoder_attention_heads
      lowercase    :			Optional[int]  =       dropout
      lowercase    :			Optional[int]  =       attention_dropout
      lowercase    :			List[Any]  =       activation_dropout
      lowercase    :			Dict  =       activation_function
      lowercase    :			Union[str, Any]  =       init_std
      lowercase    :			Optional[Any]  =       encoder_layerdrop
      lowercase    :			Tuple  =       decoder_layerdrop
      lowercase    :			List[str]  =       use_cache
      lowercase    :			Tuple  =       encoder_layers
      lowercase    :			Union[str, Any]  =       scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=snake_case      ,   bos_token_id=snake_case      ,   eos_token_id=snake_case      ,   is_encoder_decoder=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )




class lowerCamelCase__  (    __UpperCamelCase				):



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







      """simple docstring"""
      lowercase    :			Dict  =       OrderedDict(
          [
              ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
              ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
          ]	)

      if self.use_past:
         lowercase    :			List[str]  =       {0: """batch"""}
         lowercase    :			str  =       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
         lowercase    :			List[Any]  =       {0: """batch""", 1: """decoder_sequence"""}
         lowercase    :			Optional[Any]  =       {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
         self.fill_with_past_key_values_(snake_case      ,   direction="""inputs"""	)
      return common_inputs



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case = -1      ,   snake_case = -1      ,   snake_case = False      ,   snake_case = None      ,   ) ->    Mapping[str, Any]:







      """simple docstring"""
      # Copied from OnnxConfig.generate_dummy_inputs
      # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      lowercase    :			List[str]  =       compute_effective_axis_dimension(
          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
      lowercase    :			Optional[Any]  =       tokenizer.num_special_tokens_to_add(snake_case	)
      lowercase    :			Optional[int]  =       compute_effective_axis_dimension(
          snake_case      ,   fixed_dimension=OnnxConfig.default_fixed_sequence      ,   num_token_to_add=snake_case	)

      # Generate dummy inputs according to compute batch and sequence
      lowercase    :			Dict  =       [""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
      lowercase    :			Tuple  =       dict(tokenizer(snake_case      ,   return_tensors=snake_case	)	)
      return common_inputs



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case = -1      ,   snake_case = -1      ,   snake_case = False      ,   snake_case = None      ,   ) ->    Mapping[str, Any]:







      """simple docstring"""
      lowercase    :			Optional[Any]  =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case	)

      # Generate decoder inputs
      lowercase    :			Optional[Any]  =       seq_length if not self.use_past else 1
      lowercase    :			List[Any]  =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case	)
      lowercase    :			str  =       {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      lowercase    :			Tuple  =       dict(**snake_case      ,   **snake_case	)

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

         lowercase    :			Any  =       torch.cat(
             [common_inputs["""decoder_attention_mask"""], torch.ones(snake_case      ,   snake_case	)]      ,   dim=1	)

         lowercase    :			Union[str, Any]  =       []
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         lowercase  ,					lowercase    :			Any  =       self.num_layers
         lowercase    :			Any  =       min(snake_case      ,   snake_case	)
         lowercase    :			Optional[Any]  =       max(snake_case      ,   snake_case	) - min_num_layers
         lowercase    :			Union[str, Any]  =       """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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





   __UpperCAmelCase        =					_generate_dummy_inputs_for_default_and_seqaseq_lm









"""simple docstring"""
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       BertGenerationDecoder(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       __A ,__A ,model_class="""bert""" ,is_encoder_named_decoder=__A ,is_encoder=__A ,)

   # Save pytorch-model
   print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
   model.save_pretrained(__A    )


if __name__ == "__main__":
   lowerCAmelCase:	Dict							      =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--is_encoder_named_decoder",
       action="store_true",
       help="If decoder has to be renamed to encoder in PyTorch model.",
   )
   parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
   parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model")
   lowerCAmelCase:	Optional[int]							      =parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )


