


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


__UpperCAmelCase      			=  logging.get_logger(__name__)

__UpperCAmelCase      			=  {
    """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 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					'''m2m_100'''
      snake_case__       =					['''past_key_values''']
      snake_case__       =					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




      def __init__(		self   ,			__SCREAMING_SNAKE_CASE=12_81_12   ,			__SCREAMING_SNAKE_CASE=10_24   ,			__SCREAMING_SNAKE_CASE=12   ,			__SCREAMING_SNAKE_CASE=40_96   ,			__SCREAMING_SNAKE_CASE=16   ,			__SCREAMING_SNAKE_CASE=12   ,			__SCREAMING_SNAKE_CASE=40_96   ,			__SCREAMING_SNAKE_CASE=16   ,			__SCREAMING_SNAKE_CASE=0.05   ,			__SCREAMING_SNAKE_CASE=0.05   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE="relu"   ,			__SCREAMING_SNAKE_CASE=10_24   ,			__SCREAMING_SNAKE_CASE=0.1   ,			__SCREAMING_SNAKE_CASE=0.1   ,			__SCREAMING_SNAKE_CASE=0.0   ,			__SCREAMING_SNAKE_CASE=0.02   ,			__SCREAMING_SNAKE_CASE=2   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=1   ,			__SCREAMING_SNAKE_CASE=0   ,			__SCREAMING_SNAKE_CASE=2   ,			**__SCREAMING_SNAKE_CASE   ,			)					->						List[str]:







         """simple docstring"""







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

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




class 							lowercase__(						snake_case__			):






      '''simple docstring'''







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







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ])

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

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




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







         """simple docstring"""







         UpperCamelCase__ :			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
         UpperCamelCase__ :			Any 						=tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Dict 						=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
         UpperCamelCase__ :			List[str] 						=[" ".join([tokenizer.unk_token]) * seq_length] * batch_size
         UpperCamelCase__ :			int 						=dict(tokenizer(__SCREAMING_SNAKE_CASE   ,			return_tensors=__SCREAMING_SNAKE_CASE))
         return common_inputs




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







         """simple docstring"""







         UpperCamelCase__ :			Any 						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)

         # Generate decoder inputs
         UpperCamelCase__ :			str 						=seq_length if not self.use_past else 1
         UpperCamelCase__ :			Union[str, Any] 						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			str 						={F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         UpperCamelCase__ :			List[Any] 						=dict(**__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_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
            UpperCamelCase__						,	UpperCamelCase__ :			List[str] 						=common_inputs["input_ids"].shape
            UpperCamelCase__ :			Dict 						=common_inputs["decoder_input_ids"].shape[1]
            UpperCamelCase__						,	UpperCamelCase__ :			List[Any] 						=self.num_attention_heads
            UpperCamelCase__ :			Any 						=(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            UpperCamelCase__ :			List[Any] 						=decoder_seq_length + 3
            UpperCamelCase__ :			int 						=(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            UpperCamelCase__ :			Union[str, Any] 						=torch.cat(
                [common_inputs["decoder_attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)]   ,			dim=1)

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

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





from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






   for _ in tqdm(range(0							,  args.n_examples							,  A_      )      ):
      yield [next(A_      )[args.text_column] for _ in range(A_      )]


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)

