
"""simple docstring"""

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

UpperCamelCase			:       List[str]									=				logging.get_logger(__name__)


UpperCamelCase			:       Tuple									=				{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}






def 							A		(       snake_case   :Any       ,      snake_case   :Optional[Any]       ,      snake_case   :Tuple       ,      snake_case   :Union[str, Any]	)   ->			Optional[Any]:
 if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
  raise ValueError(f'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()	)}.'	)

 if tokenizer_name is None:
  __UpperCamelCase       						= TOKENIZER_CLASSES
 else:
  __UpperCamelCase       						= {tokenizer_name: getattr(snake_case       ,      tokenizer_name + 'Fast'	)}

 logger.info(f'Loading tokenizer classes: {tokenizer_names}'	)

 for tokenizer_name in tokenizer_names:
  __UpperCamelCase       						= TOKENIZER_CLASSES[tokenizer_name]

  __UpperCamelCase       						= True
  if checkpoint_name is None:
   __UpperCamelCase       						= list(tokenizer_class.max_model_input_sizes.keys()	)
  else:
   __UpperCamelCase       						= [checkpoint_name]

  logger.info(f'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'	)

  for checkpoint in checkpoint_names:
   logger.info(f'Loading {tokenizer_class.__class__.__name__} {checkpoint}'	)

   # Load tokenizer
   __UpperCamelCase       						= tokenizer_class.from_pretrained(snake_case       ,      force_download=snake_case	)

   # Save fast tokenizer
   logger.info(f'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'	)

   # For organization names we create sub-directories
   if "/" in checkpoint:
    __UpperCamelCase						,			__UpperCamelCase       						= checkpoint.split('/'	)
    __UpperCamelCase       						= os.path.join(snake_case       ,      snake_case	)
   elif add_prefix:
    __UpperCamelCase       						= checkpoint
    __UpperCamelCase       						= dump_path
   else:
    __UpperCamelCase       						= None
    __UpperCamelCase       						= dump_path

   logger.info(f'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'	)

   if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()	)[0]:
    __UpperCamelCase       						= list(tokenizer.pretrained_vocab_files_map.values()	)[0][checkpoint]
    __UpperCamelCase       						= file_path.split(snake_case	)[-1][0]
    if next_char == "/":
     __UpperCamelCase       						= os.path.join(snake_case       ,      snake_case	)
     __UpperCamelCase       						= None

    logger.info(f'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'	)

   __UpperCamelCase       						= tokenizer.save_pretrained(
       snake_case       ,      legacy_format=snake_case       ,      filename_prefix=snake_case	)
   logger.info(f'=> File names {file_names}'	)

   for file_name in file_names:
    if not file_name.endswith('tokenizer.json'	):
     os.remove(snake_case	)
     logger.info(f'=> removing {file_name}'	)


if __name__ == "__main__":
 UpperCamelCase			:       Any									=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
 )
 parser.add_argument(
     "--tokenizer_name",
     default=None,
     type=str,
     help=(
         f'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
         "download and convert all the checkpoints from AWS."
     ),
 )
 parser.add_argument(
     "--checkpoint_name",
     default=None,
     type=str,
     help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
 )
 parser.add_argument(
     "--force_download",
     action="store_true",
     help="Re-download checkpoints.",
 )
 UpperCamelCase			:       Optional[int]									=				parser.parse_args()

 convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)





"""simple docstring"""

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


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

UpperCamelCase			:       int									=				{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"gptj"
     lowercase		     =		{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }





     def __init__(			self			,  __UpperCAmelCase=5_0400			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=4096			,  __UpperCAmelCase=28			,  __UpperCAmelCase=16			,  __UpperCAmelCase=64			,  __UpperCAmelCase=None			,  __UpperCAmelCase="gelu_new"			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=True			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=False			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= n_positions
      __UpperCamelCase       						= n_embd
      __UpperCamelCase       						= n_layer
      __UpperCamelCase       						= n_head
      __UpperCamelCase       						= n_inner
      __UpperCamelCase       						= rotary_dim
      __UpperCamelCase       						= activation_function
      __UpperCamelCase       						= resid_pdrop
      __UpperCamelCase       						= embd_pdrop
      __UpperCamelCase       						= attn_pdrop
      __UpperCamelCase       						= layer_norm_epsilon
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= use_cache

      __UpperCamelCase       						= bos_token_id
      __UpperCamelCase       						= eos_token_id

      super().__init__(
          bos_token_id=__UpperCAmelCase			,  eos_token_id=__UpperCAmelCase			,  tie_word_embeddings=__UpperCAmelCase			,  **__UpperCAmelCase   )


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = "default"			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False			,  ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase			,  task=__UpperCAmelCase			,  patching_specs=__UpperCAmelCase			,  use_past=__UpperCAmelCase   )
      if not getattr(self._config			,  'pad_token_id'			,  __UpperCAmelCase   ):
       # TODO: how to do that better?
       __UpperCamelCase       						= 0





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
      if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase			,  direction='inputs'   )
       __UpperCamelCase       						= {0: 'batch', 1: 'past_sequence + sequence'}
      else:
       __UpperCamelCase       						= {0: 'batch', 1: 'sequence'}

      return common_inputs





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_layer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_head





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = False			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= super(__UpperCAmelCase			,  self   ).generate_dummy_inputs(
          __UpperCAmelCase			,  batch_size=__UpperCAmelCase			,  seq_length=__UpperCAmelCase			,  is_pair=__UpperCAmelCase			,  framework=__UpperCAmelCase   )

      # We need to order the input in the way they appears in the forward()
      __UpperCamelCase       						= OrderedDict({'input_ids': common_inputs['input_ids']}   )

      # Need to add the past_keys
      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       						= common_inputs['input_ids'].shape
        # Not using the same length for past_key_values
        __UpperCamelCase       						= seqlen + 2
        __UpperCamelCase       						= (
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        __UpperCamelCase       						= [
            (torch.zeros(__UpperCAmelCase   ), torch.zeros(__UpperCAmelCase   )) for _ in range(self.num_layers   )
        ]

      __UpperCamelCase       						= common_inputs['attention_mask']
      if self.use_past:
       __UpperCamelCase       						= ordered_inputs['attention_mask'].dtype
       __UpperCamelCase       						= torch.cat(
           [ordered_inputs['attention_mask'], torch.ones(__UpperCAmelCase			,  __UpperCAmelCase			,  dtype=__UpperCAmelCase   )]			,  dim=1   )

      return ordered_inputs







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 13



