

'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__         =      {
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

lowercase__         =      {
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}




class 	A_			(    SCREAMING_SNAKE_CASE_							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:     List[str]  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:     Any  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:     List[str]  		=  ["""input_ids""", """attention_mask"""]
     UpperCAmelCase_						:     Dict  		=  GPTaTokenizer


     def __init__(		self			:					List[str]					,      lowercase_			:					int=None					,      lowercase_			:					Union[str, Any]=None					,      lowercase_			:					List[str]=None					,      lowercase_			:					Union[str, Any]="<|endoftext|>"					,      lowercase_			:					Optional[int]="<|endoftext|>"					,      lowercase_			:					List[Any]="<|endoftext|>"					,      lowercase_			:					Dict=False					,      **lowercase_			:					List[str]					,      )						->     Tuple:
      super().__init__(
          __a					,      __a					,      tokenizer_file=__a					,      unk_token=__a					,      bos_token=__a					,      eos_token=__a					,      add_prefix_space=__a					,      **__a					,      )

      UpperCAmelCase	:     int						 =			kwargs.pop('add_bos_token'					,      __a	)

      UpperCAmelCase	:     Any						 =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get('add_prefix_space'					,      __a	) != add_prefix_space:
       UpperCAmelCase	:     Union[str, Any]						 =			getattr(__a					,      pre_tok_state.pop('type'	)	)
       UpperCAmelCase	:     Optional[int]						 =			add_prefix_space
       UpperCAmelCase	:     int						 =			pre_tok_class(**__a	)

      UpperCAmelCase	:     int						 =			add_prefix_space


     def        UpperCAmelCase_	(		self			:					Dict					,      *lowercase_			:					Dict					,      **lowercase_			:					int	)						->     BatchEncoding:
      UpperCAmelCase	:     List[Any]						 =			kwargs.get('is_split_into_words'					,      __a	)
      assert self.add_prefix_space or not is_split_into_words, (
          f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__a					,      **__a	)


     def        UpperCAmelCase_	(		self			:					List[Any]					,      *lowercase_			:					str					,      **lowercase_			:					Optional[int]	)						->     BatchEncoding:
      UpperCAmelCase	:     List[str]						 =			kwargs.get('is_split_into_words'					,      __a	)

      assert self.add_prefix_space or not is_split_into_words, (
          f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__a					,      **__a	)


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					str					,      lowercase_			:					Optional[str] = None	)						->     Tuple[str]:
      UpperCAmelCase	:     Dict						 =			self._tokenizer.model.save(__a					,      name=__a	)
      return tuple(__a	)




     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					"Conversation"	)						->     List[int]:
      UpperCAmelCase	:     Optional[Any]						 =			[]
      for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(__a					,      add_special_tokens=__a	) + [self.eos_token_id]	)

      if len(__a	) > self.model_max_length:
       UpperCAmelCase	:     Tuple						 =			input_ids[-self.model_max_length :]
      return input_ids







'''simple docstring'''

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
      UpperCAmelCase	:     Any						 =			inspect.getfile(accelerate.test_utils	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_script.py']	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(
          mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_distributed_data_loop.py']	)
      UpperCAmelCase	:     List[Any]						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_ops.py']	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Optional[int]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     List[Any]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     int						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(f"""Command: {cmd}"""	)
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     Optional[int]						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)




     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[Any]:
      print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1					,      cuda_visible_devices='0,1'	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


if __name__ == "__main__":
 lowercase__         =      Accelerator()
 lowercase__         =      (accelerator.state.process_index + 2, 10)
 lowercase__         =      torch.randint(0, 10, shape).to(accelerator.device)

 lowercase__         =      ""

 lowercase__         =      accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 lowercase__         =      accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 lowercase__         =      accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)




