


"""simple docstring"""




import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__magic_name__						: List[Any]    =				[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
					import run_clm_flax
					import run_flax_glue
					import run_flax_ner
					import run_mlm_flax
					import run_qa
					import run_summarization_flax
					import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

__magic_name__						: List[str]    =				logging.getLogger()





def       a_ (	):
			__lowerCamelCase          =      argparse.ArgumentParser()
			parser.add_argument("""-f"""     )
			__lowerCamelCase          =      parser.parse_args()
			return args.f





def       a_ (	lowercase__ :Any,			lowercase__ :List[Any]="eval"     ):
			__lowerCamelCase          =      os.path.join(UpperCamelCase__,			f'{split}_results.json'     )
			if os.path.exists(UpperCamelCase__     ):
						with open(UpperCamelCase__,			"""r"""     ) as f:
									return json.load(UpperCamelCase__     )
			raise ValueError(f'can\'t find {path}'     )


__magic_name__						: List[Any]    =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 			__snake_case   (snake_case_  ):




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      self.get_auto_remove_tmp_dir()
										__lowerCamelCase          =      f'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

										with patch.object(__lowercase     ,							"""argv"""     ,							__lowercase   ):
													run_flax_glue.main()
													__lowerCamelCase          =      get_results(__lowercase   )
													self.assertGreaterEqual(result["""eval_accuracy"""]     ,							0.75   )




							@slow
							def   __a   (     self:    str   ):
										__lowerCamelCase          =      self.get_auto_remove_tmp_dir()
										__lowerCamelCase          =      f'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

										with patch.object(__lowercase     ,							"""argv"""     ,							__lowercase   ):
													run_clm_flax.main()
													__lowerCamelCase          =      get_results(__lowercase   )
													self.assertLess(result["""eval_perplexity"""]     ,							1_00   )




							@slow
							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      self.get_auto_remove_tmp_dir()
										__lowerCamelCase          =      f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

										with patch.object(__lowercase     ,							"""argv"""     ,							__lowercase   ):
													run_summarization_flax.main()
													__lowerCamelCase          =      get_results(__lowercase     ,							split="""test"""   )
													self.assertGreaterEqual(result["""test_rouge1"""]     ,							10   )
													self.assertGreaterEqual(result["""test_rouge2"""]     ,							2   )
													self.assertGreaterEqual(result["""test_rougeL"""]     ,							7   )
													self.assertGreaterEqual(result["""test_rougeLsum"""]     ,							7   )




							@slow
							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      self.get_auto_remove_tmp_dir()
										__lowerCamelCase          =      f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

										with patch.object(__lowercase     ,							"""argv"""     ,							__lowercase   ):
													run_mlm_flax.main()
													__lowerCamelCase          =      get_results(__lowercase   )
													self.assertLess(result["""eval_perplexity"""]     ,							42   )




							@slow
							def   __a   (     self:    str   ):
										__lowerCamelCase          =      self.get_auto_remove_tmp_dir()
										__lowerCamelCase          =      f'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

										with patch.object(__lowercase     ,							"""argv"""     ,							__lowercase   ):
													run_ta_mlm_flax.main()
													__lowerCamelCase          =      get_results(__lowercase   )
													self.assertGreaterEqual(result["""eval_accuracy"""]     ,							0.42   )




							@slow
							def   __a   (     self:    int   ):
										__lowerCamelCase          =      7 if get_gpu_count() > 1 else 2

										__lowerCamelCase          =      self.get_auto_remove_tmp_dir()
										__lowerCamelCase          =      f'\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

										with patch.object(__lowercase     ,							"""argv"""     ,							__lowercase   ):
													run_flax_ner.main()
													__lowerCamelCase          =      get_results(__lowercase   )
													self.assertGreaterEqual(result["""eval_accuracy"""]     ,							0.75   )
													self.assertGreaterEqual(result["""eval_f1"""]     ,							0.3   )




							@slow
							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      self.get_auto_remove_tmp_dir()
										__lowerCamelCase          =      f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

										with patch.object(__lowercase     ,							"""argv"""     ,							__lowercase   ):
													run_qa.main()
													__lowerCamelCase          =      get_results(__lowercase   )
													self.assertGreaterEqual(result["""eval_f1"""]     ,							30   )
													self.assertGreaterEqual(result["""eval_exact"""]     ,							30   )








'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

UpperCAmelCase_			:  List[Any]										=   logging.get_logger(__name__)


UpperCAmelCase_			:  Union[str, Any]										=   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_			:  List[Any]										=   {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

UpperCAmelCase_			:  str										=   {'facebook/blenderbot-3B': 1_2_8}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    VOCAB_FILES_NAMES
 __lowercase					:		List[Any]     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		Optional[Any]     				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase					:		Tuple     				=    ['''input_ids''', '''attention_mask''']
 __lowercase					:		str     				=    BlenderbotTokenizer


 def __init__(   self   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase="replace"   ,   __lowercase="<s>"   ,   __lowercase="</s>"   ,   __lowercase="</s>"   ,   __lowercase="<s>"   ,   __lowercase="<unk>"   ,   __lowercase="<pad>"   ,   __lowercase="<mask>"   ,   __lowercase=False   ,   __lowercase=True   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(
      __lowercase   ,   __lowercase   ,   tokenizer_file=__lowercase   ,   errors=__lowercase   ,   bos_token=__lowercase   ,   eos_token=__lowercase   ,   sep_token=__lowercase   ,   cls_token=__lowercase   ,   unk_token=__lowercase   ,   pad_token=__lowercase   ,   mask_token=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase   ,   **__lowercase   ,   )

  __A	:		str	  =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'   ,   __lowercase    ) != add_prefix_space:
   __A	:		List[Any]	  =      getattr(__lowercase   ,   pre_tok_state.pop('type'    )    )
   __A	:		Any	  =      add_prefix_space
   __A	:		Any	  =      pre_tok_class(**__lowercase    )

  __A	:		Union[str, Any]	  =      add_prefix_space

  __A	:		List[Any]	  =      'post_processor'
  __A	:		Optional[Any]	  =      getattr(self.backend_tokenizer   ,   __lowercase   ,   __lowercase    )
  if tokenizer_component_instance:
   __A	:		Optional[int]	  =      json.loads(tokenizer_component_instance.__getstate__()    )

   # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
   if "sep" in state:
    __A	:		Union[str, Any]	  =      tuple(state['sep']    )
   if "cls" in state:
    __A	:		int	  =      tuple(state['cls']    )

   __A	:		Any	  =      False

   if state.get('add_prefix_space'   ,   __lowercase    ) != add_prefix_space:
    __A	:		Any	  =      add_prefix_space
    __A	:		int	  =      True

   if state.get('trim_offsets'   ,   __lowercase    ) != trim_offsets:
    __A	:		List[Any]	  =      trim_offsets
    __A	:		List[str]	  =      True

   if changes_to_apply:
    __A	:		Optional[int]	  =      getattr(__lowercase   ,   state.pop('type'    )    )
    __A	:		Optional[int]	  =      component_class(**__lowercase    )
    setattr(self.backend_tokenizer   ,   __lowercase   ,   __lowercase    )


 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 			snake_case__ (   self    ):

  """simple docstring"""







  if self._mask_token is None:
   if self.verbose:
    logger.error('Using mask_token, but it is not set yet.'    )
   return None
  return str(self._mask_token    )


 @mask_token.setter
 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      AddedToken(__lowercase   ,   lstrip=__lowercase   ,   rstrip=__lowercase    ) if isinstance(__lowercase   ,   __lowercase    ) else value
  __A	:		List[str]	  =      value


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      kwargs.get('is_split_into_words'   ,   __lowercase    )
  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(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      kwargs.get('is_split_into_words'   ,   __lowercase    )

  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(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		str	  =      self._tokenizer.model.save(__lowercase   ,   name=__lowercase    )
  return tuple(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		str	  =      [self.sep_token_id]
  __A	:		Any	  =      [self.cls_token_id]

  if token_ids_a is None:
   return len(cls + token_ids_a + sep    ) * [0]
  return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  return token_ids_a + [self.eos_token_id]







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      []
  for is_user, text in conversation.iter_texts():
   if is_user:
    # We need to space prefix as it's being done within blenderbot
    inputs.append(' ' + text    )
   else:
    # Generated responses should contain them already.
    inputs.append(__lowercase    )

  __A	:		Tuple	  =      '  '.join(__lowercase    )
  __A	:		List[Any]	  =      self.encode(__lowercase    )
  if len(__lowercase    ) > self.model_max_length:
   __A	:		Any	  =      input_ids[-self.model_max_length :]
   logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""    )
  return input_ids






