"""simple docstring"""



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
       import tensorflow as tf

if is_keras_nlp_available():
       from transformers.models.gpta import TFGPTaTokenizer


__lowercase		:		Any														=							["gpt2"]
__lowercase		:		int														=							"gpt2"

if is_tf_available():



       class      _A	(						tf.Module					):




           """simple docstring"""




           def __init__(		self							:     List[str]	,      A_							:     Any    )    ->       Tuple:
                super().__init__()
                __snake_case				=							tokenizer
                __snake_case				=							AutoConfig.from_pretrained(A_    )
                __snake_case				=							TFGPTaLMHeadModel.from_config(A_    )







           @tf.function(input_signature=(tf.TensorSpec((None,)	,      tf.string	,      name='''text'''    ),)    )
           def  lowercase					(		self							:     Dict	,      A_							:     List[Any]    )    ->       Any:
                __snake_case				=							self.tokenizer(A_    )
                __snake_case				=							tokenized['''input_ids'''].to_tensor()

                __snake_case				=							tf.cast(input_ids_dense > 0	,      tf.intaa    )
                # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

                __snake_case				=							self.model(input_ids=A_	,      attention_mask=A_    )['''logits''']

                return outputs



@require_tf
@require_keras_nlp
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[int]    )    ->       Union[str, Any]:
         super().setUp()

         __snake_case				=							[GPTaTokenizer.from_pretrained(A_    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         __snake_case				=							[TFGPTaTokenizer.from_pretrained(A_    ) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

         __snake_case				=							[
             '''This is a straightforward English test sentence.''',
             '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
             '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
             '''And some much more rare Chinese: 齉 堃 齉堃''',
             '''Je vais aussi écrire en français pour tester les accents''',
             '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
         ]
         __snake_case				=							list(zip(self.test_sentences	,      self.test_sentences[::-1]    )    )
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[int]:
         for tokenizer, tf_tokenizer in zip(self.tokenizers	,      self.tf_tokenizers    ):
              for test_inputs in self.test_sentences:
                   __snake_case				=							tokenizer([test_inputs]	,      return_tensors='''tf'''    )
                   __snake_case				=							tf_tokenizer([test_inputs]    )

                   for key in python_outputs.keys():
                        # convert them to numpy to avoid messing with ragged tensors
                        __snake_case				=							python_outputs[key].numpy()
                        __snake_case				=							tf_outputs[key].numpy()

                        self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
                        self.assertTrue(tf.reduce_all(tf.cast(A_	,      tf.intaa    ) == tf_outputs_values    )    )
    @slow
    def  lowercase					(		self							:     Dict    )    ->       int:
         for tf_tokenizer in self.tf_tokenizers:
              __snake_case				=							tf.function(A_    )
              for test_inputs in self.test_sentences:
                   __snake_case				=							tf.constant(A_    )
                   __snake_case				=							compiled_tokenizer(A_    )
                   __snake_case				=							tf_tokenizer(A_    )

                   for key in eager_outputs.keys():
                        self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )
    @slow
    def  lowercase					(		self							:     Tuple    )    ->       Dict:
         for tf_tokenizer in self.tf_tokenizers:
              __snake_case				=							ModelToSave(tokenizer=A_    )
              __snake_case				=							tf.convert_to_tensor([self.test_sentences[0]]    )
              __snake_case				=							model.serving(A_    )  # Build model with some sample inputs
              with TemporaryDirectory() as tempdir:
                   __snake_case				=							Path(A_    ) / '''saved.model'''
                   tf.saved_model.save(A_	,      A_	,      signatures={'''serving_default''': model.serving}    )
                   __snake_case				=							tf.saved_model.load(A_    )
              __snake_case				=							loaded_model.signatures['''serving_default'''](A_    )['''output_0''']
              # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
              self.assertTrue(tf.reduce_all(out == loaded_output    )    )
    @slow
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[int]:
         for tf_tokenizer in self.tf_tokenizers:
              __snake_case				=							tf.convert_to_tensor([self.test_sentences[0]]    )
              __snake_case				=							tf_tokenizer(A_    )  # Build model with some sample inputs

              __snake_case				=							tf_tokenizer.get_config()
              __snake_case				=							TFGPTaTokenizer.from_config(A_    )
              __snake_case				=							model_from_config(A_    )

              for key in from_config_output.keys():
                   self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )







    @slow
    def  lowercase					(		self							:     Tuple    )    ->       int:
         for tf_tokenizer in self.tf_tokenizers:
              # for the test to run
              __snake_case				=							123_123

              for max_length in [3, 5, 1_024]:
                   __snake_case				=							tf.convert_to_tensor([self.test_sentences[0]]    )
                   __snake_case				=							tf_tokenizer(A_	,      max_length=A_    )

                   __snake_case				=							out['''input_ids'''].numpy().shape[1]

                   assert out_length == max_length


"""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

__lowercase		:		int														=							logging.get_logger(__name__)


__lowercase		:		Tuple														=							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowercase		:		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"
    },
}

__lowercase		:		List[str]														=							{"facebook/blenderbot-3B": 128}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[Any]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					int							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Tuple							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase_	:					int							=					['''input_ids''', '''attention_mask''']
    UpperCamelCase_	:					str							=					BlenderbotTokenizer
    def __init__(		self							:     Union[str, Any]	,      A_							:     Any=None	,      A_							:     Optional[int]=None	,      A_							:     Optional[Any]=None	,      A_							:     Union[str, Any]="replace"	,      A_							:     Union[str, Any]="<s>"	,      A_							:     Union[str, Any]="</s>"	,      A_							:     Optional[int]="</s>"	,      A_							:     List[Any]="<s>"	,      A_							:     Union[str, Any]="<unk>"	,      A_							:     Any="<pad>"	,      A_							:     List[str]="<mask>"	,      A_							:     int=False	,      A_							:     Tuple=True	,      **A_							:     Optional[Any]	,      )    ->       int:
         super().__init__(
             A_	,      A_	,      tokenizer_file=A_	,      errors=A_	,      bos_token=A_	,      eos_token=A_	,      sep_token=A_	,      cls_token=A_	,      unk_token=A_	,      pad_token=A_	,      mask_token=A_	,      add_prefix_space=A_	,      trim_offsets=A_	,      **A_	,      )

         __snake_case				=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('''add_prefix_space'''	,      A_    ) != add_prefix_space:
              __snake_case				=							getattr(A_	,      pre_tok_state.pop('''type'''    )    )
              __snake_case				=							add_prefix_space
              __snake_case				=							pre_tok_class(**A_    )

         __snake_case				=							add_prefix_space

         __snake_case				=							'''post_processor'''
         __snake_case				=							getattr(self.backend_tokenizer	,      A_	,      A_    )
         if tokenizer_component_instance:
              __snake_case				=							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:
                   __snake_case				=							tuple(state['''sep''']    )
              if "cls" in state:
                   __snake_case				=							tuple(state['''cls''']    )

              __snake_case				=							False

              if state.get('''add_prefix_space'''	,      A_    ) != add_prefix_space:
                   __snake_case				=							add_prefix_space
                   __snake_case				=							True

              if state.get('''trim_offsets'''	,      A_    ) != trim_offsets:
                   __snake_case				=							trim_offsets
                   __snake_case				=							True

              if changes_to_apply:
                   __snake_case				=							getattr(A_	,      state.pop('''type'''    )    )
                   __snake_case				=							component_class(**A_    )
                   setattr(self.backend_tokenizer	,      A_	,      A_    )
    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         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  lowercase					(		self							:     int	,      A_							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else value
         __snake_case				=							value
    def  lowercase					(		self							:     Any	,      *A_							:     List[str]	,      **A_							:     Optional[int]    )    ->       BatchEncoding:
         __snake_case				=							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  lowercase					(		self							:     str	,      *A_							:     Tuple	,      **A_							:     str    )    ->       BatchEncoding:
         __snake_case				=							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  lowercase					(		self							:     str	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         __snake_case				=							self._tokenizer.model.save(A_	,      name=A_    )
         return tuple(A_    )
    def  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[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  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       Any:
         return token_ids_a + [self.eos_token_id]







    def  lowercase					(		self							:     Optional[Any]	,      A_							:     "Conversation"    )    ->       List[int]:
         __snake_case				=							[]
         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(A_    )

         __snake_case				=							'''  '''.join(A_    )
         __snake_case				=							self.encode(A_    )
         if len(A_    ) > self.model_max_length:
              __snake_case				=							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