


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers import AutoModelForCausalLM







@require_torch
class     __lowercase    (				unittest.TestCase   ):



		"""simple docstring"""

		def  __A					(      self     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     )
					lowerCamelCase  						=   AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     ).to(UpperCAmelCase__     )
					lowerCamelCase  						=   -1

					lowerCamelCase  						=   ids_tensor((1, 5)						,	vocab_size=model.config.vocab_size     ).to(UpperCAmelCase__     )
					lowerCamelCase  						=   model.generate(UpperCAmelCase__						,	max_new_tokens=10						,	do_sample=UpperCAmelCase__     )
					lowerCamelCase  						=   tokenizer.decode(greedy_ids[0]     )

					with CaptureStdout() as cs:
								lowerCamelCase  						=   TextStreamer(UpperCAmelCase__     )
								model.generate(UpperCAmelCase__						,	max_new_tokens=10						,	do_sample=UpperCAmelCase__						,	streamer=UpperCAmelCase__     )
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					lowerCamelCase  						=   cs.out[:-1]

					self.assertEqual(UpperCAmelCase__						,	UpperCAmelCase__     )







		def  __A					(      self     )   ->      Any:







					'''simple docstring'''







					lowerCamelCase  						=   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     )
					lowerCamelCase  						=   AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     ).to(UpperCAmelCase__     )
					lowerCamelCase  						=   -1

					lowerCamelCase  						=   ids_tensor((1, 5)						,	vocab_size=model.config.vocab_size     ).to(UpperCAmelCase__     )
					lowerCamelCase  						=   model.generate(UpperCAmelCase__						,	max_new_tokens=10						,	do_sample=UpperCAmelCase__     )
					lowerCamelCase  						=   tokenizer.decode(greedy_ids[0]     )

					lowerCamelCase  						=   TextIteratorStreamer(UpperCAmelCase__     )
					lowerCamelCase  						=   {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
					lowerCamelCase  						=   Thread(target=model.generate						,	kwargs=UpperCAmelCase__     )
					thread.start()
					lowerCamelCase  						=   """"""
					for new_text in streamer:
								streamer_text += new_text

					self.assertEqual(UpperCAmelCase__						,	UpperCAmelCase__     )







		def  __A					(      self     )   ->      str:







					'''simple docstring'''







					lowerCamelCase  						=   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     )
					lowerCamelCase  						=   AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     ).to(UpperCAmelCase__     )
					lowerCamelCase  						=   -1

					lowerCamelCase  						=   ids_tensor((1, 5)						,	vocab_size=model.config.vocab_size     ).to(UpperCAmelCase__     )
					lowerCamelCase  						=   model.generate(UpperCAmelCase__						,	max_new_tokens=10						,	do_sample=UpperCAmelCase__     )
					lowerCamelCase  						=   greedy_ids[:, input_ids.shape[1] :]
					lowerCamelCase  						=   tokenizer.decode(new_greedy_ids[0]     )

					with CaptureStdout() as cs:
								lowerCamelCase  						=   TextStreamer(UpperCAmelCase__						,	skip_prompt=UpperCAmelCase__     )
								model.generate(UpperCAmelCase__						,	max_new_tokens=10						,	do_sample=UpperCAmelCase__						,	streamer=UpperCAmelCase__     )
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					lowerCamelCase  						=   cs.out[:-1]

					self.assertEqual(UpperCAmelCase__						,	UpperCAmelCase__     )







		def  __A					(      self     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   AutoTokenizer.from_pretrained("""distilgpt2"""     )
					lowerCamelCase  						=   AutoModelForCausalLM.from_pretrained("""distilgpt2"""     ).to(UpperCAmelCase__     )
					lowerCamelCase  						=   -1

					lowerCamelCase  						=   torch.ones((1, 5)						,	device=UpperCAmelCase__     ).long() * model.config.bos_token_id
					with CaptureStdout() as cs:
								lowerCamelCase  						=   TextStreamer(UpperCAmelCase__						,	skip_special_tokens=UpperCAmelCase__     )
								model.generate(UpperCAmelCase__						,	max_new_tokens=1						,	do_sample=UpperCAmelCase__						,	streamer=UpperCAmelCase__     )

					# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
					# re-tokenized, must only contain one token
					lowerCamelCase  						=   cs.out[:-1]  # Remove the final "\n"
					lowerCamelCase  						=   tokenizer(UpperCAmelCase__						,	return_tensors="""pt"""     )
					self.assertEqual(streamer_text_tokenized.input_ids.shape						,	(1, 1)     )







		def  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     )
					lowerCamelCase  						=   AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     ).to(UpperCAmelCase__     )
					lowerCamelCase  						=   -1

					lowerCamelCase  						=   ids_tensor((1, 5)						,	vocab_size=model.config.vocab_size     ).to(UpperCAmelCase__     )
					lowerCamelCase  						=   TextIteratorStreamer(UpperCAmelCase__						,	timeout=0.001     )
					lowerCamelCase  						=   {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
					lowerCamelCase  						=   Thread(target=model.generate						,	kwargs=UpperCAmelCase__     )
					thread.start()

					# The streamer will timeout after 0.001 seconds, so an exception will be raised
					with self.assertRaises(UpperCAmelCase__     ):
								lowerCamelCase  						=   """"""
								for new_text in streamer:
											streamer_text += new_text





'''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

__snake_case    						=logging.get_logger(__name__)


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

__snake_case    						={
    """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"""
    },
}

__snake_case    						={"""facebook/blenderbot-3B""": 128}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	List[Any]		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	str		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     ['''input_ids''', '''attention_mask''']
     lowerCamelCase				:	List[Any]		=     BlenderbotTokenizer





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

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

        lowerCAmelCase     =			add_prefix_space

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

           lowerCAmelCase     =			False

           if state.get('add_prefix_space'    ,      UpperCAmelCase__    ) != add_prefix_space:
              lowerCAmelCase     =			add_prefix_space
              lowerCAmelCase     =			True

           if state.get('trim_offsets'    ,      UpperCAmelCase__    ) != trim_offsets:
              lowerCAmelCase     =			trim_offsets
              lowerCAmelCase     =			True

           if changes_to_apply:
              lowerCAmelCase     =			getattr(UpperCAmelCase__    ,      state.pop('type'    )    )
              lowerCAmelCase     =			component_class(**UpperCAmelCase__    )
              setattr(self.backend_tokenizer    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )





     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def 							__UpperCAmelCase      ( self       :      Union[str, 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 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Tuple:
        lowerCAmelCase     =			AddedToken(UpperCAmelCase__    ,      lstrip=UpperCAmelCase__    ,      rstrip=UpperCAmelCase__    ) if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) else value
        lowerCAmelCase     =			value





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      *UpperCAmelCase__       :      Union[str, Any]    ,      **UpperCAmelCase__       :      List[str]    )				->  BatchEncoding:
        lowerCAmelCase     =			kwargs.get('is_split_into_words'    ,      UpperCAmelCase__    )
        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(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      *UpperCAmelCase__       :      str    ,      **UpperCAmelCase__       :      List[str]    )				->  BatchEncoding:
        lowerCAmelCase     =			kwargs.get('is_split_into_words'    ,      UpperCAmelCase__    )

        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(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Optional[str] = None    )				->  Tuple[str]:
        lowerCAmelCase     =			self._tokenizer.model.save(UpperCAmelCase__    ,      name=UpperCAmelCase__    )
        return tuple(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[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 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  Any:
        return token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      "Conversation"    )				->  List[int]:
        lowerCAmelCase     =			[]
        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(UpperCAmelCase__    )

        lowerCAmelCase     =			'  '.join(UpperCAmelCase__    )
        lowerCAmelCase     =			self.encode(UpperCAmelCase__    )
        if len(UpperCAmelCase__    ) > self.model_max_length:
           lowerCAmelCase     =			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





