
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__lowerCamelCase							=       logging.get_logger(__name__)


__lowerCamelCase							=       R"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""






class   UpperCAmelCase		(						A_ ):



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					Union[str, Any]    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					Optional[int] )		->	bool:


          '''simple docstring'''


          raise NotImplementedError("StoppingCriteria needs to be subclassed" )






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					Union[str, Any]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int] = None )		->	Any:


          '''simple docstring'''


          snake_case       :   List[str]							=      max_length
          snake_case       :   Dict							=      max_position_embeddings



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					str    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					int )		->	bool:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      input_ids.shape[-1]
          snake_case       :   Union[str, Any]							=      cur_len >= self.max_length
          if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
                 logger.warning_once(
                     "This is a friendly reminder - the current text generation call will exceed the model's predefined "
                     f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
                     "exceptions, performance degradation, or nothing at all." )
          return is_done






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					List[Any]    ,						snake_case__		:					int    ,						snake_case__		:					int )		->	Union[str, Any]:


          '''simple docstring'''


          warnings.warn(
              "The class `MaxNewTokensCriteria` is deprecated. "
              f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
              "with `max_length = start_length + max_new_tokens` instead."    ,						snake_case__    ,						)
          snake_case       :   Union[str, Any]							=      start_length
          snake_case       :   Union[str, Any]							=      max_new_tokens
          snake_case       :   Union[str, Any]							=      start_length + max_new_tokens



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					Optional[Any]    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					Any )		->	bool:


          '''simple docstring'''


          return input_ids.shape[-1] >= self.max_length






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					List[str]    ,						snake_case__		:					float    ,						snake_case__		:					Optional[float] = None )		->	Any:


          '''simple docstring'''


          snake_case       :   Tuple							=      max_time
          snake_case       :   List[str]							=      time.time() if initial_timestamp is None else initial_timestamp



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					Dict    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					str )		->	bool:


          '''simple docstring'''


          return time.time() - self.initial_timestamp > self.max_time






class   UpperCAmelCase		(						A_ ):



   @add_start_docstrings(snake_case__ )
   def __call__(self		:					Any    ,						snake_case__		:					torch.LongTensor    ,						snake_case__		:					torch.FloatTensor    ,						**snake_case__		:					List[str] )		->	bool:


          '''simple docstring'''


          return any(criteria(snake_case__    ,						snake_case__ ) for criteria in self )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Optional[int]:


          '''simple docstring'''


          for stopping_criterium in self:
                 if isinstance(snake_case__    ,						snake_case__ ):
                        return stopping_criterium.max_length
                 elif isinstance(snake_case__    ,						snake_case__ ):
                        return stopping_criterium.max_length
          return None



def 			UpperCamelCase					(    __lowerCamelCase     : StoppingCriteriaList				,					__lowerCamelCase     : int    ):
       snake_case       :   Any							=      stopping_criteria.max_length
       snake_case       :   Tuple							=      deepcopy(__lowerCamelCase    )
       if stopping_max_length is not None and stopping_max_length != max_length:
              warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"				,					__lowerCamelCase    )
       elif stopping_max_length is None:
              new_stopping_criteria.append(MaxLengthCriteria(max_length=__lowerCamelCase    )    )
       return new_stopping_criteria









'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  BlenderbotSmallTokenizer
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   super().setUp()

   lowerCamelCase_      =  ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
   lowerCamelCase_      =  dict(zip(UpperCamelCase			,  range(len(UpperCamelCase     )     )     )     )

   lowerCamelCase_      =  ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
   lowerCamelCase_      =  {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]     )
   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["merges_file"]     )
   with open(self.vocab_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write(json.dumps(UpperCamelCase     ) + "\n"     )
   with open(self.merges_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write("\n".join(UpperCamelCase     )     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   kwargs.update(self.special_tokens_map     )
   return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname			,  **UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "adapt act apte"
   lowerCamelCase_      =  "adapt act apte"
   return input_text, output_text






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BlenderbotSmallTokenizer(self.vocab_file			,  self.merges_file			,  **self.special_tokens_map     )
   lowerCamelCase_      =  "adapt act apte"
   lowerCamelCase_      =  ["adapt", "act", "ap@@", "te"]
   lowerCamelCase_      =  tokenizer.tokenize(UpperCamelCase     )
   self.assertListEqual(UpperCamelCase			,  UpperCamelCase     )

   lowerCamelCase_      =  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

   lowerCamelCase_      =  [0, 1, 2, 3, 4, 5]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase     )			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )
   assert tok("sam"     ).input_ids == [1384]
   lowerCamelCase_      =  "I am a small frog."
   lowerCamelCase_      =  tok([src_text]			,  padding=UpperCamelCase			,  truncation=UpperCamelCase     )["input_ids"]
   lowerCamelCase_      =  tok.batch_decode(UpperCamelCase			,  skip_special_tokens=UpperCamelCase			,  clean_up_tokenization_spaces=UpperCamelCase     )[0]
   assert src_text != decoded  # I wish it did!
   assert decoded == "i am a small frog ."







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )
   lowerCamelCase_      =  "I am a small frog ."
   lowerCamelCase_      =  "."
   lowerCamelCase_      =  tok(UpperCamelCase     )["input_ids"]
   lowerCamelCase_      =  tok(UpperCamelCase     )["input_ids"]

   assert encoded[-1] == encoded_dot[0]



