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					:	Dict       =  logging.get_logger(__name__)


__lowerCamelCase					:	str       =  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 		a__						(					_lowerCAmelCase					):

					@add_start_docstrings(_A    )
					def __call__(    self      :       Union[str, Any],_A      :       torch.LongTensor,_A      :       torch.FloatTensor,**_A      :       str    ):







							"""simple docstring"""






							raise NotImplementedError("StoppingCriteria needs to be subclassed"    )







class 		a__						(					_lowerCAmelCase					):

					def __init__(    self      :       Tuple,_A      :       int,_A      :       Optional[int] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    max_length
							SCREAMING_SNAKE_CASE_  :		str				      =    max_position_embeddings






					@add_start_docstrings(_A    )
					def __call__(    self      :       Dict,_A      :       torch.LongTensor,_A      :       torch.FloatTensor,**_A      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    input_ids.shape[-1]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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 		a__						(					_lowerCAmelCase					):

					def __init__(    self      :       List[Any],_A      :       int,_A      :       int    ):







							"""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.",_A,)
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    start_length
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    max_new_tokens
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    start_length + max_new_tokens






					@add_start_docstrings(_A    )
					def __call__(    self      :       Optional[int],_A      :       torch.LongTensor,_A      :       torch.FloatTensor,**_A      :       Dict    ):







							"""simple docstring"""






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







class 		a__						(					_lowerCAmelCase					):

					def __init__(    self      :       Any,_A      :       float,_A      :       Optional[float] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    max_time
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    time.time() if initial_timestamp is None else initial_timestamp






					@add_start_docstrings(_A    )
					def __call__(    self      :       Dict,_A      :       torch.LongTensor,_A      :       torch.FloatTensor,**_A      :       str    ):







							"""simple docstring"""






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







class 		a__						(					_lowerCAmelCase					):

					@add_start_docstrings(_A    )
					def __call__(    self      :       Optional[Any],_A      :       torch.LongTensor,_A      :       torch.FloatTensor,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






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






					@property
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






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







def       _snake_case	(   lowerCAmelCase							:   StoppingCriteriaList   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    stopping_criteria.max_length
		SCREAMING_SNAKE_CASE_  :		int				      =    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 itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class       a_   (				_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			RobertaTokenizer
     __A      =			RobertaTokenizerFast
     __A      =			True
     __A      =			{"cls_token": "<s>"}





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase_			:List[Any]    			= [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        lowercase_			:List[Any]    			= dict(zip(lowercase	,      range(len(lowercase						)						)						)						)
        lowercase_			:Optional[Any]    			= ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        lowercase_			:Union[str, Any]    			= {"unk_token": "<unk>"}

        lowercase_			:Tuple    			= os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["vocab_file"]						)
        lowercase_			:Union[str, Any]    			= 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(lowercase						) + "\n"						)
        with open(self.merges_file	,      "w"	,      encoding="utf-8"						) as fp:
           fp.write("\n".join(lowercase						)						)





     def 							lowercase__      ( self		:					str	,      **lowercase		:					List[str]						):





        """simple docstring"""




        kwargs.update(self.special_tokens_map						)
        return self.tokenizer_class.from_pretrained(self.tmpdirname	,      **lowercase						)





     def 							lowercase__      ( self		:					int	,      **lowercase		:					int						):





        """simple docstring"""




        kwargs.update(self.special_tokens_map						)
        return RobertaTokenizerFast.from_pretrained(self.tmpdirname	,      **lowercase						)





     def 							lowercase__      ( self		:					Optional[int]	,      lowercase		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[str]    			= "lower newer"
        lowercase_			:Any    			= "lower newer"
        return input_text, output_text





     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        lowercase_			:List[Any]    			= self.tokenizer_class(self.vocab_file	,      self.merges_file	,      **self.special_tokens_map						)
        lowercase_			:Dict    			= "lower newer"
        lowercase_			:Dict    			= ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
        lowercase_			:int    			= tokenizer.tokenize(lowercase						)  # , add_prefix_space=True)
        self.assertListEqual(lowercase	,      lowercase						)

        lowercase_			:Optional[Any]    			= tokens + [tokenizer.unk_token]
        lowercase_			:Any    			= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase						)	,      lowercase						)





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:Dict    			= self.get_tokenizer()

        self.assertListEqual(tokenizer.encode("Hello world!"	,      add_special_tokens=lowercase						)	,      [0, 31_414, 232, 328, 2]						)
        self.assertListEqual(
            tokenizer.encode("Hello world! cécé herlolip 418"	,      add_special_tokens=lowercase						)	,      [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]	,      )





     @slow
     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= self.tokenizer_class.from_pretrained("roberta-base"						)

        lowercase_			:Any    			= tokenizer.encode("sequence builders"	,      add_special_tokens=lowercase						)
        lowercase_			:str    			= tokenizer.encode("multi-sequence build"	,      add_special_tokens=lowercase						)

        lowercase_			:int    			= tokenizer.encode(
            "sequence builders"	,      add_special_tokens=lowercase	,      add_prefix_space=lowercase						)
        lowercase_			:Optional[int]    			= tokenizer.encode(
            "sequence builders"	,      "multi-sequence build"	,      add_special_tokens=lowercase	,      add_prefix_space=lowercase						)

        lowercase_			:str    			= tokenizer.build_inputs_with_special_tokens(lowercase						)
        lowercase_			:Optional[int]    			= tokenizer.build_inputs_with_special_tokens(lowercase	,      lowercase						)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= self.get_tokenizer()

        lowercase_			:str    			= "Encode this sequence."
        lowercase_			:Tuple    			= tokenizer.byte_encoder[" ".encode("utf-8"						)[0]]

        # Testing encoder arguments
        lowercase_			:List[str]    			= tokenizer.encode(lowercase	,      add_special_tokens=lowercase	,      add_prefix_space=lowercase						)
        lowercase_			:List[Any]    			= tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
        self.assertNotEqual(lowercase	,      lowercase						)

        lowercase_			:List[str]    			= tokenizer.encode(lowercase	,      add_special_tokens=lowercase	,      add_prefix_space=lowercase						)
        lowercase_			:List[Any]    			= tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
        self.assertEqual(lowercase	,      lowercase						)

        tokenizer.add_special_tokens({"bos_token": "<s>"}						)
        lowercase_			:List[str]    			= tokenizer.encode(lowercase	,      add_special_tokens=lowercase						)
        lowercase_			:Optional[int]    			= tokenizer.convert_ids_to_tokens(encoded[1]						)[0]
        self.assertNotEqual(lowercase	,      lowercase						)

        # Testing spaces after special tokens
        lowercase_			:Union[str, Any]    			= "<mask>"
        tokenizer.add_special_tokens(
            {"mask_token": AddedToken(lowercase	,      lstrip=lowercase	,      rstrip=lowercase						)}						)  # mask token has a left space
        lowercase_			:Any    			= tokenizer.convert_tokens_to_ids(lowercase						)

        lowercase_			:Tuple    			= "Encode <mask> sequence"
        lowercase_			:int    			= "Encode <mask>sequence"

        lowercase_			:str    			= tokenizer.encode(lowercase						)
        lowercase_			:Any    			= encoded.index(lowercase						)
        lowercase_			:Dict    			= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
        self.assertEqual(lowercase	,      lowercase						)

        lowercase_			:str    			= tokenizer.encode(lowercase						)
        lowercase_			:int    			= encoded.index(lowercase						)
        lowercase_			:Optional[int]    			= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
        self.assertNotEqual(lowercase	,      lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        pass





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
              lowercase_			:List[Any]    			= self.rust_tokenizer_class.from_pretrained(lowercase	,      **lowercase						)
              lowercase_			:Dict    			= self.tokenizer_class.from_pretrained(lowercase	,      **lowercase						)
              lowercase_			:str    			= "A, <mask> AllenNLP sentence."
              lowercase_			:Tuple    			= tokenizer_r.encode_plus(lowercase	,      add_special_tokens=lowercase	,      return_token_type_ids=lowercase						)
              lowercase_			:str    			= tokenizer_p.encode_plus(lowercase	,      add_special_tokens=lowercase	,      return_token_type_ids=lowercase						)

              # token_type_ids should put 0 everywhere
              self.assertEqual(sum(tokens_r["token_type_ids"]						)	,      sum(tokens_p["token_type_ids"]						)						)

              # attention_mask should put 1 everywhere, so sum over length should be 1
              self.assertEqual(
                  sum(tokens_r["attention_mask"]						) / len(tokens_r["attention_mask"]						)	,      sum(tokens_p["attention_mask"]						) / len(tokens_p["attention_mask"]						)	,      )

              lowercase_			:Any    			= tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]						)
              lowercase_			:Union[str, Any]    			= tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]						)

              # Rust correctly handles the space before the mask while python doesnt
              self.assertSequenceEqual(tokens_p["input_ids"]	,      [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)
              self.assertSequenceEqual(tokens_r["input_ids"]	,      [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)

              self.assertSequenceEqual(
                  lowercase	,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]						)
              self.assertSequenceEqual(
                  lowercase	,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]						)





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        for trim_offsets, add_prefix_space in itertools.product([True, False]	,      repeat=2						):
           lowercase_			:List[Any]    			= self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname	,      use_fast=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase						)

           lowercase_			:int    			= json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()						)
           lowercase_			:Any    			= json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()						)

           self.assertEqual(pre_tokenizer_state["add_prefix_space"]	,      lowercase						)

           self.assertEqual(post_processor_state["add_prefix_space"]	,      lowercase						)
           self.assertEqual(post_processor_state["trim_offsets"]	,      lowercase						)







     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
              lowercase_			:Tuple    			= "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
              lowercase_			:Optional[Any]    			= F'{text_of_1_token} {text_of_1_token}'

              lowercase_			:int    			= self.rust_tokenizer_class.from_pretrained(
                  lowercase	,      use_fast=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase						)
              lowercase_			:Optional[int]    			= tokenizer_r(lowercase	,      return_offsets_mapping=lowercase	,      add_special_tokens=lowercase						)
              self.assertEqual(encoding.offset_mapping[0]	,      (0, len(lowercase						))						)
              self.assertEqual(
                  encoding.offset_mapping[1]	,      (len(lowercase						) + 1, len(lowercase						) + 1 + len(lowercase						))	,      )

              lowercase_			:Dict    			= self.rust_tokenizer_class.from_pretrained(
                  lowercase	,      use_fast=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase						)
              lowercase_			:List[Any]    			= tokenizer_r(lowercase	,      return_offsets_mapping=lowercase	,      add_special_tokens=lowercase						)
              self.assertEqual(encoding.offset_mapping[0]	,      (0, len(lowercase						))						)
              self.assertEqual(
                  encoding.offset_mapping[1]	,      (len(lowercase						) + 1, len(lowercase						) + 1 + len(lowercase						))	,      )

              lowercase_			:Any    			= self.rust_tokenizer_class.from_pretrained(
                  lowercase	,      use_fast=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase						)
              lowercase_			:Optional[Any]    			= tokenizer_r(lowercase	,      return_offsets_mapping=lowercase	,      add_special_tokens=lowercase						)
              self.assertEqual(encoding.offset_mapping[0]	,      (0, len(lowercase						))						)
              self.assertEqual(
                  encoding.offset_mapping[1]	,      (len(lowercase						), len(lowercase						) + 1 + len(lowercase						))	,      )

              lowercase_			:List[str]    			= self.rust_tokenizer_class.from_pretrained(
                  lowercase	,      use_fast=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase						)
              lowercase_			:List[Any]    			= tokenizer_r(lowercase	,      return_offsets_mapping=lowercase	,      add_special_tokens=lowercase						)
              self.assertEqual(encoding.offset_mapping[0]	,      (0, len(lowercase						))						)
              self.assertEqual(
                  encoding.offset_mapping[1]	,      (len(lowercase						), len(lowercase						) + 1 + len(lowercase						))	,      )

              lowercase_			:Dict    			= F' {text}'

              # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
              #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
              # )
              # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
              # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
              # self.assertEqual(
              #     encoding.offset_mapping[1],
              #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
              # )

              lowercase_			:Optional[Any]    			= self.rust_tokenizer_class.from_pretrained(
                  lowercase	,      use_fast=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase						)
              lowercase_			:Optional[Any]    			= tokenizer_r(lowercase	,      return_offsets_mapping=lowercase	,      add_special_tokens=lowercase						)
              self.assertEqual(encoding.offset_mapping[0]	,      (1, 1 + len(lowercase						))						)
              self.assertEqual(
                  encoding.offset_mapping[1]	,      (1 + len(lowercase						) + 1, 1 + len(lowercase						) + 1 + len(lowercase						))	,      )

              lowercase_			:Union[str, Any]    			= self.rust_tokenizer_class.from_pretrained(
                  lowercase	,      use_fast=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase						)
              lowercase_			:Dict    			= tokenizer_r(lowercase	,      return_offsets_mapping=lowercase	,      add_special_tokens=lowercase						)
              self.assertEqual(encoding.offset_mapping[0]	,      (0, 1 + len(lowercase						))						)
              self.assertEqual(
                  encoding.offset_mapping[1]	,      (1 + len(lowercase						), 1 + len(lowercase						) + 1 + len(lowercase						))	,      )

              lowercase_			:Optional[int]    			= self.rust_tokenizer_class.from_pretrained(
                  lowercase	,      use_fast=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase						)
              lowercase_			:Dict    			= tokenizer_r(lowercase	,      return_offsets_mapping=lowercase	,      add_special_tokens=lowercase						)
              self.assertEqual(encoding.offset_mapping[0]	,      (0, 1 + len(lowercase						))						)
              self.assertEqual(
                  encoding.offset_mapping[1]	,      (1 + len(lowercase						), 1 + len(lowercase						) + 1 + len(lowercase						))	,      )


