





'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.roberta.modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
  )


class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=7		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=99		,      _UpperCAmelCase=32		,      _UpperCAmelCase=5		,      _UpperCAmelCase=4		,      _UpperCAmelCase=37		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=512		,      _UpperCAmelCase=16		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=4		,      ):

          '''simple docstring'''






          __A		:					Optional[int]						=			parent
          __A		:					List[str]						=			batch_size
          __A		:					Tuple						=			seq_length
          __A		:					Optional[Any]						=			is_training
          __A		:					str						=			use_attention_mask
          __A		:					Dict						=			use_token_type_ids
          __A		:					int						=			use_labels
          __A		:					Any						=			vocab_size
          __A		:					List[str]						=			hidden_size
          __A		:					str						=			num_hidden_layers
          __A		:					Optional[Any]						=			num_attention_heads
          __A		:					Any						=			intermediate_size
          __A		:					Dict						=			hidden_act
          __A		:					Any						=			hidden_dropout_prob
          __A		:					Optional[int]						=			attention_probs_dropout_prob
          __A		:					Optional[int]						=			max_position_embeddings
          __A		:					Optional[int]						=			type_vocab_size
          __A		:					int						=			type_sequence_label_size
          __A		:					str						=			initializer_range
          __A		:					Tuple						=			num_choices


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size)

          __A		:					Union[str, Any]						=			None
          if self.use_attention_mask:
               __A		:					Tuple						=			random_attention_mask([self.batch_size, self.seq_length])

          __A		:					Dict						=			None
          if self.use_token_type_ids:
               __A		:					Any						=			ids_tensor([self.batch_size, self.seq_length]		,      self.type_vocab_size)

          __A		:					int						=			RobertaConfig(
              vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      type_vocab_size=self.type_vocab_size		,      is_decoder=_UpperCAmelCase		,      initializer_range=self.initializer_range		,      )

          return config, input_ids, token_type_ids, attention_mask


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			self.prepare_config_and_inputs()
          __A					,__A					,__A					,__A		:					Any						=			config_and_inputs
          __A		:					Tuple						=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			self.prepare_config_and_inputs()
          __A					,__A					,__A					,__A		:					Optional[int]						=			config_and_inputs

          __A		:					Any						=			True
          __A		:					str						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          __A		:					Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length]		,      vocab_size=2)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )




@require_flax
class 					SCREAMING_SNAKE_CASE       (a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			True

     lowerCAmelCase						     =			(
         (
             FlaxRobertaModel,
             FlaxRobertaForCausalLM,
             FlaxRobertaForMaskedLM,
             FlaxRobertaForSequenceClassification,
             FlaxRobertaForTokenClassification,
             FlaxRobertaForMultipleChoice,
             FlaxRobertaForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			FlaxRobertaModelTester(self)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          for model_class_name in self.all_model_classes:
               __A		:					Any						=			model_class_name.from_pretrained('roberta-base'		,      from_pt=_UpperCAmelCase)
               __A		:					List[str]						=			model(np.ones((1, 1)))
               self.assertIsNotNone(_UpperCAmelCase)



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCAmelCase   :    Union[str, Any]     =		None


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)


UpperCAmelCase   :    Optional[Any]     =		"▁"

UpperCAmelCase   :    str     =		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase   :    Dict     =		{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase   :    List[Any]     =		{
    "google/pegasus-xsum": 5_12,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					int			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					List[Any]			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Any			       =		PegasusTokenizer
		UpperCamelCase     :					Optional[Any]			       =		["input_ids", "attention_mask"]







		def __init__( self		,      A=None		,      A=None		,      A="<pad>"		,      A="</s>"		,      A="<unk>"		,      A="<mask_2>"		,      A="<mask_1>"		,      A=None		,      A=1_03		,      **A		,      )  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  offset

					if additional_special_tokens is not None:
								if not isinstance(A		,      A	):
											raise TypeError(
											    F'additional_special_tokens should be of type {type(A	)}, but is'
											    F' {type(A	)}'	)

								lowerCamelCase    		=  (
								    ([mask_token_sent] + additional_special_tokens)
								    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
								    else additional_special_tokens
								)
								# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
								additional_special_tokens_extended += [
								    F'<unk_{i}>' for i in range(len(A	)		,      self.offset - 1	)
								]

								if len(set(A	)	) != len(A	):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'	)
								lowerCamelCase    		=  additional_special_tokens_extended
					else:
								lowerCamelCase    		=  [mask_token_sent] if mask_token_sent is not None else []
								additional_special_tokens += [F'<unk_{i}>' for i in range(2		,      self.offset	)]

					super().__init__(
					    A		,      tokenizer_file=A		,      pad_token=A		,      eos_token=A		,      unk_token=A		,      mask_token=A		,      mask_token_sent=A		,      offset=A		,      additional_special_tokens=A		,      **A		,      )
					lowerCamelCase    		=  vocab_file
					lowerCamelCase    		=  False if not self.vocab_file else True







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  set(self.all_special_ids	)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
								raise ValueError(
								    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
								    F' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'	)

					return [1 if x in all_special_ids else 0 for x in seq]







		def 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''simple docstring'''







					if already_has_special_tokens:
								return self._special_token_mask(A	)
					elif token_ids_a is None:
								return self._special_token_mask(A	) + [1]
					else:
								return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







		def 					__A   ( self		,      A		,      A=None	)  -> List[int]:







					'''simple docstring'''







					if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]







		def 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''simple docstring'''







					if not self.can_save_slow_tokenizer:
								raise ValueError(
								    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
								    """tokenizer."""	)

					if not os.path.isdir(A	):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
								return
					lowerCamelCase    		=  os.path.join(
					    A		,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

					if os.path.abspath(self.vocab_file	) != os.path.abspath(A	):
								copyfile(self.vocab_file		,      A	)

					return (out_vocab_file,)

