

import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 				__UpperCamelCase				(					lowercase_  ,						unittest.TestCase			):
 _lowercase  :   Optional[Any]       				=  MvpTokenizer
 _lowercase  :   Optional[Any]       				=  MvpTokenizerFast
 _lowercase  :   Optional[int]       				=  True
 _lowercase  :   Optional[Any]       				=  filter_roberta_detectors







 def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

      '''simple docstring'''





      super().setUp()
      __magic_name__							=    [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      __magic_name__							=    dict(zip(lowerCamelCase_  , range(len(lowerCamelCase_      )      )      )      )
      __magic_name__							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      __magic_name__							=    {"""unk_token""": """<unk>"""}

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







 def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , **__UpperCamelCase: List[str]      ):

      '''simple docstring'''





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







 def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , **__UpperCamelCase: Optional[Any]      ):

      '''simple docstring'''





      kwargs.update(self.special_tokens_map      )
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  , **lowerCamelCase_      )







 def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: List[Any]      ):

      '''simple docstring'''





      return "lower newer", "lower newer"







 @cached_property
 def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

      '''simple docstring'''





      return MvpTokenizer.from_pretrained('RUCAIBox/mvp'      )







 @cached_property
 def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

      '''simple docstring'''





      return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'      )







 @require_torch
 def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

      '''simple docstring'''





      __magic_name__							=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      __magic_name__							=    [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__							=    tokenizer(lowerCamelCase_  , max_length=len(lowerCamelCase_      )  , padding=lowerCamelCase_  , return_tensors='pt'      )
           self.assertIsInstance(lowerCamelCase_  , lowerCamelCase_      )

           self.assertEqual((2, 9)  , batch.input_ids.shape      )
           self.assertEqual((2, 9)  , batch.attention_mask.shape      )
           __magic_name__							=    batch.input_ids.tolist()[0]
           self.assertListEqual(lowerCamelCase_  , lowerCamelCase_      )
           # Test that special tokens are reset







 @require_torch
 def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

      '''simple docstring'''





      __magic_name__							=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__							=    tokenizer(lowerCamelCase_  , padding=lowerCamelCase_  , return_tensors='pt'      )
           # check if input_ids are returned and no labels
           self.assertIn('input_ids'  , lowerCamelCase_      )
           self.assertIn('attention_mask'  , lowerCamelCase_      )
           self.assertNotIn('labels'  , lowerCamelCase_      )
           self.assertNotIn('decoder_attention_mask'  , lowerCamelCase_      )







 @require_torch
 def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

      '''simple docstring'''





      __magic_name__							=    [
          """Summary of the text.""",
          """Another summary.""",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__							=    tokenizer(text_target=lowerCamelCase_  , max_length=32  , padding='max_length'  , return_tensors='pt'      )
           self.assertEqual(32  , targets['input_ids'].shape[1]      )







 @require_torch
 def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

      '''simple docstring'''





      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__							=    tokenizer(
               ['I am a small frog' * 10_24, 'I am a small frog']  , padding=lowerCamelCase_  , truncation=lowerCamelCase_  , return_tensors='pt'      )
           self.assertIsInstance(lowerCamelCase_  , lowerCamelCase_      )
           self.assertEqual(batch.input_ids.shape  , (2, 10_24)      )







 @require_torch
 def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

      '''simple docstring'''





      __magic_name__							=    ["""A long paragraph for summarization."""]
      __magic_name__							=    [
          """Summary of the text.""",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__							=    tokenizer(lowerCamelCase_  , text_target=lowerCamelCase_  , return_tensors='pt'      )
           __magic_name__							=    inputs["""input_ids"""]
           __magic_name__							=    inputs["""labels"""]
           self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
           self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
           self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
           self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )







 def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

      '''simple docstring'''





      pass







 def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

      '''simple docstring'''





      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                __magic_name__							=    self.rust_tokenizer_class.from_pretrained(lowerCamelCase_  , **lowerCamelCase_      )
                __magic_name__							=    self.tokenizer_class.from_pretrained(lowerCamelCase_  , **lowerCamelCase_      )
                __magic_name__							=    """A, <mask> AllenNLP sentence."""
                __magic_name__							=    tokenizer_r.encode_plus(lowerCamelCase_  , add_special_tokens=lowerCamelCase_  , return_token_type_ids=lowerCamelCase_      )
                __magic_name__							=    tokenizer_p.encode_plus(lowerCamelCase_  , add_special_tokens=lowerCamelCase_  , return_token_type_ids=lowerCamelCase_      )

                # 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']      )  , )

                __magic_name__							=    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
                __magic_name__							=    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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
                self.assertSequenceEqual(tokens_r['input_ids']  , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )

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







import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
						import jax

						from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
						from transformers.models.bert.modeling_flax_bert import FlaxBertModel
						from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											with self.subTest(__UpperCamelCase      ):
																__magic_name__							=    AutoConfig.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )

																__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						for model_name in ["roberta-base", "roberta-large"]:
											with self.subTest(__UpperCamelCase      ):
																__magic_name__							=    AutoConfig.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )

																__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											__magic_name__							=    AutoTokenizer.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    FlaxBertModel.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    tokenizer('Do you support jax jitted function?'  , return_tensors=TensorType.JAX      )

											@jax.jit
											def eval(**__UpperCamelCase: Optional[Any]      ):
																return model(**__UpperCamelCase      )

											eval(**__UpperCamelCase      ).block_until_ready()







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						for model_name in ["roberta-base", "roberta-large"]:
											__magic_name__							=    AutoTokenizer.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    FlaxRobertaModel.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    tokenizer('Do you support jax jitted function?'  , return_tensors=TensorType.JAX      )

											@jax.jit
											def eval(**__UpperCamelCase: Any      ):
																return model(**__UpperCamelCase      )

											eval(**__UpperCamelCase      ).block_until_ready()







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , 'bert-base is not a local folder and is not a valid model identifier'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('bert-base'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase  , revision='aaaaaa'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , 'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'  , ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						with self.assertRaisesRegex(__UpperCamelCase  , 'Use `from_pt=True` to load this model'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'      )




