
'''simple docstring'''

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 ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					MvpTokenizer
     a_    	=					MvpTokenizerFast
     a_    	=					True
     a_    	=					filter_roberta_detectors


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

       a_      :	Optional[int]      =							os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""] )
       a_      :	Optional[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(lowerCAmelCase_ ) + """\n""" )
       with open(self.merges_file							,		"""w"""							,		encoding="""utf-8""" ) as fp:
         fp.write("""\n""".join(lowerCAmelCase_ ) )


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return "lower newer", "lower newer"


     @cached_property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""" )


     @cached_property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""" )


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       a_      :	Dict      =							[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]:
         a_      :	Union[str, Any]      =							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 )
         a_      :	int      =							batch.input_ids.tolist()[0]
         self.assertListEqual(lowerCAmelCase_							,		lowerCAmelCase_ )
         # Test that special tokens are reset


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a_      :	str      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a_      :	str      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							["""A long paragraph for summarization."""]
       a_      :	Dict      =							[
           """Summary of the text.""",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a_      :	Optional[int]      =							tokenizer(lowerCAmelCase_							,		text_target=lowerCAmelCase_							,		return_tensors="""pt""" )
         a_      :	Tuple      =							inputs["""input_ids"""]
         a_      :	Tuple      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

           a_      :	Optional[int]      =							tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
           a_      :	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, 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>"""] )




'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate


@dataclass(frozen=lowerCAmelCase__    )
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					field(default="automatic-speech-recognition"	,metadata={"include_in_asdict_even_if_is_default": True}    )
     a_    	=					Features({"audio": Audio()}    )
     a_    	=					Features({"transcription": Value("string"    )}    )
     a_    	=					"audio"
     a_    	=					"transcription"


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       if self.audio_column not in features:
         raise ValueError(f'''Column {self.audio_column} is not present in features.''' )
       if not isinstance(features[self.audio_column]							,		lowerCAmelCase_ ):
         raise ValueError(f'''Column {self.audio_column} is not an Audio type.''' )
       a_      :	int      =							copy.deepcopy(self )
       a_      :	Optional[int]      =							self.input_schema.copy()
       a_      :	Any      =							features[self.audio_column]
       a_      :	Any      =							input_schema
       return task_template






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return {self.audio_column: "audio", self.transcription_column: "transcription"}


