





import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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 			__A(    __UpperCAmelCase    ,      unittest.TestCase			):
     __A							=							BartTokenizer
     __A							=							BartTokenizerFast
     __A							=							True
     __A							=							filter_roberta_detectors

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            super().setUp()
            _UpperCamelCase														=    [
                '''l''',
                '''o''',
                '''w''',
                '''e''',
                '''r''',
                '''s''',
                '''t''',
                '''i''',
                '''d''',
                '''n''',
                '''\u0120''',
                '''\u0120l''',
                '''\u0120n''',
                '''\u0120lo''',
                '''\u0120low''',
                '''er''',
                '''\u0120lowest''',
                '''\u0120newer''',
                '''\u0120wider''',
                '''<unk>''',
            ]
            _UpperCamelCase														=    dict(zip(A,							range(len(A   )   )   )   )
            _UpperCamelCase														=    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
            _UpperCamelCase														=    {'''unk_token''': '''<unk>'''}

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

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            return "lower newer", "lower newer"

     @cached_property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return BartTokenizer.from_pretrained('''facebook/bart-large'''   )

     @cached_property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return BartTokenizerFast.from_pretrained('''facebook/bart-large'''   )

     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            _UpperCamelCase														=    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

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

     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _UpperCamelCase														=    tokenizer(
                       ['''I am a small frog''' * 1024, '''I am a small frog'''],							padding=A,							truncation=A,							return_tensors='''pt'''   )
                   self.assertIsInstance(A,							A   )
                   self.assertEqual(batch.input_ids.shape,							(2, 1024)   )

     @require_torch
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    ['''A long paragraph for summarization.''']
            _UpperCamelCase														=    [
                '''Summary of the text.''',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _UpperCamelCase														=    tokenizer(A,							return_tensors='''pt'''   )
                   _UpperCamelCase														=    tokenizer(text_target=A,							return_tensors='''pt'''   )
                   _UpperCamelCase														=    inputs['''input_ids''']
                   _UpperCamelCase														=    targets['''input_ids''']
                   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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

                          # 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''']   ),							)

                          _UpperCamelCase														=    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
                          _UpperCamelCase														=    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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )
                          self.assertSequenceEqual(tokens_r['''input_ids'''],							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )

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













import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
       from PIL import Image

class 			__A(    unittest.TestCase    ,      __UpperCAmelCase			):

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    load_tool('''image-question-answering'''   )
            self.tool.setup()
            _UpperCamelCase														=    load_tool('''image-question-answering''',							remote=A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )






