




'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image
else:

      class 	SCREAMING_SNAKE_CASE_		:



       @staticmethod
       def  SCREAMING_SNAKE_CASE(	*A ,		**A			):






              '''simple docstring'''




              pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	SCREAMING_SNAKE_CASE_		(							unittest.TestCase					):
 __snake_case						:  Dict       =   MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 __snake_case						:  Tuple       =   TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   ImageClassificationPipeline(model=__lowerCamelCase ,		image_processor=__lowerCamelCase ,		top_k=2			)
        lowercase	:		List[Any]    =   [
            Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			),
            "http://images.cocodataset.org/val2017/000000039769.jpg",
        ]
        return image_classifier, examples



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Any    =   image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)

        self.assertEqual(
            __lowerCamelCase ,		[
                {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
            ] ,		)

        import datasets

        lowercase	:		Optional[Any]    =   datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,		'''image''' ,		split='''test'''			)

        # Accepts URL + PIL.Image + lists
        lowercase	:		Optional[int]    =   image_classifier(
            [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			),
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                # RGBA
                dataset[0]['''file'''],
                # LA
                dataset[1]['''file'''],
                # L
                dataset[2]['''file'''],
            ]			)
        self.assertEqual(
            __lowerCamelCase ,		[
                [
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                ],
                [
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                ],
                [
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                ],
                [
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                ],
                [
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                    {'''score''': ANY(__lowerCamelCase			), '''label''': ANY(__lowerCamelCase			)},
                ],
            ] ,		)



 @require_torch
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   "hf-internal-testing/tiny-random-vit"
        lowercase	:		str    =   pipeline('''image-classification''' ,		model=__lowerCamelCase			)

        lowercase	:		Optional[int]    =   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
        self.assertEqual(
            nested_simplify(__lowerCamelCase ,		decimals=4			) ,		[{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}] ,		)

        lowercase	:		str    =   image_classifier(
            [
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
            ] ,		top_k=2 ,		)
        self.assertEqual(
            nested_simplify(__lowerCamelCase ,		decimals=4			) ,		[
                [{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}],
                [{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}],
            ] ,		)



 @require_tf
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   "hf-internal-testing/tiny-random-vit"
        lowercase	:		Tuple    =   pipeline('''image-classification''' ,		model=__lowerCamelCase ,		framework='''tf'''			)

        lowercase	:		Optional[Any]    =   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
        self.assertEqual(
            nested_simplify(__lowerCamelCase ,		decimals=4			) ,		[{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}] ,		)

        lowercase	:		Optional[int]    =   image_classifier(
            [
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
            ] ,		top_k=2 ,		)
        self.assertEqual(
            nested_simplify(__lowerCamelCase ,		decimals=4			) ,		[
                [{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}],
                [{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}],
            ] ,		)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Tuple    =   PreTrainedTokenizer()

        # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
        lowercase	:		Dict    =   pipeline(
            '''image-classification''' ,		model='''hf-internal-testing/tiny-random-vit''' ,		tokenizer=__lowerCamelCase			)

        self.assertIs(image_classifier.tokenizer ,		__lowerCamelCase			)





 @slow
 @require_torch
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Dict    =   pipeline('''image-classification''' ,		model='''deepmind/vision-perceiver-conv'''			)
        lowercase	:		Optional[int]    =   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
        self.assertEqual(
            nested_simplify(__lowerCamelCase ,		decimals=4			) ,		[
                {'''score''': 0.4_3_8_5, '''label''': '''tabby, tabby cat'''},
                {'''score''': 0.3_2_1, '''label''': '''tiger cat'''},
                {'''score''': 0.0_5_0_2, '''label''': '''Egyptian cat'''},
                {'''score''': 0.0_1_3_7, '''label''': '''crib, cot'''},
                {'''score''': 0.0_0_7, '''label''': '''radiator'''},
            ] ,		)

        lowercase	:		Dict    =   pipeline('''image-classification''' ,		model='''deepmind/vision-perceiver-fourier'''			)
        lowercase	:		str    =   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
        self.assertEqual(
            nested_simplify(__lowerCamelCase ,		decimals=4			) ,		[
                {'''score''': 0.5_6_5_8, '''label''': '''tabby, tabby cat'''},
                {'''score''': 0.1_3_0_9, '''label''': '''tiger cat'''},
                {'''score''': 0.0_7_2_2, '''label''': '''Egyptian cat'''},
                {'''score''': 0.0_7_0_7, '''label''': '''remote control, remote'''},
                {'''score''': 0.0_0_8_2, '''label''': '''computer keyboard, keypad'''},
            ] ,		)

        lowercase	:		Optional[int]    =   pipeline('''image-classification''' ,		model='''deepmind/vision-perceiver-learned'''			)
        lowercase	:		List[Any]    =   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
        self.assertEqual(
            nested_simplify(__lowerCamelCase ,		decimals=4			) ,		[
                {'''score''': 0.3_0_2_2, '''label''': '''tabby, tabby cat'''},
                {'''score''': 0.2_3_6_2, '''label''': '''Egyptian cat'''},
                {'''score''': 0.1_8_5_6, '''label''': '''tiger cat'''},
                {'''score''': 0.0_3_2_4, '''label''': '''remote control, remote'''},
                {'''score''': 0.0_0_9_6, '''label''': '''quilt, comforter, comfort, puff'''},
            ] ,		)









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

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


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE     =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						__lowerCamelCase   :      str=None						,						__lowerCamelCase   :      int=None						,						__lowerCamelCase   :      Optional[Any]=False						,						__lowerCamelCase   :      List[Any]=True						,						__lowerCamelCase   :      int=False						,						__lowerCamelCase   :      Optional[int]="<s>"						,						__lowerCamelCase   :      Union[str, Any]="</s>"						,						__lowerCamelCase   :      Dict="<unk>"						,						__lowerCamelCase   :      List[str]="<sep>"						,						__lowerCamelCase   :      Any="<pad>"						,						__lowerCamelCase   :      Optional[Any]="<cls>"						,						__lowerCamelCase   :      Union[str, Any]="<mask>"						,						__lowerCamelCase   :      List[str]=["<eop>", "<eod>"]						,						**__lowerCamelCase   :      Optional[Any]						,						)   ->					Optional[int]:
									A				:       Any														=    AddedToken(__lowerCamelCase						,						lstrip=__lowerCamelCase						,						rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase						,						__lowerCamelCase			) else mask_token

									super().__init__(
									    vocab_file=__lowerCamelCase						,						tokenizer_file=__lowerCamelCase						,						do_lower_case=__lowerCamelCase						,						remove_space=__lowerCamelCase						,						keep_accents=__lowerCamelCase						,						bos_token=__lowerCamelCase						,						eos_token=__lowerCamelCase						,						unk_token=__lowerCamelCase						,						sep_token=__lowerCamelCase						,						pad_token=__lowerCamelCase						,						cls_token=__lowerCamelCase						,						mask_token=__lowerCamelCase						,						additional_special_tokens=__lowerCamelCase						,						**__lowerCamelCase						,						)

									A				:       str														=    3
									A				:       str														=    do_lower_case
									A				:       int														=    remove_space
									A				:       Optional[int]														=    keep_accents
									A				:       Tuple														=    vocab_file
									A				:       int														=    False if not self.vocab_file else True

									try:
												import jieba
									except ModuleNotFoundError as error:
												raise error.__class__(
												    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
												    "See https://pypi.org/project/jieba/ for installation."			)
									A				:       str														=    jieba
									A				:       Optional[Any]														=    str.maketrans(" \n"						,						"\u2582\u2583"			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Dict														=    [self.sep_token_id]
									A				:       Tuple														=    [self.cls_token_id]
									if token_ids_a is None:
												return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Tuple														=    [self.sep_token_id]
									A				:       Dict														=    [2]

									if token_ids_a is None:
												return len(token_ids_a + sep			) * [0] + cls_segment_id
									return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Optional[str] = None			)   ->					Tuple[str]:
									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(__lowerCamelCase			):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
												return
									A				:       List[Any]														=    os.path.join(
									    __lowerCamelCase						,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

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

									return (out_vocab_file,)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      Dict						,						*__lowerCamelCase   :      List[str]						,						**__lowerCamelCase   :      Tuple			)   ->					str:
									A				:       Union[str, Any]														=    [
									    " ".join([x.translate(self.translator			) for x in self.jieba.cut(__lowerCamelCase						,						cut_all=__lowerCamelCase			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(__lowerCamelCase						,						*__lowerCamelCase						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						*__lowerCamelCase   :      Optional[Any]						,						**__lowerCamelCase   :      Optional[int]			)   ->					Tuple:
									A				:       Optional[Any]														=    super()._decode(*__lowerCamelCase						,						**__lowerCamelCase			)
									A				:       Tuple														=    text.replace(" "						,						""			).replace("\u2582"						,						" "			).replace("\u2583"						,						"\n"			)
									return text