



"""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    UpperCamelCase     :




			"""simple docstring"""






			@staticmethod
			def 					lowerCamelCase__ (			*UpperCAmelCase_							,**UpperCAmelCase_					):
					pass





@is_pipeline_test
@require_torch_or_tf
@require_vision
class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Optional[Any]		    =   MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
	SCREAMING_SNAKE_CASE_				:    Union[str, Any]		    =   TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
			_lowercase							:      List[Any]			=					ImageClassificationPipeline(model=UpperCAmelCase_							,image_processor=UpperCAmelCase_							,top_k=2					)
			_lowercase							:      Dict			=					[
			    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					),
			    """http://images.cocodataset.org/val2017/000000039769.jpg""",
			]
			return image_classifier, examples






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_					):
			_lowercase							:      Union[str, Any]			=					image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)

			self.assertEqual(
			    UpperCAmelCase_							,[
			        {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			        {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			    ]							,)

			import datasets

			_lowercase							:      str			=					datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""							,"""image"""							,split="""test"""					)

			# Accepts URL + PIL.Image + lists
			_lowercase							:      str			=					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(
			    UpperCAmelCase_							,[
			        [
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			        ],
			        [
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			        ],
			        [
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			        ],
			        [
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			        ],
			        [
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			            {"""score""": ANY(UpperCAmelCase_					), """label""": ANY(UpperCAmelCase_					)},
			        ],
			    ]							,)






	@require_torch
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Union[str, Any]			=					"""hf-internal-testing/tiny-random-vit"""
			_lowercase							:      str			=					pipeline("""image-classification"""							,model=UpperCAmelCase_					)

			_lowercase							:      int			=					image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
			self.assertEqual(
			    nested_simplify(UpperCAmelCase_							,decimals=4					)							,[{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}]							,)

			_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(UpperCAmelCase_							,decimals=4					)							,[
			        [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
			        [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
			    ]							,)






	@require_tf
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      str			=					"""hf-internal-testing/tiny-random-vit"""
			_lowercase							:      Dict			=					pipeline("""image-classification"""							,model=UpperCAmelCase_							,framework="""tf"""					)

			_lowercase							:      Dict			=					image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
			self.assertEqual(
			    nested_simplify(UpperCAmelCase_							,decimals=4					)							,[{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}]							,)

			_lowercase							:      List[Any]			=					image_classifier(
			    [
			        """http://images.cocodataset.org/val2017/000000039769.jpg""",
			        """http://images.cocodataset.org/val2017/000000039769.jpg""",
			    ]							,top_k=2							,)
			self.assertEqual(
			    nested_simplify(UpperCAmelCase_							,decimals=4					)							,[
			        [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
			        [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
			    ]							,)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					PreTrainedTokenizer()

			# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
			_lowercase							:      List[str]			=					pipeline(
			    """image-classification"""							,model="""hf-internal-testing/tiny-random-vit"""							,tokenizer=UpperCAmelCase_					)

			self.assertIs(image_classifier.tokenizer							,UpperCAmelCase_					)






	@slow
	@require_torch
	def 					lowerCamelCase__ (			self					):
			# Perceiver is not tested by `run_pipeline_test` properly.
			# That is because the type of feature_extractor and model preprocessor need to be kept
			# in sync, which is not the case in the current design
			_lowercase							:      Any			=					pipeline("""image-classification"""							,model="""deepmind/vision-perceiver-conv"""					)
			_lowercase							:      List[str]			=					image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
			self.assertEqual(
			    nested_simplify(UpperCAmelCase_							,decimals=4					)							,[
			        {"""score""": 0.4385, """label""": """tabby, tabby cat"""},
			        {"""score""": 0.321, """label""": """tiger cat"""},
			        {"""score""": 0.0502, """label""": """Egyptian cat"""},
			        {"""score""": 0.0137, """label""": """crib, cot"""},
			        {"""score""": 0.007, """label""": """radiator"""},
			    ]							,)

			_lowercase							:      Optional[int]			=					pipeline("""image-classification"""							,model="""deepmind/vision-perceiver-fourier"""					)
			_lowercase							:      Optional[int]			=					image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
			self.assertEqual(
			    nested_simplify(UpperCAmelCase_							,decimals=4					)							,[
			        {"""score""": 0.5658, """label""": """tabby, tabby cat"""},
			        {"""score""": 0.1309, """label""": """tiger cat"""},
			        {"""score""": 0.0722, """label""": """Egyptian cat"""},
			        {"""score""": 0.0707, """label""": """remote control, remote"""},
			        {"""score""": 0.0082, """label""": """computer keyboard, keypad"""},
			    ]							,)

			_lowercase							:      int			=					pipeline("""image-classification"""							,model="""deepmind/vision-perceiver-learned"""					)
			_lowercase							:      Union[str, Any]			=					image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""					)
			self.assertEqual(
			    nested_simplify(UpperCAmelCase_							,decimals=4					)							,[
			        {"""score""": 0.3022, """label""": """tabby, tabby cat"""},
			        {"""score""": 0.2362, """label""": """Egyptian cat"""},
			        {"""score""": 0.1856, """label""": """tiger cat"""},
			        {"""score""": 0.0324, """label""": """remote control, remote"""},
			        {"""score""": 0.0096, """label""": """quilt, comforter, comfort, puff"""},
			    ]							,)











"""simple docstring"""







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_xglm import XGLMTokenizer
else:
  _UpperCamelCase     			=  None


_UpperCamelCase     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase     			=  {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

_UpperCamelCase     			=  {
    """facebook/xglm-564M""": 2048,
}







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=			PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=			['''input_ids''', '''attention_mask''']
    SCREAMING_SNAKE_CASE		=			XGLMTokenizer






    def __init__( self	,A=None	,A=None	,A="<s>"	,A="</s>"	,A="</s>"	,A="<s>"	,A="<unk>"	,A="<pad>"	,**A	,):
          # Compatibility with the original tokenizer
          UpperCAmelCase							=       7
          UpperCAmelCase							=       [F'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

          UpperCAmelCase							=       kwargs.get("""additional_special_tokens"""	,[]					)
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              A	,tokenizer_file=A	,bos_token=A	,eos_token=A	,sep_token=A	,cls_token=A	,unk_token=A	,pad_token=A	,**A	,)

          UpperCAmelCase							=       vocab_file
          UpperCAmelCase							=       False if not self.vocab_file else True






    def 				_UpperCamelCase							( self	,A	,A = None					):

          if token_ids_a is None:
                return [self.sep_token_id] + token_ids_a
          UpperCAmelCase							=       [self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a






    def 				_UpperCamelCase							( self	,A	,A = None					):
          UpperCAmelCase							=       [self.sep_token_id]

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






    def 				_UpperCamelCase							( self	,A	,A = None					):
          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
          UpperCAmelCase							=       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,)



