




import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:




       class 		UpperCamelCase__	:

           '''simple docstring'''



           @staticmethod
           def     _lowercase     (    *UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Union[str, Any]:
                  pass




@is_pipeline_test
@require_vision
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    @require_torch
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      Tuple			 =					pipeline(
               model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"     ,  )
           lowerCamelCase   :      Tuple			 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
           lowerCamelCase   :      List[Any]			 =					image_classifier(UpperCamelCase__     ,  candidate_labels=["a", "b", "c"]     )

           # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
           # python and torch versions.
           self.assertIn(
               nested_simplify(UpperCamelCase__     )     ,  [
                   [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
                   [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
               ]     ,  )

           lowerCamelCase   :      List[Any]			 =					image_classifier([image] * 5     ,  candidate_labels=["A", "B", "C"]     ,  batch_size=2     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
               ]     ,  )







    @require_tf
    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      str			 =					pipeline(
               model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"     ,  framework="tf"     )
           lowerCamelCase   :      int			 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
           lowerCamelCase   :      Dict			 =					image_classifier(UpperCamelCase__     ,  candidate_labels=["a", "b", "c"]     )

           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}]     ,  )

           lowerCamelCase   :      str			 =					image_classifier([image] * 5     ,  candidate_labels=["A", "B", "C"]     ,  batch_size=2     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                       {"score": 0.333, "label": ANY(UpperCamelCase__     )},
                   ],
               ]     ,  )







    @slow
    @require_torch
    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      int			 =					pipeline(
               task="zero-shot-image-classification"     ,  model="openai/clip-vit-base-patch32"     ,  )
           # This is an image of 2 cats with remotes and no planes
           lowerCamelCase   :      Union[str, Any]			 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
           lowerCamelCase   :      Optional[int]			 =					image_classifier(UpperCamelCase__     ,  candidate_labels=["cat", "plane", "remote"]     )

           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [
                   {"score": 0.511, "label": "remote"},
                   {"score": 0.485, "label": "cat"},
                   {"score": 0.004, "label": "plane"},
               ]     ,  )

           lowerCamelCase   :      Tuple			 =					image_classifier([image] * 5     ,  candidate_labels=["cat", "plane", "remote"]     ,  batch_size=2     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [
                   [
                       {"score": 0.511, "label": "remote"},
                       {"score": 0.485, "label": "cat"},
                       {"score": 0.004, "label": "plane"},
                   ],
               ]
               * 5     ,  )







    @slow
    @require_tf
    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      str			 =					pipeline(
               task="zero-shot-image-classification"     ,  model="openai/clip-vit-base-patch32"     ,  framework="tf"     )
           # This is an image of 2 cats with remotes and no planes
           lowerCamelCase   :      Any			 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
           lowerCamelCase   :      Tuple			 =					image_classifier(UpperCamelCase__     ,  candidate_labels=["cat", "plane", "remote"]     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [
                   {"score": 0.511, "label": "remote"},
                   {"score": 0.485, "label": "cat"},
                   {"score": 0.004, "label": "plane"},
               ]     ,  )

           lowerCamelCase   :      int			 =					image_classifier([image] * 5     ,  candidate_labels=["cat", "plane", "remote"]     ,  batch_size=2     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [
                   [
                       {"score": 0.511, "label": "remote"},
                       {"score": 0.485, "label": "cat"},
                       {"score": 0.004, "label": "plane"},
                   ],
               ]
               * 5     ,  )











import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase: Optional[int]      			=		get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							XLMProphetNetTokenizer
	__A							=							False
	__A							=							True




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()

						# We have a SentencePiece fixture for testing
						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)
						tokenizer.save_pretrained(self.tmpdirname		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "[PAD]"
						a					     =   0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_		)		,       lowerCamelCase_		)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_		)		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   list(self.get_tokenizer().get_vocab().keys()		)

						self.assertEqual(vocab_keys[0]		,       "[PAD]"		)
						self.assertEqual(vocab_keys[1]		,       "[CLS]"		)
						self.assertEqual(vocab_keys[-1]		,       "j"		)
						self.assertEqual(len(lowerCamelCase_		)		,       1012		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.assertEqual(self.get_tokenizer().vocab_size		,       1012		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)

						a					     =   tokenizer.tokenize("This is a test"		)
						self.assertListEqual(lowerCamelCase_		,       ["▁This", "▁is", "▁a", "▁t", "est"]		)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(lowerCamelCase_		)		,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,       )

						a					     =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "9",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "é",
						        ".",
						    ]		,       )
						a					     =   tokenizer.convert_tokens_to_ids(lowerCamelCase_		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]		,       )

						a					     =   tokenizer.convert_ids_to_tokens(lowerCamelCase_		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "[UNK]",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "[UNK]",
						        ".",
						    ]		,       )




	@cached_property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "Hello World!"
						a					     =   [35389, 6672, 49, 2]
						self.assertListEqual(lowerCamelCase_		,       self.big_tokenizer.encode(lowerCamelCase_		)		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   {"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=lowerCamelCase_		,       model_name="microsoft/xprophetnet-large-wiki100-cased"		,       revision="1acad1643ddd54a44df6a1b797ada8373685d90e"		,       )
