




import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Dict          =  BarthezTokenizer
   UpperCAmelCase__  :    Optional[Any]          =  BarthezTokenizerFast
   UpperCAmelCase__  :    Union[str, Any]          =  True
   UpperCAmelCase__  :    Optional[Any]          =  True







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        super().setUp()

        UpperCAmelCase_          =							BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''')
        tokenizer.save_pretrained(self.tmpdirname)
        tokenizer.save_pretrained(self.tmpdirname					,						legacy_format=_snake_case)
        UpperCAmelCase_          =							tokenizer







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''<pad>'''
        UpperCAmelCase_          =							1

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







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


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

        self.assertEqual(vocab_keys[0]					,						'''<s>''')
        self.assertEqual(vocab_keys[1]					,						'''<pad>''')
        self.assertEqual(vocab_keys[-1]					,						'''<mask>''')
        self.assertEqual(len(_snake_case)					,						101122)







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        self.assertEqual(self.get_tokenizer().vocab_size					,						101122)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_          =							[0, 57, 3018, 70307, 91, 2]

        UpperCAmelCase_          =							self.tokenizer(
            _snake_case					,						max_length=len(_snake_case)					,						padding=_snake_case					,						truncation=_snake_case					,						return_tensors='''pt''')
        self.assertIsInstance(_snake_case					,						_snake_case)

        self.assertEqual((2, 6)					,						batch.input_ids.shape)
        self.assertEqual((2, 6)					,						batch.attention_mask.shape)
        UpperCAmelCase_          =							batch.input_ids.tolist()[0]
        self.assertListEqual(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        if not self.test_rust_tokenizer:
             return

        UpperCAmelCase_          =							self.get_tokenizer()
        UpperCAmelCase_          =							self.get_rust_tokenizer()

        UpperCAmelCase_          =							'''I was born in 92000, and this is falsé.'''

        UpperCAmelCase_          =							tokenizer.tokenize(_snake_case)
        UpperCAmelCase_          =							rust_tokenizer.tokenize(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
        UpperCAmelCase_          =							rust_tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							self.get_rust_tokenizer()
        UpperCAmelCase_          =							tokenizer.encode(_snake_case)
        UpperCAmelCase_          =							rust_tokenizer.encode(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)







   @slow
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							{'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''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, 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, 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]]}  # noqa: E501
        # fmt: on

        # moussaKam/mbarthez is a french model. So we also use french texts.
        UpperCAmelCase_          =							[
            '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
            '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
            '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
            '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
            '''telles que la traduction et la synthèse de texte.''',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=_snake_case					,						model_name='''moussaKam/mbarthez'''					,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''					,						sequences=_snake_case					,						)












import hashlib
import unittest

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

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:







    class __snake_case			:







       @staticmethod
       def lowerCamelCase						(      *_snake_case		:						Optional[int]					,						**_snake_case		:						int):

            """simple docstring"""


            pass


def    A						(__A   :    Image       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							hashlib.mda(image.tobytes()       )
     return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    Tuple          =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							DepthEstimationPipeline(model=_snake_case					,						image_processor=_snake_case)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)}					,						_snake_case)
        import datasets

        UpperCAmelCase_          =							datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''					,						'''image'''					,						split='''test''')
        UpperCAmelCase_          =							depth_estimator(
            [
                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(
            [
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
            ]					,						_snake_case					,						)







   @require_tf
   @unittest.skip('''Depth estimation is not implemented in TF''')
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        pass







   @slow
   @require_torch
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Intel/dpt-large'''
        UpperCAmelCase_          =							pipeline('''depth-estimation'''					,						model=_snake_case)
        UpperCAmelCase_          =							depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
        UpperCAmelCase_          =							hashimage(outputs['''depth'''])

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item())					,						2_9.3_0_4)
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item())					,						2.6_6_2)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')






