



'''simple docstring'''





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						  =		get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class     _UpperCAmelCase			(       UpperCamelCase__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				XLMProphetNetTokenizer
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				True




     def 		snake_case_		(				self):
         super().setUp()

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




     def 		snake_case_		(				self):
         A__		 =					'''[PAD]'''
         A__		 =					0

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




     def 		snake_case_		(				self):
         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(a__) ,						1_0_1_2)




     def 		snake_case_		(				self):
         self.assertEqual(self.get_tokenizer().vocab_size ,						1_0_1_2)




     def 		snake_case_		(				self):
         A__		 =					XLMProphetNetTokenizer(a__ ,						keep_accents=a__)

         A__		 =					tokenizer.tokenize('''This is a test''')
         self.assertListEqual(a__ ,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(a__) ,						[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] ,						)

         A__		 =					tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
         self.assertListEqual(
             a__ ,						[
                 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(a__)
         self.assertListEqual(
             a__ ,						[
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
             ] ,						)

         A__		 =					tokenizer.convert_ids_to_tokens(a__)
         self.assertListEqual(
             a__ ,						[
                 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 		snake_case_		(				self):
         return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''')




     @slow
     def 		snake_case_		(				self):
         A__		 =					'''Hello World!'''
         A__		 =					[3_5_3_8_9, 6_6_7_2, 4_9, 2]
         self.assertListEqual(a__ ,						self.big_tokenizer.encode(a__))




     @slow
     def 		snake_case_		(				self):
         # fmt: off
         A__		 =					{'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 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], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 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=a__ ,						model_name='''microsoft/xprophetnet-large-wiki100-cased''' ,						revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' ,						)





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     _UpperCAmelCase			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Image	)->  str:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					DepthEstimationPipeline(model=a__ ,						image_processor=a__)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




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

         A__		 =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test''')
         A__		 =					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)},
             ] ,						a__ ,						)




     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF''')
     def 		snake_case_		(				self):
         pass




     @slow
     @require_torch
     def 		snake_case_		(				self):
         A__		 =					'''Intel/dpt-large'''
         A__		 =					pipeline('''depth-estimation''' ,						model=a__)
         A__		 =					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
         A__		 =					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 		snake_case_		(				self):
         # This is highly irregular to have no small tests.
         self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')
