



import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



class     _UpperCAmelCase			(       A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				ProphetNetTokenizer
     UpperCamelCase__		    =				False




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

         A__		 =					[
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''want''',
             '''##want''',
             '''##ed''',
             '''wa''',
             '''un''',
             '''runn''',
             '''##ing''',
             ''',''',
             '''low''',
             '''lowest''',
         ]
         A__		 =					os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file'''])
         with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8''') as vocab_writer:
             vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))




     def 		snake_case_		(				self ,						a__):
         A__		 =					'''UNwant\u00E9d,running'''
         A__		 =					'''unwanted, running'''
         return input_text, output_text




     def 		snake_case_		(				self):
         A__		 =					self.tokenizer_class(self.vocab_file)

         A__		 =					tokenizer.tokenize('''UNwant\u00E9d,running''')
         self.assertListEqual(a__ ,						['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''])
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__) ,						[9, 6, 7, 1_2, 1_0, 1_1])




     def 		snake_case_		(				self):
         A__		 =					BasicTokenizer()

         self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''') ,						['''ah''', '''\u535A''', '''\u63A8''', '''zz'''])




     def 		snake_case_		(				self):
         A__		 =					BasicTokenizer(do_lower_case=a__)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''') ,						['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''])
         self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''') ,						['''hello'''])




     def 		snake_case_		(				self):
         A__		 =					BasicTokenizer(do_lower_case=a__ ,						strip_accents=a__)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''') ,						['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
         self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''') ,						['''h\u00E9llo'''])




     def 		snake_case_		(				self):
         A__		 =					BasicTokenizer(do_lower_case=a__ ,						strip_accents=a__)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''') ,						['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
         self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''') ,						['''hello'''])




     def 		snake_case_		(				self):
         A__		 =					BasicTokenizer(do_lower_case=a__)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''') ,						['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
         self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''') ,						['''hello'''])




     def 		snake_case_		(				self):
         A__		 =					BasicTokenizer(do_lower_case=a__)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''') ,						['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])




     def 		snake_case_		(				self):
         A__		 =					BasicTokenizer(do_lower_case=a__ ,						strip_accents=a__)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''') ,						['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])




     def 		snake_case_		(				self):
         A__		 =					BasicTokenizer(do_lower_case=a__ ,						strip_accents=a__)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''') ,						['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])




     def 		snake_case_		(				self):
         A__		 =					BasicTokenizer(do_lower_case=a__ ,						never_split=['''[UNK]'''])

         self.assertListEqual(
             tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]''') ,						['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''])




     def 		snake_case_		(				self):
         A__		 =					['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

         A__		 =					{}
         for i, token in enumerate(a__):
             A__		 =					i
         A__		 =					WordpieceTokenizer(vocab=a__ ,						unk_token='''[UNK]''')

         self.assertListEqual(tokenizer.tokenize('''''') ,						[])

         self.assertListEqual(tokenizer.tokenize('''unwanted running''') ,						['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''])

         self.assertListEqual(tokenizer.tokenize('''unwantedX running''') ,						['''[UNK]''', '''runn''', '''##ing'''])




     @require_torch
     def 		snake_case_		(				self):
         A__		 =					self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''')

         A__		 =					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         A__		 =					[1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
         A__		 =					tokenizer(a__ ,						padding=a__ ,						return_tensors='''pt''')
         self.assertIsInstance(a__ ,						a__)
         A__		 =					list(batch.input_ids.numpy()[0])
         self.assertListEqual(a__ ,						a__)

         self.assertEqual((2, 9) ,						batch.input_ids.shape)
         self.assertEqual((2, 9) ,						batch.attention_mask.shape)




     def 		snake_case_		(				self):
         self.assertTrue(_is_whitespace(''' '''))
         self.assertTrue(_is_whitespace('''\t'''))
         self.assertTrue(_is_whitespace('''\r'''))
         self.assertTrue(_is_whitespace('''\n'''))
         self.assertTrue(_is_whitespace('''\u00A0'''))

         self.assertFalse(_is_whitespace('''A'''))
         self.assertFalse(_is_whitespace('''-'''))




     def 		snake_case_		(				self):
         self.assertTrue(_is_control('''\u0005'''))

         self.assertFalse(_is_control('''A'''))
         self.assertFalse(_is_control(''' '''))
         self.assertFalse(_is_control('''\t'''))
         self.assertFalse(_is_control('''\r'''))




     def 		snake_case_		(				self):
         self.assertTrue(_is_punctuation('''-'''))
         self.assertTrue(_is_punctuation('''$'''))
         self.assertTrue(_is_punctuation('''`'''))
         self.assertTrue(_is_punctuation('''.'''))

         self.assertFalse(_is_punctuation('''A'''))
         self.assertFalse(_is_punctuation(''' '''))




     @slow
     def 		snake_case_		(				self):
         A__		 =					self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''')

         A__		 =					tokenizer.encode('''sequence builders''' ,						add_special_tokens=a__)
         A__		 =					tokenizer.encode('''multi-sequence build''' ,						add_special_tokens=a__)

         A__		 =					tokenizer.build_inputs_with_special_tokens(a__)
         A__		 =					tokenizer.build_inputs_with_special_tokens(a__ ,						a__)

         assert encoded_sentence == text + [1_0_2]
         assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]





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''')
