



'''simple docstring'''





import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


_lowercase						  =		"hf-internal-testing/tiny-random-bert"
_lowercase						  =		os.path.join(TRANSFORMERS_CACHE, "models--hf-internal-testing--tiny-random-bert")
_lowercase						  =		"9b8c223d42b2188cb49d29af482996f9d0f3e5a6"



class     _UpperCAmelCase			(       unittest.TestCase     ):




     def 		snake_case_		(				self):
         A__		 =					cached_file(lowerCAmelCase_ ,						lowerCAmelCase_)
         # Should have downloaded the file in here
         self.assertTrue(os.path.isdir(lowerCAmelCase_))
         # Cache should contain at least those three subfolders:
         for subfolder in ["blobs", "refs", "snapshots"]:
             self.assertTrue(os.path.isdir(os.path.join(lowerCAmelCase_ ,						lowerCAmelCase_)))
         with open(os.path.join(lowerCAmelCase_ ,						'''refs''' ,						'''main''')) as f:
             A__		 =					f.read()
         self.assertEqual(lowerCAmelCase_ ,						os.path.join(lowerCAmelCase_ ,						'''snapshots''' ,						lowerCAmelCase_ ,						lowerCAmelCase_))
         self.assertTrue(os.path.isfile(lowerCAmelCase_))

         # File is cached at the same place the second time.
         A__		 =					cached_file(lowerCAmelCase_ ,						lowerCAmelCase_)
         self.assertEqual(lowerCAmelCase_ ,						lowerCAmelCase_)

         # Using a specific revision to test the full commit hash.
         A__		 =					cached_file(lowerCAmelCase_ ,						lowerCAmelCase_ ,						revision='''9b8c223''')
         self.assertEqual(lowerCAmelCase_ ,						os.path.join(lowerCAmelCase_ ,						'''snapshots''' ,						lowerCAmelCase_ ,						lowerCAmelCase_))




     def 		snake_case_		(				self):
         with self.assertRaisesRegex(lowerCAmelCase_ ,						'''is not a valid model identifier'''):
             A__		 =					cached_file('''tiny-random-bert''' ,						lowerCAmelCase_)

         with self.assertRaisesRegex(lowerCAmelCase_ ,						'''is not a valid git identifier'''):
             A__		 =					cached_file(lowerCAmelCase_ ,						lowerCAmelCase_ ,						revision='''aaaa''')

         with self.assertRaisesRegex(lowerCAmelCase_ ,						'''does not appear to have a file named'''):
             A__		 =					cached_file(lowerCAmelCase_ ,						'''conf''')




     def 		snake_case_		(				self):
         with self.assertRaisesRegex(lowerCAmelCase_ ,						'''does not appear to have a file named'''):
             A__		 =					cached_file(lowerCAmelCase_ ,						'''conf''')

         with open(os.path.join(lowerCAmelCase_ ,						'''refs''' ,						'''main''')) as f:
             A__		 =					f.read()
         self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase_ ,						'''.no_exist''' ,						lowerCAmelCase_ ,						'''conf''')))

         A__		 =					cached_file(lowerCAmelCase_ ,						'''conf''' ,						_raise_exceptions_for_missing_entries=lowerCAmelCase_)
         self.assertIsNone(lowerCAmelCase_)

         A__		 =					cached_file(lowerCAmelCase_ ,						'''conf''' ,						local_files_only=lowerCAmelCase_ ,						_raise_exceptions_for_missing_entries=lowerCAmelCase_)
         self.assertIsNone(lowerCAmelCase_)

         A__		 =					mock.Mock()
         A__		 =					5_0_0
         A__		 =					{}
         A__		 =					HTTPError
         A__		 =					{}

         # Under the mock environment we get a 500 error when trying to reach the tokenizer.
         with mock.patch('''requests.Session.request''' ,						return_value=lowerCAmelCase_) as mock_head:
             A__		 =					cached_file(lowerCAmelCase_ ,						'''conf''' ,						_raise_exceptions_for_connection_errors=lowerCAmelCase_)
             self.assertIsNone(lowerCAmelCase_)
             # This check we did call the fake head request
             mock_head.assert_called()




     def 		snake_case_		(				self):
         self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,						lowerCAmelCase_))
         self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,						lowerCAmelCase_))
         self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,						lowerCAmelCase_))




     def 		snake_case_		(				self):
         # `get_file_from_repo` returns None if the file does not exist
         self.assertIsNone(get_file_from_repo('''bert-base-cased''' ,						'''ahah.txt'''))

         # The function raises if the repository does not exist.
         with self.assertRaisesRegex(lowerCAmelCase_ ,						'''is not a valid model identifier'''):
             get_file_from_repo('''bert-base-case''' ,						lowerCAmelCase_)

         # The function raises if the revision does not exist.
         with self.assertRaisesRegex(lowerCAmelCase_ ,						'''is not a valid git identifier'''):
             get_file_from_repo('''bert-base-cased''' ,						lowerCAmelCase_ ,						revision='''ahaha''')

         A__		 =					get_file_from_repo('''bert-base-cased''' ,						lowerCAmelCase_)
         # The name is the cached name which is not very easy to test, so instead we load the content.
         A__		 =					json.loads(open(lowerCAmelCase_ ,						'''r''').read())
         self.assertEqual(config['''hidden_size'''] ,						7_6_8)




     def 		snake_case_		(				self):
         with tempfile.TemporaryDirectory() as tmp_dir:
             A__		 =					Path(lowerCAmelCase_) / '''a.txt'''
             filename.touch()
             self.assertEqual(get_file_from_repo(lowerCAmelCase_ ,						'''a.txt''') ,						str(lowerCAmelCase_))

             self.assertIsNone(get_file_from_repo(lowerCAmelCase_ ,						'''b.txt'''))





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''')
