import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


snake_case__					:      Tuple				      =  get_tests_dir('''fixtures''')






class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				mock.Mock()
       UpperCAmelCase_							:       List[str]       =				5_0_0
       UpperCAmelCase_							:       Any       =				{}
       UpperCAmelCase_							:       Any       =				HTTPError
       UpperCAmelCase_							:       str       =				{}

       # Download this model to make sure it's in the cache.
       UpperCAmelCase_							:       Optional[Any]       =				WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'						)
       # Under the mock environment we get a 500 error when trying to reach the model.
       with mock.patch('requests.Session.request'				,		return_value=lowerCAmelCase__						) as mock_head:
            UpperCAmelCase_							:       Optional[int]       =				WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'						)
            # This check we did call the fake head request
            mock_head.assert_called()






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				WavaVecaFeatureExtractor.from_pretrained(
           'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'						)







@is_staging_test
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  @classmethod
  def _UpperCamelCase					(			cls						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				TOKEN
       HfFolder.save_token(lowerCAmelCase__						)


  @classmethod
  def _UpperCamelCase					(			cls						):




       '''simple docstring'''

       try:
            delete_repo(token=cls._token				,		repo_id='test-feature-extractor'						)
       except HTTPError:
            pass

       try:
            delete_repo(token=cls._token				,		repo_id='valid_org/test-feature-extractor-org'						)
       except HTTPError:
            pass

       try:
            delete_repo(token=cls._token				,		repo_id='test-dynamic-feature-extractor'						)
       except HTTPError:
            pass


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__						)
       feature_extractor.push_to_hub('test-feature-extractor'				,		use_auth_token=self._token						)

       UpperCAmelCase_							:       str       =				WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''						)
       for k, v in feature_extractor.__dict__.items():
            self.assertEqual(lowerCAmelCase__				,		getattr(lowerCAmelCase__				,		lowerCAmelCase__						)						)

       # Reset repo
       delete_repo(token=self._token				,		repo_id='test-feature-extractor'						)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
            feature_extractor.save_pretrained(
                lowerCAmelCase__				,		repo_id='test-feature-extractor'				,		push_to_hub=lowerCAmelCase__				,		use_auth_token=self._token						)

       UpperCAmelCase_							:       str       =				WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''						)
       for k, v in feature_extractor.__dict__.items():
            self.assertEqual(lowerCAmelCase__				,		getattr(lowerCAmelCase__				,		lowerCAmelCase__						)						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__						)
       feature_extractor.push_to_hub('valid_org/test-feature-extractor'				,		use_auth_token=self._token						)

       UpperCAmelCase_							:       Optional[int]       =				WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'						)
       for k, v in feature_extractor.__dict__.items():
            self.assertEqual(lowerCAmelCase__				,		getattr(lowerCAmelCase__				,		lowerCAmelCase__						)						)

       # Reset repo
       delete_repo(token=self._token				,		repo_id='valid_org/test-feature-extractor'						)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
            feature_extractor.save_pretrained(
                lowerCAmelCase__				,		repo_id='valid_org/test-feature-extractor-org'				,		push_to_hub=lowerCAmelCase__				,		use_auth_token=self._token						)

       UpperCAmelCase_							:       Union[str, Any]       =				WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'						)
       for k, v in feature_extractor.__dict__.items():
            self.assertEqual(lowerCAmelCase__				,		getattr(lowerCAmelCase__				,		lowerCAmelCase__						)						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       CustomFeatureExtractor.register_for_auto_class()
       UpperCAmelCase_							:       Union[str, Any]       =				CustomFeatureExtractor.from_pretrained(lowerCAmelCase__						)

       feature_extractor.push_to_hub('test-dynamic-feature-extractor'				,		use_auth_token=self._token						)

       # This has added the proper auto_map field to the config
       self.assertDictEqual(
           feature_extractor.auto_map				,		{'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}				,		)

       UpperCAmelCase_							:       Dict       =				AutoFeatureExtractor.from_pretrained(
           F'''{USER}/test-dynamic-feature-extractor'''				,		trust_remote_code=lowerCAmelCase__						)
       # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
       self.assertEqual(new_feature_extractor.__class__.__name__				,		'CustomFeatureExtractor'						)







'''simple docstring'''


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 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





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






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				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_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				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 _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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






