


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


SCREAMING_SNAKE_CASE__     					=			get_tests_dir("fixtures")



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						mock.Mock()
				SCREAMING_SNAKE_CASE_				:List[str]				     =						5_00
				SCREAMING_SNAKE_CASE_				:Any				     =						{}
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						HTTPError
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						{}

				# Download this model to make sure it's in the cache.
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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=_lowercase) as mock_head:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
						# This check we did call the fake head request
						mock_head.assert_called()







		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						WavaVecaFeatureExtractor.from_pretrained(
				    "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json")




@is_staging_test
class 						_UpperCAmelCase      (     unittest.TestCase							):






		@classmethod
		def _snake_case		(						cls				:     str):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						TOKEN
				HfFolder.save_token(_lowercase)






		@classmethod
		def _snake_case		(						cls				:     Dict):



				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 _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Any				     =						WavaVecaFeatureExtractor.from_pretrained(_lowercase)
				feature_extractor.push_to_hub("test-feature-extractor"   ,  use_auth_token=self._token)

				SCREAMING_SNAKE_CASE_				:int				     =						WavaVecaFeatureExtractor.from_pretrained(F"{USER}/test-feature-extractor")
				for k, v in feature_extractor.__dict__.items():
						self.assertEqual(_lowercase   ,  getattr(_lowercase   ,  _lowercase))

				# 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(
						    _lowercase   ,  repo_id="test-feature-extractor"   ,  push_to_hub=_lowercase   ,  use_auth_token=self._token)

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						WavaVecaFeatureExtractor.from_pretrained(F"{USER}/test-feature-extractor")
				for k, v in feature_extractor.__dict__.items():
						self.assertEqual(_lowercase   ,  getattr(_lowercase   ,  _lowercase))






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_				:int				     =						WavaVecaFeatureExtractor.from_pretrained(_lowercase)
				feature_extractor.push_to_hub("valid_org/test-feature-extractor"   ,  use_auth_token=self._token)

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

				# 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(
						    _lowercase   ,  repo_id="valid_org/test-feature-extractor-org"   ,  push_to_hub=_lowercase   ,  use_auth_token=self._token)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org")
				for k, v in feature_extractor.__dict__.items():
						self.assertEqual(_lowercase   ,  getattr(_lowercase   ,  _lowercase))







		def _snake_case		(						self				:     Tuple):
				CustomFeatureExtractor.register_for_auto_class()
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						CustomFeatureExtractor.from_pretrained(_lowercase)

				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"}   ,  )

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						AutoFeatureExtractor.from_pretrained(
				    F"{USER}/test-dynamic-feature-extractor"   ,  trust_remote_code=_lowercase)
				# 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'''



from itertools import product


def A (__lowerCamelCase :int					,      __lowerCamelCase :int						):
						_lowerCAmelCase    		=					sides_number
						_lowerCAmelCase    		=					max_face_number * dice_number
						_lowerCAmelCase    		=					[0] * (max_total + 1)

						_lowerCAmelCase    		=					1
						_lowerCAmelCase    		=					range(__lowerCamelCase					,      max_face_number + 1						)
						for dice_numbers in product(__lowerCamelCase					,      repeat=__lowerCamelCase						):
												_lowerCAmelCase    		=					sum(__lowerCamelCase						)
												totals_frequencies[total] += 1

						return totals_frequencies


def A ():
						_lowerCAmelCase    		=					total_frequency_distribution(
						    sides_number=4					,      dice_number=9						)
						_lowerCAmelCase    		=					total_frequency_distribution(
						    sides_number=6					,      dice_number=6						)

						_lowerCAmelCase    		=					0
						_lowerCAmelCase    		=					9
						_lowerCAmelCase    		=					4 * 9
						_lowerCAmelCase    		=					6
						for peter_total in range(__lowerCamelCase					,      max_peter_total + 1						):
												peter_wins_count += peter_totals_frequencies[peter_total] * sum(
												    colin_totals_frequencies[min_colin_total:peter_total]						)

						_lowerCAmelCase    		=					(4**9) * (6**6)
						_lowerCAmelCase    		=					peter_wins_count / total_games_number

						_lowerCAmelCase    		=					round(__lowerCamelCase					,      ndigits=7						)

						return rounded_peter_win_probability


if __name__ == "__main__":
						print(F"""{solution() = }""")
