

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 AutoImageProcessor, ViTImageProcessor
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_image_processing import CustomImageProcessor  # noqa E402


lowerCAmelCase_       	=	get_tests_dir('''fixtures''')





class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Union[str, Any]							)      ->Dict:
									# A mock response for an HTTP head request to emulate server down
									snake_case_   =	mock.Mock()
									snake_case_   =	5_0_0
									snake_case_   =	{}
									snake_case_   =	HTTPError
									snake_case_   =	{}

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







			def 				snake_case__(     self :				List[str]							)      ->Optional[int]:
									# This test is for deprecated behavior and can be removed in v5
									snake_case_   =	ViTImageProcessor.from_pretrained(
									    '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''							)







			def 				snake_case__(     self :				Any							)      ->List[str]:
									with self.assertRaises(_UpperCamelCase							):
															# config is in subfolder, the following should not work without specifying the subfolder
															snake_case_   =	AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''							)

									snake_case_   =	AutoImageProcessor.from_pretrained(
									    '''hf-internal-testing/stable-diffusion-all-variants'''							,   subfolder='''feature_extractor'''							)

									self.assertIsNotNone(_UpperCamelCase							)






@is_staging_test
class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			@classmethod
			def 				snake_case__(     cls :				List[Any]							)      ->Dict:
									snake_case_   =	TOKEN
									HfFolder.save_token(_UpperCamelCase							)







			@classmethod
			def 				snake_case__(     cls :				List[Any]							)      ->Optional[int]:
									try:
															delete_repo(token=cls._token							,   repo_id='''test-image-processor'''							)
									except HTTPError:
															pass

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

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







			def 				snake_case__(     self :				Any							)      ->List[Any]:
									snake_case_   =	ViTImageProcessor.from_pretrained(_UpperCamelCase							)
									image_processor.push_to_hub('''test-image-processor'''							,   use_auth_token=self._token							)

									snake_case_   =	ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''							)
									for k, v in image_processor.__dict__.items():
															self.assertEqual(_UpperCamelCase							,   getattr(_UpperCamelCase							,   _UpperCamelCase							)							)

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

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    _UpperCamelCase							,   repo_id='''test-image-processor'''							,   push_to_hub=_UpperCamelCase							,   use_auth_token=self._token							)

									snake_case_   =	ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''							)
									for k, v in image_processor.__dict__.items():
															self.assertEqual(_UpperCamelCase							,   getattr(_UpperCamelCase							,   _UpperCamelCase							)							)







			def 				snake_case__(     self :				str							)      ->List[str]:
									snake_case_   =	ViTImageProcessor.from_pretrained(_UpperCamelCase							)
									image_processor.push_to_hub('''valid_org/test-image-processor'''							,   use_auth_token=self._token							)

									snake_case_   =	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''							)
									for k, v in image_processor.__dict__.items():
															self.assertEqual(_UpperCamelCase							,   getattr(_UpperCamelCase							,   _UpperCamelCase							)							)

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

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    _UpperCamelCase							,   repo_id='''valid_org/test-image-processor-org'''							,   push_to_hub=_UpperCamelCase							,   use_auth_token=self._token							)

									snake_case_   =	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''							)
									for k, v in image_processor.__dict__.items():
															self.assertEqual(_UpperCamelCase							,   getattr(_UpperCamelCase							,   _UpperCamelCase							)							)







			def 				snake_case__(     self :				List[str]							)      ->Tuple:
									CustomImageProcessor.register_for_auto_class()
									snake_case_   =	CustomImageProcessor.from_pretrained(_UpperCamelCase							)

									image_processor.push_to_hub('''test-dynamic-image-processor'''							,   use_auth_token=self._token							)

									# This has added the proper auto_map field to the config
									self.assertDictEqual(
									    image_processor.auto_map							,   {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}							,   )

									snake_case_   =	AutoImageProcessor.from_pretrained(
									    f'''{USER}/test-dynamic-image-processor'''							,   trust_remote_code=_UpperCamelCase							)
									# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
									self.assertEqual(new_image_processor.__class__.__name__							,   '''CustomImageProcessor'''							)


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)