
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	:    Optional[int]          =      get_tests_dir('''fixtures''')




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      mock.Mock()
				_snake_case          =      500
				_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=lowercase     ) 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 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      ViTImageProcessor.from_pretrained(
				    'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'     )





	def 			A							(  self     :   str     ):


				'''simple docstring'''


				with self.assertRaises(lowercase     ):
							# 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(lowercase     )




@is_staging_test
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	@classmethod
	def 			A							(  cls     :   int     ):


				'''simple docstring'''


				_snake_case          =      TOKEN
				HfFolder.save_token(lowercase     )



	@classmethod
	def 			A							(  cls     :   Optional[Any]     ):


				'''simple docstring'''


				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 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      ViTImageProcessor.from_pretrained(lowercase     )
				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(lowercase      ,  getattr(lowercase      ,  lowercase     )     )

				# 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(
							    lowercase      ,  repo_id='test-image-processor'      ,  push_to_hub=lowercase      ,  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(lowercase      ,  getattr(lowercase      ,  lowercase     )     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      ViTImageProcessor.from_pretrained(lowercase     )
				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(lowercase      ,  getattr(lowercase      ,  lowercase     )     )

				# 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(
							    lowercase      ,  repo_id='valid_org/test-image-processor-org'      ,  push_to_hub=lowercase      ,  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(lowercase      ,  getattr(lowercase      ,  lowercase     )     )





	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				CustomImageProcessor.register_for_auto_class()
				_snake_case          =      CustomImageProcessor.from_pretrained(lowercase     )

				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=lowercase     )
				# 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'     )

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_lowerCamelCase	:    Union[str, Any]          =      logging.getLogger()






def 	a_		(	__lowercase       :     Path	, __lowercase       :     list			)			->  Tuple:
			_snake_case          =      '\n'.join(__lowercase			)
			Path(__lowercase			).open('w'			).writelines(__lowercase			)


_lowerCamelCase	:    Any          =      '''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/bart-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/tiny-mbart'''

_lowerCamelCase	:    Union[str, Any]          =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()
				_snake_case          =      [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
				_dump_articles(lowercase      ,  lowercase     )

				_snake_case          =      str(Path(self.get_auto_remove_tmp_dir()     ) / 'scores.json'     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							run_generate()
							assert Path(lowercase     ).exists()
							# os.remove(Path(output_file_name))



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )



	@parameterized.expand([BART_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )





	@parameterized.expand([T5_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()

				_snake_case          =      {
				    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
				    'de': [
				        'Maschinelles Lernen ist großartig, oder?',
				        'Ich esse gerne Bananen',
				        'Morgen ist wieder ein toller Tag!',
				    ],
				}

				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     )
				_snake_case          =      str(tmp_dir / 'scores.json'     )
				_snake_case          =      str(tmp_dir / 'val.target'     )
				_dump_articles(lowercase      ,  text['en']     )
				_dump_articles(lowercase      ,  text['de']     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {str(lowercase     )}
            {str(lowercase     )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
				testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']     )

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							with CaptureStdout() as cs:
										run_search()
							_snake_case          =      [' num_beams | length_penalty', model, 'Best score args']
							_snake_case          =      ['Info']
							if "translation" in task:
										expected_strings.append('bleu'     )
							else:
										expected_strings.extend(lowercase     )
							for w in expected_strings:
										assert w in cs.out
							for w in un_expected_strings:
										assert w not in cs.out
							assert Path(lowercase     ).exists()
							os.remove(Path(lowercase     )     )