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)
__UpperCamelCase		:       List[Any]									=     logging.getLogger()
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    List[str]							=							'''\n'''.join(__A						)
						Path(__A						).open('''w'''						).writelines(__A						)


__UpperCamelCase		:       List[Any]									=     "patrickvonplaten/t5-tiny-random"
__UpperCamelCase		:       Optional[int]									=     "sshleifer/bart-tiny-random"
__UpperCamelCase		:       Dict									=     "sshleifer/tiny-mbart"

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





class 					lowercase__						(   _lowerCamelCase):


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				Tuple						):





												'''simple docstring'''





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

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

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


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												self.run_eval_tester(lowerCAmelCase_						)


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





												'''simple docstring'''





												self.run_eval_tester(lowerCAmelCase_						)



						@parameterized.expand([T5_TINY, MBART_TINY]						)
						@slow
						def        __A       (       self	:				str    ,		UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							Path(self.get_auto_remove_tmp_dir()						) / '''utest_input.source'''
												SCREAMING_SNAKE_CASE							:    Dict							=							input_file_name.parent / '''utest_output.txt'''
												assert not output_file_name.exists()

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							{
												    '''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!''',
												    ],
												}

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

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







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :							List[str]      =					{
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Optional[int]      =					["Pix2StructImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Tuple      =					[
					    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Pix2StructPreTrainedModel",
					    "Pix2StructForConditionalGeneration",
					    "Pix2StructVisionModel",
					    "Pix2StructTextModel",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					lowercase    :							List[str]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

