"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 		UpperCamelCase			(    unittest.TestCase						):



				def 					_lowercase     (self				:      Dict)    ->	Union[str, Any]:
					__snake_case			:							Dict      		= tempfile.mkdtemp()

					__snake_case			:							Dict      		= [
					    """[UNK]""",
					    """[CLS]""",
					    """[SEP]""",
					    """[PAD]""",
					    """[MASK]""",
					    """want""",
					    """##want""",
					    """##ed""",
					    """wa""",
					    """un""",
					    """runn""",
					    """##ing""",
					    """,""",
					    """low""",
					    """lowest""",
					]
					__snake_case			:							List[Any]      		= os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES['vocab_file'])
					with open(self.vocab_file	,  'w'	,  encoding='utf-8') as vocab_writer:
						vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

					__snake_case			:							List[Any]      		= {
					    """do_resize""": True,
					    """size""": 20,
					    """do_center_crop""": True,
					    """crop_size""": 18,
					    """do_normalize""": True,
					    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
					    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
					}
					__snake_case			:							Any      		= os.path.join(self.tmpdirname	,  SCREAMING_SNAKE_CASE__)
					with open(self.image_processor_file	,  'w'	,  encoding='utf-8') as fp:
						json.dump(SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__)



				def 					_lowercase     (self				:      Optional[Any]	,  **_A				:      List[str])    ->	Tuple:
					return BertTokenizer.from_pretrained(self.tmpdirname	,  **SCREAMING_SNAKE_CASE__)



				def 					_lowercase     (self				:      List[str]	,  **_A				:      Union[str, Any])    ->	Any:
					return BertTokenizerFast.from_pretrained(self.tmpdirname	,  **SCREAMING_SNAKE_CASE__)



				def 					_lowercase     (self				:      Tuple	,  **_A				:      int)    ->	Tuple:
					return EfficientNetImageProcessor.from_pretrained(self.tmpdirname	,  **SCREAMING_SNAKE_CASE__)



				def 					_lowercase     (self				:      Dict)    ->	int:
					shutil.rmtree(self.tmpdirname)



				def 					_lowercase     (self				:      str)    ->	Dict:
					__snake_case			:							Union[str, Any]      		= [np.random.randint(2_55	,  size=(3, 30, 4_00)	,  dtype=np.uinta)]
					__snake_case			:							Tuple      		= [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__	,  0	,  -1)) for x in image_inputs]
					return image_inputs



				def 					_lowercase     (self				:      List[Any])    ->	Dict:
					__snake_case			:							List[str]      		= self.get_tokenizer()
					__snake_case			:							int      		= self.get_rust_tokenizer()
					__snake_case			:							Union[str, Any]      		= self.get_image_processor()

					__snake_case			:							Any      		= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__	,  image_processor=SCREAMING_SNAKE_CASE__)
					processor_slow.save_pretrained(self.tmpdirname)
					__snake_case			:							Any      		= AlignProcessor.from_pretrained(self.tmpdirname	,  use_fast=SCREAMING_SNAKE_CASE__)

					__snake_case			:							Optional[Any]      		= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__	,  image_processor=SCREAMING_SNAKE_CASE__)
					processor_fast.save_pretrained(self.tmpdirname)
					__snake_case			:							str      		= AlignProcessor.from_pretrained(self.tmpdirname)

					self.assertEqual(processor_slow.tokenizer.get_vocab()	,  tokenizer_slow.get_vocab())
					self.assertEqual(processor_fast.tokenizer.get_vocab()	,  tokenizer_fast.get_vocab())
					self.assertEqual(tokenizer_slow.get_vocab()	,  tokenizer_fast.get_vocab())
					self.assertIsInstance(processor_slow.tokenizer	,  SCREAMING_SNAKE_CASE__)
					self.assertIsInstance(processor_fast.tokenizer	,  SCREAMING_SNAKE_CASE__)

					self.assertEqual(processor_slow.image_processor.to_json_string()	,  image_processor.to_json_string())
					self.assertEqual(processor_fast.image_processor.to_json_string()	,  image_processor.to_json_string())
					self.assertIsInstance(processor_slow.image_processor	,  SCREAMING_SNAKE_CASE__)
					self.assertIsInstance(processor_fast.image_processor	,  SCREAMING_SNAKE_CASE__)



				def 					_lowercase     (self				:      str)    ->	Union[str, Any]:
					__snake_case			:							Any      		= AlignProcessor(tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor())
					processor.save_pretrained(self.tmpdirname)

					__snake_case			:							Any      		= self.get_tokenizer(bos_token='(BOS)'	,  eos_token='(EOS)')
					__snake_case			:							Tuple      		= self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__	,  padding_value=1.0)

					__snake_case			:							List[Any]      		= AlignProcessor.from_pretrained(
					    self.tmpdirname	,  bos_token='(BOS)'	,  eos_token='(EOS)'	,  do_normalize=SCREAMING_SNAKE_CASE__	,  padding_value=1.0)

					self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab())
					self.assertIsInstance(processor.tokenizer	,  SCREAMING_SNAKE_CASE__)

					self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string())
					self.assertIsInstance(processor.image_processor	,  SCREAMING_SNAKE_CASE__)



				def 					_lowercase     (self				:      Tuple)    ->	Dict:
					__snake_case			:							Union[str, Any]      		= self.get_image_processor()
					__snake_case			:							List[Any]      		= self.get_tokenizer()

					__snake_case			:							Optional[int]      		= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__	,  image_processor=SCREAMING_SNAKE_CASE__)

					__snake_case			:							str      		= self.prepare_image_inputs()

					__snake_case			:							List[Any]      		= image_processor(SCREAMING_SNAKE_CASE__	,  return_tensors='np')
					__snake_case			:							Any      		= processor(images=SCREAMING_SNAKE_CASE__	,  return_tensors='np')

					for key in input_image_proc.keys():
						self.assertAlmostEqual(input_image_proc[key].sum()	,  input_processor[key].sum()	,  delta=1E-2)



				def 					_lowercase     (self				:      Any)    ->	Union[str, Any]:
					__snake_case			:							List[str]      		= self.get_image_processor()
					__snake_case			:							Dict      		= self.get_tokenizer()

					__snake_case			:							Any      		= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__	,  image_processor=SCREAMING_SNAKE_CASE__)

					__snake_case			:							str      		= """lower newer"""

					__snake_case			:							Optional[int]      		= processor(text=SCREAMING_SNAKE_CASE__)

					__snake_case			:							Dict      		= tokenizer(SCREAMING_SNAKE_CASE__	,  padding='max_length'	,  max_length=64)

					for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]	,  encoded_processor[key])



				def 					_lowercase     (self				:      Optional[Any])    ->	List[str]:
					__snake_case			:							Optional[Any]      		= self.get_image_processor()
					__snake_case			:							Union[str, Any]      		= self.get_tokenizer()

					__snake_case			:							Optional[int]      		= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__	,  image_processor=SCREAMING_SNAKE_CASE__)

					__snake_case			:							Optional[int]      		= """lower newer"""
					__snake_case			:							int      		= self.prepare_image_inputs()

					__snake_case			:							str      		= processor(text=SCREAMING_SNAKE_CASE__	,  images=SCREAMING_SNAKE_CASE__)

					self.assertListEqual(list(inputs.keys())	,  ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'])

					# test if it raises when no input is passed
					with pytest.raises(SCREAMING_SNAKE_CASE__):
						processor()



				def 					_lowercase     (self				:      int)    ->	Union[str, Any]:
					__snake_case			:							Tuple      		= self.get_image_processor()
					__snake_case			:							List[str]      		= self.get_tokenizer()

					__snake_case			:							Optional[int]      		= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__	,  image_processor=SCREAMING_SNAKE_CASE__)

					__snake_case			:							Optional[int]      		= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__snake_case			:							Dict      		= processor.batch_decode(SCREAMING_SNAKE_CASE__)
					__snake_case			:							List[str]      		= tokenizer.batch_decode(SCREAMING_SNAKE_CASE__)

					self.assertListEqual(SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__)






				def 					_lowercase     (self				:      List[str])    ->	Optional[int]:
					__snake_case			:							List[Any]      		= self.get_image_processor()
					__snake_case			:							int      		= self.get_tokenizer()

					__snake_case			:							List[str]      		= AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__	,  image_processor=SCREAMING_SNAKE_CASE__)

					__snake_case			:							str      		= """lower newer"""
					__snake_case			:							Dict      		= self.prepare_image_inputs()

					__snake_case			:							List[str]      		= processor(text=SCREAMING_SNAKE_CASE__	,  images=SCREAMING_SNAKE_CASE__)

					self.assertListEqual(list(inputs.keys())	,  processor.model_input_names)







"""simple docstring"""




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 lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """\n""".join(_snake_case						)
							Path(_snake_case						).open("""w"""						).writelines(_snake_case						)


UpperCAmelCase__      :    Union[str, Any]    							= 'patrickvonplaten/t5-tiny-random'
UpperCAmelCase__      :    Optional[int]    							= 'sshleifer/bart-tiny-random'
UpperCAmelCase__      :    Dict    							= 'sshleifer/tiny-mbart'

UpperCAmelCase__      :    int    							= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__						:       Any           =   str(Path(self.get_auto_remove_tmp_dir()    ) / """scores.json"""    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   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(SCREAMING_SNAKE_CASE__   ,       """argv"""   ,       SCREAMING_SNAKE_CASE__    ):
																		run_generate()
																		assert Path(SCREAMING_SNAKE_CASE__    ).exists()
																		# os.remove(Path(output_file_name))



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([BART_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([T5_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   Path(self.get_auto_remove_tmp_dir()    ) / """utest_input.source"""
											SCREAMING_SNAKE_CASE__						:       int           =   input_file_name.parent / """utest_output.txt"""
											assert not output_file_name.exists()

											SCREAMING_SNAKE_CASE__						:       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__						:       List[str]           =   Path(self.get_auto_remove_tmp_dir()    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """scores.json"""    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """val.target"""    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""en"""]    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""de"""]    )
											SCREAMING_SNAKE_CASE__						:       str           =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   F'''
            run_eval_search.py
            {model}
            {str(SCREAMING_SNAKE_CASE__    )}
            {str(SCREAMING_SNAKE_CASE__    )}
            --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(SCREAMING_SNAKE_CASE__   ,       """argv"""   ,       SCREAMING_SNAKE_CASE__    ):
																		with CaptureStdout() as cs:
																									run_search()
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [""" num_beams | length_penalty""", model, """Best score args"""]
																		SCREAMING_SNAKE_CASE__						:       Any           =   ["""Info"""]
																		if "translation" in task:
																									expected_strings.append("""bleu"""    )
																		else:
																									expected_strings.extend(SCREAMING_SNAKE_CASE__    )
																		for w in expected_strings:
																									assert w in cs.out
																		for w in un_expected_strings:
																									assert w not in cs.out
																		assert Path(SCREAMING_SNAKE_CASE__    ).exists()
																		os.remove(Path(SCREAMING_SNAKE_CASE__    )    )




