
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)
_snake_case      :							str     	=    logging.getLogger()

def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      "\n".join(__UpperCAmelCase						)
				Path(__UpperCAmelCase						).open("w"						).writelines(__UpperCAmelCase						)


_snake_case      :							Union[str, Any]     	=    'patrickvonplaten/t5-tiny-random'
_snake_case      :							Dict     	=    'sshleifer/bart-tiny-random'
_snake_case      :							List[str]     	=    'sshleifer/tiny-mbart'

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





class   UpperCamelCase_      (      _lowerCamelCase		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:int		,    lowerCAmelCase__							:Tuple     )  ->Dict:
										lowercase         =      Path(self.get_auto_remove_tmp_dir()     ) / "utest_input.source"
										lowercase         =      input_file_name.parent / "utest_output.txt"
										assert not output_file_name.exists()
										lowercase         =      [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
										_dump_articles(lowercase_		,    lowercase_     )

										lowercase         =      str(Path(self.get_auto_remove_tmp_dir()     ) / "scores.json"     )
										lowercase         =      "translation_en_to_de" if model == T5_TINY else "summarization"
										lowercase         =      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(lowercase_		,    "argv"		,    lowercase_     ):
														run_generate()
														assert Path(lowercase_     ).exists()
														# os.remove(Path(output_file_name))





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Any:
										self.run_eval_tester(lowercase_     )





						@parameterized.expand([BART_TINY, MBART_TINY]     )
						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:int     )  ->Union[str, Any]:
										self.run_eval_tester(lowercase_     )





						@parameterized.expand([T5_TINY, MBART_TINY]     )
						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Dict     )  ->str:
										lowercase         =      Path(self.get_auto_remove_tmp_dir()     ) / "utest_input.source"
										lowercase         =      input_file_name.parent / "utest_output.txt"
										assert not output_file_name.exists()

										lowercase         =      {
										    "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!",
										    ],
										}

										lowercase         =      Path(self.get_auto_remove_tmp_dir()     )
										lowercase         =      str(tmp_dir / "scores.json"     )
										lowercase         =      str(tmp_dir / "val.target"     )
										_dump_articles(lowercase_		,    text["en"]     )
										_dump_articles(lowercase_		,    text["de"]     )
										lowercase         =      "translation_en_to_de" if model == T5_TINY else "summarization"
										lowercase         =      F'''\n            run_eval_search.py\n            {model}\n            {str(lowercase_     )}\n            {str(lowercase_     )}\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(lowercase_		,    "argv"		,    lowercase_     ):
														with CaptureStdout() as cs:
																		run_search()
														lowercase         =      [" num_beams | length_penalty", model, "Best score args"]
														lowercase         =      ["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_     )     )






'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 		a							(  unittest.TestCase	):
				snake_case_           =						ViTImageProcessor if is_vision_available() else None


				@property
				def     A_					(						self       :   List[str]   ):
									return self.image_processor_tester.prepare_image_processor_dict()


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							(3, 32, 128)
									snake_case_		     =							tempfile.mkdtemp()

									# fmt: off
									snake_case_		     =							['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
									# fmt: on
									snake_case_		     =							dict(zip(lowercase_			,							range(len(lowercase_   )   )   )   )

									snake_case_		     =							os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES['''vocab_file''']   )
									with open(self.vocab_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														fp.write(json.dumps(lowercase_   ) + '''\n'''   )

									snake_case_		     =							{
									    '''do_normalize''': False,
									    '''do_resize''': True,
									    '''image_processor_type''': '''ViTImageProcessor''',
									    '''resample''': 3,
									    '''size''': {'''height''': 32, '''width''': 128},
									}
									snake_case_		     =							os.path.join(self.tmpdirname			,							lowercase_   )
									with open(self.image_processor_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														json.dump(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any			,							**lowercase_       :   str   ):
									return MgpstrTokenizer.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   List[str]			,							**lowercase_       :   List[Any]   ):
									return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									shutil.rmtree(self.tmpdirname   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta   )

									snake_case_		     =							Image.fromarray(np.moveaxis(lowercase_			,							0			,							-1   )   )

									return image_input


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )
									snake_case_		     =							MgpstrProcessor.from_pretrained(self.tmpdirname			,							use_fast=lowercase_   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

									self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()   )
									self.assertIsInstance(processor.image_processor			,							lowercase_   )


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )

									snake_case_		     =							self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''   )
									snake_case_		     =							self.get_image_processor(do_normalize=lowercase_			,							padding_value=1.0   )

									snake_case_		     =							MgpstrProcessor.from_pretrained(
									    self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=lowercase_			,							padding_value=1.0   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

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


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							image_processor(lowercase_			,							return_tensors='''np'''   )
									snake_case_		     =							processor(images=lowercase_			,							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     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''

									snake_case_		     =							processor(text=lowercase_   )

									snake_case_		     =							tokenizer(lowercase_   )
									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							['''pixel_values''', '''labels''']   )

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


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

									snake_case_		     =							processor.char_decode(lowercase_   )
									snake_case_		     =							tokenizer.batch_decode(lowercase_   )
									snake_case_		     =							[seq.replace(''' '''			,							''''''   ) for seq in decoded_tok]

									self.assertListEqual(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							None
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

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


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							torch.randn(1			,							27			,							38   )
									snake_case_		     =							torch.randn(1			,							27			,							5_0257   )
									snake_case_		     =							torch.randn(1			,							27			,							3_0522   )

									snake_case_		     =							processor.batch_decode([char_input, bpe_input, wp_input]   )

									self.assertListEqual(list(results.keys()   )			,							['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']   )

