




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class       a__   ( unittest.TestCase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	tempfile.mkdtemp()

											__lowerCAmelCase           =	BlipImageProcessor()
											__lowerCAmelCase           =	GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"      )

											__lowerCAmelCase           =	BlipaProcessor(_A		,      _A      )

											processor.save_pretrained(self.tmpdirname      )






						def     __SCREAMING_SNAKE_CASE(       self		,      **_A      ):




											"""simple docstring"""



											return AutoProcessor.from_pretrained(self.tmpdirname		,      **_A      ).tokenizer






						def     __SCREAMING_SNAKE_CASE(       self		,      **_A      ):




											"""simple docstring"""



											return AutoProcessor.from_pretrained(self.tmpdirname		,      **_A      ).image_processor






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											shutil.rmtree(self.tmpdirname      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta      )]

											__lowerCAmelCase           =	[Image.fromarray(np.moveaxis(_A		,      0		,      -1      )      ) for x in image_inputs]

											return image_inputs






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	BlipaProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()      )
											processor.save_pretrained(self.tmpdirname      )

											__lowerCAmelCase           =	self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"      )
											__lowerCAmelCase           =	self.get_image_processor(do_normalize=_A		,      padding_value=1.0      )

											__lowerCAmelCase           =	BlipaProcessor.from_pretrained(
											    self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=_A		,      padding_value=1.0      )

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

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_image_processor()
											__lowerCAmelCase           =	self.get_tokenizer()

											__lowerCAmelCase           =	BlipaProcessor(tokenizer=_A		,      image_processor=_A      )

											__lowerCAmelCase           =	self.prepare_image_inputs()

											__lowerCAmelCase           =	image_processor(_A		,      return_tensors="np"      )
											__lowerCAmelCase           =	processor(images=_A		,      return_tensors="np"      )

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_image_processor()
											__lowerCAmelCase           =	self.get_tokenizer()

											__lowerCAmelCase           =	BlipaProcessor(tokenizer=_A		,      image_processor=_A      )

											__lowerCAmelCase           =	"lower newer"

											__lowerCAmelCase           =	processor(text=_A      )

											__lowerCAmelCase           =	tokenizer(_A		,      return_token_type_ids=_A      )

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_image_processor()
											__lowerCAmelCase           =	self.get_tokenizer()

											__lowerCAmelCase           =	BlipaProcessor(tokenizer=_A		,      image_processor=_A      )

											__lowerCAmelCase           =	"lower newer"
											__lowerCAmelCase           =	self.prepare_image_inputs()

											__lowerCAmelCase           =	processor(text=_A		,      images=_A      )

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

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_image_processor()
											__lowerCAmelCase           =	self.get_tokenizer()

											__lowerCAmelCase           =	BlipaProcessor(tokenizer=_A		,      image_processor=_A      )

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

											__lowerCAmelCase           =	processor.batch_decode(_A      )
											__lowerCAmelCase           =	tokenizer.batch_decode(_A      )

											self.assertListEqual(_A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_image_processor()
											__lowerCAmelCase           =	self.get_tokenizer()

											__lowerCAmelCase           =	BlipaProcessor(tokenizer=_A		,      image_processor=_A      )

											__lowerCAmelCase           =	"lower newer"
											__lowerCAmelCase           =	self.prepare_image_inputs()

											__lowerCAmelCase           =	processor(text=_A		,      images=_A      )

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys()      )		,      ["pixel_values", "input_ids", "attention_mask"]      )










from pathlib import Path

import fire
from tqdm import tqdm







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict="ro"				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]="en"				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]="wmt16"				,				SCREAMING_SNAKE_CASE_							:    List[str]=None					):
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError("run pip install datasets"					)
					__lowerCAmelCase           =	F"""{src_lang}-{tgt_lang}"""
					print(F"""Converting {dataset}-{pair}"""					)
					__lowerCAmelCase           =	datasets.load_dataset(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					if save_dir is None:
										__lowerCAmelCase           =	F"""{dataset}-{pair}"""
					__lowerCAmelCase           =	Path(SCREAMING_SNAKE_CASE_					)
					save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)

					for split in ds.keys():
										print(F"""Splitting {split} with {ds[split].num_rows} records"""					)

										# to save to val.source, val.target like summary datasets
										__lowerCAmelCase           =	"val" if split == "validation" else split
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.source"""					)
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.target"""					)
										__lowerCAmelCase           =	src_path.open("w+"					)
										__lowerCAmelCase           =	tgt_path.open("w+"					)

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]					):
															__lowerCAmelCase           =	x["translation"]
															src_fp.write(ex[src_lang] + "\n"					)
															tgt_fp.write(ex[tgt_lang] + "\n"					)

					print(F"""Saved {dataset} dataset to {save_dir}"""					)


if __name__ == "__main__":
					fire.Fire(download_wmt_dataset)




