

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPImageProcessor, CLIPProcessor



@require_vision
class SCREAMING_SNAKE_CASE      (    unittest.TestCase					):
  """simple docstring"""






  def 			SCREAMING_SNAKE_CASE       (     self					:       str       )				->      Any:






     """simple docstring"""







     __lowerCAmelCase    :     str          =		tempfile.mkdtemp()

     # fmt: off
     __lowerCAmelCase    :     str          =		["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
     # fmt: on
     __lowerCAmelCase    :     Any          =		dict(zip(__snake_case			,				range(len(__snake_case       )       )       )       )
     __lowerCAmelCase    :     Union[str, Any]          =		["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
     __lowerCAmelCase    :     Any          =		{"""unk_token""": """<unk>"""}

     __lowerCAmelCase    :     List[Any]          =		os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES["""vocab_file"""]       )
     __lowerCAmelCase    :     Dict          =		os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES["""merges_file"""]       )
     with open(self.vocab_file			,				"""w"""			,				encoding="""utf-8"""       ) as fp:
        fp.write(json.dumps(__snake_case       ) + """\n"""       )
     with open(self.merges_file			,				"""w"""			,				encoding="""utf-8"""       ) as fp:
        fp.write("""\n""".join(__snake_case       )       )

     __lowerCAmelCase    :     List[str]          =		{
         """do_resize""": True,
         """size""": 20,
         """do_center_crop""": True,
         """crop_size""": 18,
         """do_normalize""": True,
         """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
         """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
     }
     __lowerCAmelCase    :     str          =		os.path.join(self.tmpdirname			,				__snake_case       )
     with open(self.image_processor_file			,				"""w"""			,				encoding="""utf-8"""       ) as fp:
        json.dump(__snake_case			,				__snake_case       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Dict			,				**lowerCAmelCase					:       Any       )				->      Any:






     """simple docstring"""







     return CLIPTokenizer.from_pretrained(self.tmpdirname			,				**__snake_case       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       List[Any]			,				**lowerCAmelCase					:       List[Any]       )				->      List[Any]:






     """simple docstring"""







     return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,				**__snake_case       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Tuple			,				**lowerCAmelCase					:       List[Any]       )				->      str:






     """simple docstring"""







     return CLIPImageProcessor.from_pretrained(self.tmpdirname			,				**__snake_case       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[int]       )				->      Dict:






     """simple docstring"""







     shutil.rmtree(self.tmpdirname       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Any       )				->      Dict:






     """simple docstring"""







     __lowerCAmelCase    :     Dict          =		[np.random.randint(2_55			,				size=(3, 30, 4_00)			,				dtype=np.uinta       )]

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

     return image_inputs







  def 			SCREAMING_SNAKE_CASE       (     self					:       Tuple       )				->      int:






     """simple docstring"""







     __lowerCAmelCase    :     Optional[int]          =		self.get_tokenizer()
     __lowerCAmelCase    :     List[Any]          =		self.get_rust_tokenizer()
     __lowerCAmelCase    :     Union[str, Any]          =		self.get_image_processor()

     __lowerCAmelCase    :     Optional[Any]          =		CLIPProcessor(tokenizer=__snake_case			,				image_processor=__snake_case       )
     processor_slow.save_pretrained(self.tmpdirname       )
     __lowerCAmelCase    :     int          =		CLIPProcessor.from_pretrained(self.tmpdirname			,				use_fast=__snake_case       )

     __lowerCAmelCase    :     Optional[Any]          =		CLIPProcessor(tokenizer=__snake_case			,				image_processor=__snake_case       )
     processor_fast.save_pretrained(self.tmpdirname       )
     __lowerCAmelCase    :     List[Any]          =		CLIPProcessor.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			,				__snake_case       )
     self.assertIsInstance(processor_fast.tokenizer			,				__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			,				__snake_case       )
     self.assertIsInstance(processor_fast.image_processor			,				__snake_case       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Tuple       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     int          =		CLIPProcessor(tokenizer=self.get_tokenizer()			,				image_processor=self.get_image_processor()       )
     processor.save_pretrained(self.tmpdirname       )

     __lowerCAmelCase    :     Any          =		self.get_tokenizer(bos_token="""(BOS)"""			,				eos_token="""(EOS)"""       )
     __lowerCAmelCase    :     Dict          =		self.get_image_processor(do_normalize=__snake_case			,				padding_value=1.0       )

     __lowerCAmelCase    :     Tuple          =		CLIPProcessor.from_pretrained(
         self.tmpdirname			,				bos_token="""(BOS)"""			,				eos_token="""(EOS)"""			,				do_normalize=__snake_case			,				padding_value=1.0       )

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

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







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      List[Any]:






     """simple docstring"""







     __lowerCAmelCase    :     List[Any]          =		self.get_image_processor()
     __lowerCAmelCase    :     int          =		self.get_tokenizer()

     __lowerCAmelCase    :     Dict          =		CLIPProcessor(tokenizer=__snake_case			,				image_processor=__snake_case       )

     __lowerCAmelCase    :     int          =		self.prepare_image_inputs()

     __lowerCAmelCase    :     Union[str, Any]          =		image_processor(__snake_case			,				return_tensors="""np"""       )
     __lowerCAmelCase    :     Any          =		processor(images=__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 			SCREAMING_SNAKE_CASE       (     self					:       Tuple       )				->      List[Any]:






     """simple docstring"""







     __lowerCAmelCase    :     str          =		self.get_image_processor()
     __lowerCAmelCase    :     Tuple          =		self.get_tokenizer()

     __lowerCAmelCase    :     List[str]          =		CLIPProcessor(tokenizer=__snake_case			,				image_processor=__snake_case       )

     __lowerCAmelCase    :     Any          =		"""lower newer"""

     __lowerCAmelCase    :     Tuple          =		processor(text=__snake_case       )

     __lowerCAmelCase    :     Dict          =		tokenizer(__snake_case       )

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







  def 			SCREAMING_SNAKE_CASE       (     self					:       str       )				->      Any:






     """simple docstring"""







     __lowerCAmelCase    :     str          =		self.get_image_processor()
     __lowerCAmelCase    :     List[str]          =		self.get_tokenizer()

     __lowerCAmelCase    :     Optional[Any]          =		CLIPProcessor(tokenizer=__snake_case			,				image_processor=__snake_case       )

     __lowerCAmelCase    :     List[Any]          =		"""lower newer"""
     __lowerCAmelCase    :     Optional[Any]          =		self.prepare_image_inputs()

     __lowerCAmelCase    :     Optional[Any]          =		processor(text=__snake_case			,				images=__snake_case       )

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

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







  def 			SCREAMING_SNAKE_CASE       (     self					:       Tuple       )				->      Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase    :     Dict          =		self.get_image_processor()
     __lowerCAmelCase    :     str          =		self.get_tokenizer()

     __lowerCAmelCase    :     int          =		CLIPProcessor(tokenizer=__snake_case			,				image_processor=__snake_case       )

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

     __lowerCAmelCase    :     Optional[Any]          =		processor.batch_decode(__snake_case       )
     __lowerCAmelCase    :     Dict          =		tokenizer.batch_decode(__snake_case       )

     self.assertListEqual(__snake_case			,				__snake_case       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      List[Any]:






     """simple docstring"""







     __lowerCAmelCase    :     Tuple          =		self.get_image_processor()
     __lowerCAmelCase    :     Any          =		self.get_tokenizer()

     __lowerCAmelCase    :     List[str]          =		CLIPProcessor(tokenizer=__snake_case			,				image_processor=__snake_case       )

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

     __lowerCAmelCase    :     int          =		processor(text=__snake_case			,				images=__snake_case       )

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








from pathlib import Path

import fire
from tqdm import tqdm




def 		lowerCAmelCase_ (						lowerCamelCase="ro"						,  lowerCamelCase="en"						,  lowerCamelCase="wmt16"						,  lowerCamelCase=None ):
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("""run pip install datasets""" )
				__magic_name__						:						Dict										=F"{src_lang}-{tgt_lang}"
				print(F"Converting {dataset}-{pair}" )
				__magic_name__						:						Dict										=datasets.load_dataset(lowerCamelCase						,  lowerCamelCase )
				if save_dir is None:
								__magic_name__						:						Optional[int]										=F"{dataset}-{pair}"
				__magic_name__						:						int										=Path(lowerCamelCase )
				save_dir.mkdir(exist_ok=lowerCamelCase )

				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
								__magic_name__						:						Dict										="""val""" if split == """validation""" else split
								__magic_name__						:						List[Any]										=save_dir.joinpath(F"{fn}.source" )
								__magic_name__						:						Optional[int]										=save_dir.joinpath(F"{fn}.target" )
								__magic_name__						:						Optional[Any]										=src_path.open("""w+""" )
								__magic_name__						:						List[Any]										=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] ):
												__magic_name__						:						str										=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)




