




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							UpperCAmelCase_		:					int          =  False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor




class 		__A   (      unittest.TestCase      ):

						def __init__(		self :Optional[Any]     ,			__snake_case :Dict     ,			__snake_case :List[str]=7     ,			__snake_case :Union[str, Any]=3     ,			__snake_case :int=18     ,			__snake_case :str=30     ,			__snake_case :int=4_00     ,			__snake_case :Dict=None     ,			__snake_case :Tuple=True     ,			__snake_case :Any=True     ,			__snake_case :Tuple=None     ,			):
										'''simple docstring'''

										__magic_name__						:						str										=size if size is not None else {"""height""": 20, """width""": 20}
										__magic_name__						:						Optional[Any]										=parent
										__magic_name__						:						Optional[Any]										=batch_size
										__magic_name__						:						Optional[int]										=num_channels
										__magic_name__						:						List[Any]										=image_size
										__magic_name__						:						Optional[Any]										=min_resolution
										__magic_name__						:						str										=max_resolution
										__magic_name__						:						List[Any]										=size
										__magic_name__						:						Any										=do_normalize
										__magic_name__						:						int										=do_convert_rgb
										__magic_name__						:						Any										=[5_12, 10_24, 20_48, 40_96]
										__magic_name__						:						List[Any]										=patch_size if patch_size is not None else {"""height""": 16, """width""": 16}

						def      A__    (		self :Dict			):
										'''simple docstring'''

										return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}


						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										="""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
										__magic_name__						:						Any										=Image.open(requests.get(__snake_case     ,			stream=__snake_case			).raw			).convert("""RGB"""			)
										return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,    reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""       ,    )
@require_torch
@require_vision
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       PixaStructImageProcessor if is_vision_available() else None

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=PixaStructImageProcessingTester(self			)

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(__snake_case     ,			"""do_normalize"""			)			)
										self.assertTrue(hasattr(__snake_case     ,			"""do_convert_rgb"""			)			)

						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.image_processor_tester.prepare_dummy_image()

										__magic_name__						:						Dict										=self.image_processing_class(**self.image_processor_dict			)
										__magic_name__						:						str										=20_48

										__magic_name__						:						Tuple										=image_processor(__snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			)
										self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     ,			torch.tensor(0.0606			)     ,			atol=1E-3     ,			rtol=1E-3			)			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										__magic_name__						:						Optional[int]										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			Image.Image			)

										# Test not batched input
										__magic_name__						:						Union[str, Any]										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														__magic_name__						:						Union[str, Any]										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						List[str]										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										__magic_name__						:						str										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			Image.Image			)

										# Test not batched input
										__magic_name__						:						str										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										__magic_name__						:						List[Any]										=True

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														with self.assertRaises(__snake_case			):
																		__magic_name__						:						Optional[Any]										=image_processor(
																		    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches

														__magic_name__						:						Optional[int]										="""Hello"""

														__magic_name__						:						List[Any]										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case     ,			header_text=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						Optional[Any]										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case     ,			header_text=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										__magic_name__						:						str										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case     ,			numpify=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			np.ndarray			)

										__magic_name__						:						List[Any]										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														__magic_name__						:						Optional[Any]										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						str										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										__magic_name__						:						Union[str, Any]										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case     ,			torchify=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			torch.Tensor			)

										# Test not batched input
										__magic_name__						:						Union[str, Any]										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														__magic_name__						:						Tuple										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						str										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,    reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""       ,    )
@require_torch
@require_vision
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       PixaStructImageProcessor if is_vision_available() else None

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=PixaStructImageProcessingTester(self     ,			num_channels=4			)
										__magic_name__						:						Optional[int]										=3

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(__snake_case     ,			"""do_normalize"""			)			)
										self.assertTrue(hasattr(__snake_case     ,			"""do_convert_rgb"""			)			)


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										__magic_name__						:						Tuple										=prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__snake_case			)
										for image in image_inputs:
														self.assertIsInstance(__snake_case     ,			Image.Image			)

										# Test not batched input
										__magic_name__						:						str										=(
										    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
										    * (self.image_processor_tester.num_channels - 1)
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														__magic_name__						:						Any										=image_processor(
														    image_inputs[0]     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(1, max_patch, expected_hidden_dim)     ,			)

														# Test batched
														__magic_name__						:						List[Any]										=image_processor(
														    __snake_case     ,			return_tensors="""pt"""     ,			max_patches=__snake_case			).flattened_patches
														self.assertEqual(
														    encoded_images.shape     ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,			)










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)




