
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:
				snake_case :		List[str]							      =  False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor





class       _snake_case					(     unittest.TestCase				):







			def __init__(				self						,   _a						,   _a=7						,   _a=3						,   _a=18						,   _a=30						,   _a=400						,   _a=None						,   _a=True						,   _a=True						,   _a=None						,   ):
				__magic_name__		:		List[Any]	   =     size if size is not None else {"height": 20, "width": 20}
				__magic_name__		:		List[str]	   =     parent
				__magic_name__		:		Any	   =     batch_size
				__magic_name__		:		Any	   =     num_channels
				__magic_name__		:		Tuple	   =     image_size
				__magic_name__		:		Any	   =     min_resolution
				__magic_name__		:		Union[str, Any]	   =     max_resolution
				__magic_name__		:		Tuple	   =     size
				__magic_name__		:		Any	   =     do_normalize
				__magic_name__		:		List[Any]	   =     do_convert_rgb
				__magic_name__		:		Dict	   =     [512, 1_024, 2_048, 4_096]
				__magic_name__		:		List[Any]	   =     patch_size if patch_size is not None else {"height": 16, "width": 16}







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
				__magic_name__		:		Optional[Any]	   =     Image.open(requests.get(_a						,   stream=_a		).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       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    PixaStructImageProcessor if is_vision_available() else None







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     PixaStructImageProcessingTester(self		)







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return self.image_processor_tester.prepare_image_processor_dict()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(_a						,   "do_normalize"		)		)
				self.assertTrue(hasattr(_a						,   "do_convert_rgb"		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     self.image_processor_tester.prepare_dummy_image()

				__magic_name__		:		Optional[Any]	   =     self.image_processing_class(**self.image_processor_dict		)
				__magic_name__		:		List[Any]	   =     2_048

				__magic_name__		:		Union[str, Any]	   =     image_processor(_a						,   return_tensors="pt"						,   max_patches=_a		)
				self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,   torch.tensor(0.06_06		)						,   atol=1e-3						,   rtol=1e-3		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Initialize image_processor
				__magic_name__		:		Union[str, 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=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   Image.Image		)

				# Test not batched input
				__magic_name__		:		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__		:		Dict	   =     image_processor(
					    image_inputs[0]						,   return_tensors="pt"						,   max_patches=_a		).flattened_patches
					self.assertEqual(
					    encoded_images.shape						,   (1, max_patch, expected_hidden_dim)						,   )

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







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

				# Test not batched input
				__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

				__magic_name__		:		Any	   =     True

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

					__magic_name__		:		List[str]	   =     "Hello"

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

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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Initialize image_processor
				__magic_name__		:		Dict	   =     self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				__magic_name__		:		int	   =     prepare_image_inputs(self.image_processor_tester						,   equal_resolution=_a						,   numpify=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   np.ndarray		)

				__magic_name__		:		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=_a		).flattened_patches
					self.assertEqual(
					    encoded_images.shape						,   (1, max_patch, expected_hidden_dim)						,   )

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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Initialize image_processor
				__magic_name__		:		Tuple	   =     self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				__magic_name__		:		Tuple	   =     prepare_image_inputs(self.image_processor_tester						,   equal_resolution=_a						,   torchify=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   torch.Tensor		)

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

					# Test batched
					__magic_name__		:		int	   =     image_processor(
					    _a						,   return_tensors="pt"						,   max_patches=_a		).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       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    PixaStructImageProcessor if is_vision_available() else None







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     PixaStructImageProcessingTester(self						,   num_channels=4		)
				__magic_name__		:		Union[str, Any]	   =     3







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return self.image_processor_tester.prepare_image_processor_dict()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(_a						,   "do_normalize"		)		)
				self.assertTrue(hasattr(_a						,   "do_convert_rgb"		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Initialize image_processor
				__magic_name__		:		Dict	   =     self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				__magic_name__		:		int	   =     prepare_image_inputs(self.image_processor_tester						,   equal_resolution=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   Image.Image		)

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

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


import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard






def   lowerCAmelCase_						(					_snake_case      :  List[Any]   )				->		List[Any]:







	'''simple docstring'''


	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


	assert "mock" not in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Dict	   =     "mock-s3-bucket"
	__magic_name__		:		Any	   =     F'''s3://{mock_bucket}'''
	__magic_name__		:		str	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path.startswith("s3://"   ) is False

	__magic_name__		:		Tuple	   =     "./local/path"
	__magic_name__		:		Optional[Any]	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path == new_dataset_path






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is True

	__magic_name__		:		Optional[int]	   =     fsspec.filesystem("file"   )

	__magic_name__		:		int	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is False






@pytest.mark.parametrize("compression_fs_class"      ,   _snake_case   )
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  int      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Any   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
	__magic_name__		:		str	   =     input_paths[compression_fs_class.protocol]
	if input_path is None:
		__magic_name__		:		Dict	   =     F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
		if compression_fs_class.protocol == "lz4":
			reason += require_lza.kwargs["reason"]
		elif compression_fs_class.protocol == "zstd":
			reason += require_zstandard.kwargs["reason"]
		pytest.skip(_snake_case   )
	__magic_name__		:		str	   =     fsspec.filesystem(compression_fs_class.protocol      ,   fo=_snake_case   )
	assert isinstance(_snake_case      ,   _snake_case   )
	__magic_name__		:		int	   =     os.path.basename(_snake_case   )
	__magic_name__		:		Optional[int]	   =     expected_filename[: expected_filename.rindex("."   )]
	assert fs.glob("*"   ) == [expected_filename]
	with fs.open(_snake_case      ,   "r"      ,   encoding="utf-8"   ) as f, open(_snake_case      ,   encoding="utf-8"   ) as expected_file:
		assert f.read() == expected_file.read()






@pytest.mark.parametrize("protocol"      ,   ["zip", "gzip"]   )
def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
	__magic_name__		:		int	   =     compressed_file_paths[protocol]
	__magic_name__		:		Tuple	   =     "dataset.jsonl"
	__magic_name__		:		List[str]	   =     F'''{protocol}://{member_file_path}::{compressed_file_path}'''
	__magic_name__   ,    *__magic_name__		:		Optional[Any]	   =     fsspec.get_fs_token_paths(_snake_case   )
	assert fs.isfile(_snake_case   )
	assert not fs.isfile("non_existing_" + member_file_path   )






@pytest.mark.integration
def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     hf_api.dataset_info(_snake_case      ,   token=_snake_case   )
	__magic_name__		:		Optional[Any]	   =     HfFileSystem(repo_info=_snake_case      ,   token=_snake_case   )
	assert sorted(hffs.glob("*"   )   ) == [".gitattributes", "data"]
	assert hffs.isdir("data"   )
	assert hffs.isfile(".gitattributes"   ) and hffs.isfile("data/text_data.txt"   )
	with open(_snake_case   ) as f:
		assert hffs.open("data/text_data.txt"      ,   "r"   ).read() == f.read()






def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     "bz2"

	# Import module
	import datasets.filesystems

	# Overwrite protocol and reload
	register_implementation(_snake_case      ,   _snake_case      ,   clobber=_snake_case   )
	with pytest.warns(_snake_case   ) as warning_info:
		importlib.reload(datasets.filesystems   )

	assert len(_snake_case   ) == 1
	assert (
	    str(warning_info[0].message   )
	    == F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
	)
