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:
				__A      =				False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor





class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def __init__(  self:   Tuple		,     __A:   str		,     __A:   Dict=7		,     __A:   Tuple=3		,     __A:   Dict=18		,     __A:   Dict=30		,     __A:   Tuple=4_00		,     __A:   Optional[int]=None		,     __A:   List[Any]=True		,     __A:   int=True		,     __A:   Optional[int]=None		,     )    ->				str:
												_A								= size if size is not None else {'''height''': 20, '''width''': 20}
												_A								= parent
												_A								= batch_size
												_A								= num_channels
												_A								= image_size
												_A								= min_resolution
												_A								= max_resolution
												_A								= size
												_A								= do_normalize
												_A								= do_convert_rgb
												_A								= [5_12, 10_24, 20_48, 40_96]
												_A								= patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

							def       __A (  self:   Tuple   )    ->				Optional[Any]:
												return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



							def       __A (  self:   str   )    ->				List[str]:
												_A								= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
												_A								= 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 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					PixaStructImageProcessor if is_vision_available() else None

							def       __A (  self:   Optional[int]   )    ->				Any:
												_A								= PixaStructImageProcessingTester(self   )

							@property
							def       __A (  self:   List[Any]   )    ->				str:
												return self.image_processor_tester.prepare_image_processor_dict()

							def       __A (  self:   Optional[int]   )    ->				Union[str, Any]:
												_A								= self.image_processing_class(**self.image_processor_dict   )
												self.assertTrue(hasattr(__A		,     '''do_normalize'''   )   )
												self.assertTrue(hasattr(__A		,     '''do_convert_rgb'''   )   )

							def       __A (  self:   Any   )    ->				Union[str, Any]:
												_A								= self.image_processor_tester.prepare_dummy_image()

												_A								= self.image_processing_class(**self.image_processor_dict   )
												_A								= 20_48

												_A								= image_processor(__A		,     return_tensors='''pt'''		,     max_patches=__A   )
												self.assertTrue(torch.allclose(inputs.flattened_patches.mean()		,     torch.tensor(0.0_606   )		,     atol=1e-3		,     rtol=1e-3   )   )

							def       __A (  self:   Optional[int]   )    ->				List[str]:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												_A								= prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A   )
												for image in image_inputs:
																	self.assertIsInstance(__A		,     Image.Image   )

												# Test not batched input
												_A								= (
												    (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
																	_A								= 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
																	_A								= 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       __A (  self:   Any   )    ->				Tuple:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												_A								= prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A   )
												for image in image_inputs:
																	self.assertIsInstance(__A		,     Image.Image   )

												# Test not batched input
												_A								= (
												    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
												    * self.image_processor_tester.num_channels
												) + 2

												_A								= True

												for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	with self.assertRaises(__A   ):
																						_A								= image_processor(
																						    image_inputs[0]		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches

																	_A								= '''Hello'''

																	_A								= 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
																	_A								= 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       __A (  self:   Optional[int]   )    ->				Any:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random numpy tensors
												_A								= prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A		,     numpify=__A   )
												for image in image_inputs:
																	self.assertIsInstance(__A		,     np.ndarray   )

												_A								= (
												    (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
																	_A								= 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
																	_A								= 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       __A (  self:   Tuple   )    ->				str:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PyTorch tensors
												_A								= 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
												_A								= (
												    (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
																	_A								= 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
																	_A								= 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 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					PixaStructImageProcessor if is_vision_available() else None

							def       __A (  self:   Optional[int]   )    ->				List[str]:
												_A								= PixaStructImageProcessingTester(self		,     num_channels=4   )
												_A								= 3

							@property
							def       __A (  self:   List[Any]   )    ->				str:
												return self.image_processor_tester.prepare_image_processor_dict()

							def       __A (  self:   str   )    ->				Dict:
												_A								= self.image_processing_class(**self.image_processor_dict   )
												self.assertTrue(hasattr(__A		,     '''do_normalize'''   )   )
												self.assertTrue(hasattr(__A		,     '''do_convert_rgb'''   )   )



							def       __A (  self:   List[Any]   )    ->				str:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												_A								= prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A   )
												for image in image_inputs:
																	self.assertIsInstance(__A		,     Image.Image   )

												# Test not batched input
												_A								= (
												    (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
																	_A								= 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
																	_A								= 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 os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__A      =				'\\n    Text data.\n    Second line of data.'

__A      =				'file'




@pytest.fixture(scope='''session''' )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
					_A								= bytes(_lowercase					,			'''utf-8''' )
					with zstd.open(_lowercase					,			'''wb''' ) as f:
										f.write(_lowercase )
					return path




@pytest.fixture
def      __A					(     _lowercase ):

					'''simple docstring'''







					with open(os.path.join(tmpfs.local_root_dir					,			_lowercase )					,			'''w''' ) as f:
										f.write(_lowercase )
					return FILE_PATH




@pytest.mark.parametrize('''compression_format'''					,			['''gzip''', '''xz''', '''zstd'''] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
					_A								= input_paths[compression_format]
					_A								= tmp_path / '''cache'''
					_A								= DownloadConfig(cache_dir=_lowercase					,			extract_compressed_file=_lowercase )
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					with open(_lowercase ) as f:
										_A								= f.read()
					with open(_lowercase ) as f:
										_A								= f.read()
					assert extracted_file_content == expected_file_content




@pytest.mark.parametrize('''default_extracted'''					,			[True, False] )
@pytest.mark.parametrize('''default_cache_dir'''					,			[True, False] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= '''custom_cache'''
					_A								= '''custom_extracted_dir'''
					_A								= tmp_path / '''custom_extracted_path'''
					if default_extracted:
										_A								= ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
					else:
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''					,			_lowercase )
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''					,			str(_lowercase ) )
										_A								= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

					_A								= xz_file
					_A								= (
					    DownloadConfig(extract_compressed_file=_lowercase )
					    if default_cache_dir
					    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir					,			extract_compressed_file=_lowercase )
					)
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					assert Path(_lowercase ).parent.parts[-2:] == expected




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(Path(_lowercase ).resolve() )
					assert cached_path(_lowercase ) == text_file
					# relative path
					_A								= str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
					assert cached_path(_lowercase ) == text_file




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(tmp_path.resolve() / '''__missing_file__.txt''' )
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )
					# relative path
					_A								= '''./__missing_file__.txt'''
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= get_from_cache(f"""tmp://{tmpfs_file}""" )
					with open(_lowercase ) as f:
										_A								= f.read()
					assert output_file_content == FILE_CONTENT




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     ):

					'''simple docstring'''







					with pytest.raises(_lowercase ):
										cached_path('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										http_get('''https://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										http_head('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										ftp_get('''ftp://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										ftp_head('''ftp://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										fsspec_get('''s3://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										fsspec_head('''s3://huggingface.co''' )



