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:
	lowerCamelCase_    :		Dict							=	False

if is_vision_available():
	from PIL import Image

	from transformers import PixaStructImageProcessor


class 						a__							(       unittest.TestCase		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase=7				,						UpperCAmelCase=3				,						UpperCAmelCase=1_8				,						UpperCAmelCase=3_0				,						UpperCAmelCase=4_0_0				,						UpperCAmelCase=None				,						UpperCAmelCase=True				,						UpperCAmelCase=True				,						UpperCAmelCase=None				,						)			->   Optional[int]:
								__a		 =       size if size is not None else {'height': 2_0, 'width': 2_0}
								__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_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
								__a		 =       patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
								__a		 =       Image.open(requests.get(UpperCAmelCase				,						stream=UpperCAmelCase   ).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__							(       __snake_case				,						unittest.TestCase		):
			A__    :       Tuple        =      PixaStructImageProcessor if is_vision_available() else None



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       PixaStructImageProcessingTester(self   )



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								return self.image_processor_tester.prepare_image_processor_dict()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'do_normalize'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'do_convert_rgb'   )   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.image_processor_tester.prepare_dummy_image()

								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								__a		 =       2_0_4_8

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								# 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=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						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=UpperCAmelCase   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,						(1, max_patch, expected_hidden_dim)				,						)

													# Test batched
													__a		 =       image_processor(
													    UpperCAmelCase				,						return_tensors='pt'				,						max_patches=UpperCAmelCase   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,						)



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								# 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=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						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(UpperCAmelCase   ):
																		__a		 =       image_processor(
																		    image_inputs[0]				,						return_tensors='pt'				,						max_patches=UpperCAmelCase   ).flattened_patches

													__a		 =       'Hello'

													__a		 =       image_processor(
													    image_inputs[0]				,						return_tensors='pt'				,						max_patches=UpperCAmelCase				,						header_text=UpperCAmelCase   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,						(1, max_patch, expected_hidden_dim)				,						)

													# Test batched
													__a		 =       image_processor(
													    UpperCAmelCase				,						return_tensors='pt'				,						max_patches=UpperCAmelCase				,						header_text=UpperCAmelCase   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,						)



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								# 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=UpperCAmelCase				,						numpify=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						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=UpperCAmelCase   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,						(1, max_patch, expected_hidden_dim)				,						)

													# Test batched
													__a		 =       image_processor(
													    UpperCAmelCase				,						return_tensors='pt'				,						max_patches=UpperCAmelCase   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,						)







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								# 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=UpperCAmelCase				,						torchify=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						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=UpperCAmelCase   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,						(1, max_patch, expected_hidden_dim)				,						)

													# Test batched
													__a		 =       image_processor(
													    UpperCAmelCase				,						return_tensors='pt'				,						max_patches=UpperCAmelCase   ).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__							(       __snake_case				,						unittest.TestCase		):
			A__    :       int        =      PixaStructImageProcessor if is_vision_available() else None



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       PixaStructImageProcessingTester(self				,						num_channels=4   )
								__a		 =       3



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								return self.image_processor_tester.prepare_image_processor_dict()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'do_normalize'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'do_convert_rgb'   )   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								# 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=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						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=UpperCAmelCase   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,						(1, max_patch, expected_hidden_dim)				,						)

													# Test batched
													__a		 =       image_processor(
													    UpperCAmelCase				,						return_tensors='pt'				,						max_patches=UpperCAmelCase   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,						)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    :		Optional[int]							=	{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		str							=	["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	[
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[int]							=	[
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Tuple							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




