



"""simple docstring"""





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       :					Tuple      					=					False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=7							,    _lowercase=3							,    _lowercase=1_8							,    _lowercase=3_0							,    _lowercase=4_0_0							,    _lowercase=None							,    _lowercase=True							,    _lowercase=True							,    _lowercase=None							,    ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Dict						=			size if size is not None else {"""height""": 2_0, """width""": 2_0}
								snake_case_   : List[str]						=			parent
								snake_case_   : Dict						=			batch_size
								snake_case_   : Any						=			num_channels
								snake_case_   : Dict						=			image_size
								snake_case_   : List[str]						=			min_resolution
								snake_case_   : str						=			max_resolution
								snake_case_   : List[Any]						=			size
								snake_case_   : Optional[int]						=			do_normalize
								snake_case_   : Optional[Any]						=			do_convert_rgb
								snake_case_   : Optional[int]						=			[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
								snake_case_   : List[str]						=			patch_size if patch_size is not None else {"""height""": 1_6, """width""": 1_6}





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
								snake_case_   : int						=			Image.open(requests.get(_lowercase							,    stream=_lowercase   ).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    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       PixaStructImageProcessor if is_vision_available() else None





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : List[str]						=			PixaStructImageProcessingTester(self   )





		@property
		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(_lowercase							,    """do_normalize"""   )   )
								self.assertTrue(hasattr(_lowercase							,    """do_convert_rgb"""   )   )





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : str						=			self.image_processor_tester.prepare_dummy_image()

								snake_case_   : str						=			self.image_processing_class(**self.image_processor_dict   )
								snake_case_   : Dict						=			2_0_4_8

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





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







								'''simple docstring'''

								snake_case_   : Any						=			self.image_processing_class(**self.image_processor_dict   )
								# create random PIL images
								snake_case_   : Union[str, Any]						=			prepare_image_inputs(self.image_processor_tester							,    equal_resolution=_lowercase   )
								for image in image_inputs:
														self.assertIsInstance(_lowercase							,    Image.Image   )

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

														# Test batched
														snake_case_   : Optional[int]						=			image_processor(
														    _lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase   ).flattened_patches
														self.assertEqual(
														    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

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

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

								snake_case_   : Dict						=			True

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

														snake_case_   : Dict						=			"""Hello"""

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

														# Test batched
														snake_case_   : Tuple						=			image_processor(
														    _lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase							,    header_text=_lowercase   ).flattened_patches
														self.assertEqual(
														    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





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







								'''simple docstring'''

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

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

														# Test batched
														snake_case_   : Tuple						=			image_processor(
														    _lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase   ).flattened_patches
														self.assertEqual(
														    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								snake_case_   : Any						=			prepare_image_inputs(self.image_processor_tester							,    equal_resolution=_lowercase							,    torchify=_lowercase   )
								for image in image_inputs:
														self.assertIsInstance(_lowercase							,    torch.Tensor   )

								# Test not batched input
								snake_case_   : List[str]						=			(
								    (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
														snake_case_   : Union[str, Any]						=			image_processor(
														    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=_lowercase   ).flattened_patches
														self.assertEqual(
														    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

														# Test batched
														snake_case_   : Optional[Any]						=			image_processor(
														    _lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase   ).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    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       PixaStructImageProcessor if is_vision_available() else None





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Tuple						=			PixaStructImageProcessingTester(self							,    num_channels=4   )
								snake_case_   : Tuple						=			3





		@property
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : str						=			self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(_lowercase							,    """do_normalize"""   )   )
								self.assertTrue(hasattr(_lowercase							,    """do_convert_rgb"""   )   )





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







								'''simple docstring'''

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

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

														# Test batched
														snake_case_   : Any						=			image_processor(
														    _lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase   ).flattened_patches
														self.assertEqual(
														    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )






"""simple docstring"""





import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_3							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=3_2							,    _lowercase=5							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=1_6							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=4							,    ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Tuple						=			parent
								snake_case_   : List[str]						=			batch_size
								snake_case_   : int						=			seq_length
								snake_case_   : List[Any]						=			is_training
								snake_case_   : Optional[int]						=			use_attention_mask
								snake_case_   : Optional[Any]						=			use_token_type_ids
								snake_case_   : Union[str, Any]						=			use_labels
								snake_case_   : str						=			vocab_size
								snake_case_   : List[str]						=			hidden_size
								snake_case_   : List[str]						=			num_hidden_layers
								snake_case_   : int						=			num_attention_heads
								snake_case_   : Dict						=			intermediate_size
								snake_case_   : Union[str, Any]						=			hidden_act
								snake_case_   : List[str]						=			hidden_dropout_prob
								snake_case_   : Any						=			attention_probs_dropout_prob
								snake_case_   : List[str]						=			max_position_embeddings
								snake_case_   : Union[str, Any]						=			type_vocab_size
								snake_case_   : str						=			type_sequence_label_size
								snake_case_   : Dict						=			initializer_range
								snake_case_   : str						=			num_choices





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size   )

								snake_case_   : Tuple						=			None
								if self.use_attention_mask:
														snake_case_   : Union[str, Any]						=			random_attention_mask([self.batch_size, self.seq_length]   )

								snake_case_   : Optional[int]						=			None
								if self.use_token_type_ids:
														snake_case_   : str						=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size   )

								snake_case_   : Union[str, Any]						=			RoFormerConfig(
								    vocab_size=self.vocab_size							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    max_position_embeddings=self.max_position_embeddings							,    type_vocab_size=self.type_vocab_size							,    is_decoder=_lowercase							,    initializer_range=self.initializer_range							,    )

								return config, input_ids, token_type_ids, attention_mask





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : str						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : List[Any]						=			config_and_inputs
								snake_case_   : Tuple						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict




@require_flax
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       True

		_lowerCamelCase			  =       (
		    (
		        FlaxRoFormerModel,
		        FlaxRoFormerForMaskedLM,
		        FlaxRoFormerForSequenceClassification,
		        FlaxRoFormerForTokenClassification,
		        FlaxRoFormerForMultipleChoice,
		        FlaxRoFormerForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)





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







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerModelTester(self   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_   : Tuple						=			model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,    from_pt=_lowercase   )
														snake_case_   : List[str]						=			model(np.ones((1, 1)   )   )
														self.assertIsNotNone(_lowercase   )




@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Dict						=			FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
								snake_case_   : Tuple						=			jnp.array([[0, 1, 2, 3, 4, 5]]   )
								snake_case_   : Dict						=			model(_lowercase   )[0]

								snake_case_   : Optional[int]						=			5_0_0_0_0

								snake_case_   : Union[str, Any]						=			(1, 6, vocab_size)
								self.assertEqual(output.shape							,    _lowercase   )

								snake_case_   : Dict						=			jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

								self.assertTrue(jnp.allclose(output[:, :3, :3]							,    _lowercase							,    atol=1E-4   )   )

