
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:
		_lowercase							   =				False

if is_vision_available():
		from PIL import Image

		from transformers import PixaStructImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      List[Any]  ,A_     :      int  ,A_     :      Optional[int]=7  ,A_     :      List[str]=3  ,A_     :      Tuple=18  ,A_     :      List[Any]=30  ,A_     :      Tuple=400  ,A_     :      Any=None  ,A_     :      Optional[int]=True  ,A_     :      List[str]=True  ,A_     :      Union[str, Any]=None  ,)     ->   List[Any]:
							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						   =   [512, 1024, 2048, 4096]
							A						   =   patch_size if patch_size is not None else {'height': 16, 'width': 16}


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Tuple:
							A						   =   'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
							A						   =   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_				(      lowercase__		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Any      =    PixaStructImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   int:
							A						   =   PixaStructImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   str:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(__lowercase  ,'do_normalize'      )      )
							self.assertTrue(hasattr(__lowercase  ,'do_convert_rgb'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Dict:
							A						   =   self.image_processor_tester.prepare_dummy_image()

							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   2048

							A						   =   image_processor(__lowercase  ,return_tensors='pt'  ,max_patches=__lowercase      )
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()  ,torch.tensor(0.06_06      )  ,atol=1e-3  ,rtol=1e-3      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, 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=__lowercase      )
							for image in image_inputs:
								self.assertIsInstance(__lowercase  ,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=__lowercase      ).flattened_patches
								self.assertEqual(
								    encoded_images.shape  ,(1, max_patch, expected_hidden_dim)  ,)

								# Test batched
								A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      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=__lowercase      )
							for image in image_inputs:
								self.assertIsInstance(__lowercase  ,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(__lowercase      ):
									A						   =   image_processor(
									    image_inputs[0]  ,return_tensors='pt'  ,max_patches=__lowercase      ).flattened_patches

								A						   =   'Hello'

								A						   =   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
								A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Optional[int]:
							# 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=__lowercase  ,numpify=__lowercase      )
							for image in image_inputs:
								self.assertIsInstance(__lowercase  ,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=__lowercase      ).flattened_patches
								self.assertEqual(
								    encoded_images.shape  ,(1, max_patch, expected_hidden_dim)  ,)

								# Test batched
								A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[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=__lowercase  ,torchify=__lowercase      )
							for image in image_inputs:
								self.assertIsInstance(__lowercase  ,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=__lowercase      ).flattened_patches
								self.assertEqual(
								    encoded_images.shape  ,(1, max_patch, expected_hidden_dim)  ,)

								# Test batched
								A						   =   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_				(      lowercase__		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[int]      =    PixaStructImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[Any]:
							A						   =   PixaStructImageProcessingTester(self  ,num_channels=4      )
							A						   =   3


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Optional[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Dict:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(__lowercase  ,'do_normalize'      )      )
							self.assertTrue(hasattr(__lowercase  ,'do_convert_rgb'      )      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, 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=__lowercase      )
							for image in image_inputs:
								self.assertIsInstance(__lowercase  ,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=__lowercase      ).flattened_patches
								self.assertEqual(
								    encoded_images.shape  ,(1, max_patch, expected_hidden_dim)  ,)

								# Test batched
								A						   =   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"""


from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy
		import tensorflow as tf

		from transformers.models.esm.modeling_tf_esm import (
		    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFEsmForMaskedLM,
		    TFEsmForSequenceClassification,
		    TFEsmForTokenClassification,
		    TFEsmModel,
		)







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      List[Any]  ,A_     :      Optional[Any]  ,)     ->   Optional[int]:
							A						   =   parent
							A						   =   13
							A						   =   7
							A						   =   True
							A						   =   True
							A						   =   True
							A						   =   99
							A						   =   32
							A						   =   2
							A						   =   4
							A						   =   37
							A						   =   'gelu'
							A						   =   0.1
							A						   =   0.1
							A						   =   512
							A						   =   16
							A						   =   2
							A						   =   0.02
							A						   =   3
							A						   =   4
							A						   =   None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Union[str, Any]:
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size      )

							A						   =   None
							if self.use_input_mask:
								A						   =   random_attention_mask([self.batch_size, self.seq_length]      )

							A						   =   None
							A						   =   None
							A						   =   None
							if self.use_labels:
								A						   =   ids_tensor([self.batch_size]  ,self.type_sequence_label_size      )
								A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels      )
								A						   =   ids_tensor([self.batch_size]  ,self.num_choices      )

							A						   =   EsmConfig(
							    vocab_size=self.vocab_size  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,pad_token_id=1  ,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  ,initializer_range=self.initializer_range  ,)

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Dict:
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   self.prepare_config_and_inputs()

							A						   =   True
							A						   =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,vocab_size=2      )

							return (
							    config,
							    input_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      int  ,A_     :      List[str]  ,A_     :      Optional[int]  ,A_     :      List[Any]  ,A_     :      Any  ,A_     :      Any      )     ->   Dict:
							A						   =   TFEsmModel(config=A_      )
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							A						   =   model(A_      )

							A						   =   [input_ids, input_mask]
							A						   =   model(A_      )

							A						   =   model(A_      )

							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      int  ,A_     :      List[Any]  ,A_     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,)     ->   Optional[int]:
							A						   =   True

							A						   =   TFEsmModel(config=A_      )
							A						   =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'encoder_hidden_states': encoder_hidden_states,
							    'encoder_attention_mask': encoder_attention_mask,
							}
							A						   =   model(A_      )

							A						   =   [input_ids, input_mask]
							A						   =   model(A_  ,encoder_hidden_states=A_      )

							# Also check the case where encoder outputs are not passed
							A						   =   model(A_  ,attention_mask=A_      )

							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      List[Any]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[Any]      )     ->   Dict:
							A						   =   TFEsmForMaskedLM(config=A_      )
							A						   =   model([input_ids, input_mask]      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      List[Any]  ,A_     :      int  ,A_     :      Tuple  ,A_     :      Optional[int]      )     ->   Union[str, Any]:
							A						   =   self.num_labels
							A						   =   TFEsmForTokenClassification(config=A_      )
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							A						   =   model(A_      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.prepare_config_and_inputs()
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   config_and_inputs
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_tf
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    (
						    (
						        TFEsmModel,
						        TFEsmForMaskedLM,
						        TFEsmForSequenceClassification,
						        TFEsmForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCamelCase:    List[str]      =    (
						    {
						        '''feature-extraction''': TFEsmModel,
						        '''fill-mask''': TFEsmForMaskedLM,
						        '''text-classification''': TFEsmForSequenceClassification,
						        '''token-classification''': TFEsmForTokenClassification,
						        '''zero-shot''': TFEsmForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_lowerCamelCase:    Union[str, Any]      =    False
						_lowerCamelCase:    List[Any]      =    False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   int:
							A						   =   TFEsmModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,hidden_size=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[int]:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[str]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A_      )


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Tuple:
							for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   TFEsmModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )


						@unittest.skip('Protein models do not support embedding resizing.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[int]:
							pass


						@unittest.skip('Protein models do not support embedding resizing.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Any:
							pass




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Dict:
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A						   =   model_class(A_      )
								assert isinstance(model.get_input_embeddings()  ,tf.keras.layers.Layer      )
								if model_class is TFEsmForMaskedLM:
									# Output embedding test differs from the main test because they're a matrix, not a layer
									A						   =   model.get_bias()
									assert isinstance(A_  ,A_      )
									for k, v in name.items():
										assert isinstance(A_  ,tf.Variable      )
								else:
									A						   =   model.get_output_embeddings()
									assert x is None
									A						   =   model.get_bias()
									assert name is None







@require_tf
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							A						   =   TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'      )

							A						   =   tf.constant([[0, 1, 2, 3, 4, 5]]      )
							A						   =   model(A_      )[0]
							A						   =   [1, 6, 33]
							self.assertEqual(list(output.numpy().shape      )  ,A_      )
							# compare the actual values for a slice.
							A						   =   tf.constant(
							    [
							        [
							            [8.92_15_18, -10.58_98_14, -6.4_67_13_07],
							            [-6.3_96_71_56, -13.91_13_77, -1.1_21_19_15],
							            [-7.78_12_47, -13.95_15_57, -3.74_05_92],
							        ]
							    ]      )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1e-2      )      )




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   int:
							A						   =   TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'      )

							A						   =   tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
							A						   =   model(A_      )[0]
							# compare the actual values for a slice.
							A						   =   tf.constant(
							    [
							        [
							            [0.14_44_30_92, 0.54_12_53_27, 0.3_24_77_39],
							            [0.30_34_04_84, 0.00_52_66_76, 0.31_07_77_22],
							            [0.32_27_80_43, -0.24_98_70_96, 0.3_41_46_28],
							        ]
							    ]      )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1e-4      )      )