
"""simple docstring"""




import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
					from transformers.models.esm.modeling_esm import (
					    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    EsmEmbeddings,
					    create_position_ids_from_input_ids,
					)



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=13   ,       SCREAMING_SNAKE_CASE__=7   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=33   ,       SCREAMING_SNAKE_CASE__=32   ,       SCREAMING_SNAKE_CASE__=5   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=37   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=5_12   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=None   ,       )					->		Optional[Any]:



											"""simple docstring"""




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



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   None
											if self.use_input_mask:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   random_attention_mask([self.batch_size, self.seq_length]    )

											SCREAMING_SNAKE_CASE__						:       List[str]           =   None
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   None
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   ids_tensor([self.batch_size]   ,       self.type_sequence_label_size    )
																		SCREAMING_SNAKE_CASE__						:       Any           =   ids_tensor([self.batch_size, self.seq_length]   ,       self.num_labels    )
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ids_tensor([self.batch_size]   ,       self.num_choices    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.get_config()

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



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



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   EsmModel(config=SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   model(SCREAMING_SNAKE_CASE__   ,       attention_mask=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   model(SCREAMING_SNAKE_CASE__    )

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



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   EsmForMaskedLM(config=SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model(SCREAMING_SNAKE_CASE__   ,       attention_mask=SCREAMING_SNAKE_CASE__   ,       labels=SCREAMING_SNAKE_CASE__    )
											self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.num_labels
											SCREAMING_SNAKE_CASE__						:       Tuple           =   EsmForTokenClassification(config=SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model(SCREAMING_SNAKE_CASE__   ,       attention_mask=SCREAMING_SNAKE_CASE__   ,       labels=SCREAMING_SNAKE_CASE__    )
											self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.num_labels)    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.prepare_config_and_inputs()
											(
											    (
											    SCREAMING_SNAKE_CASE__
											)      ,      (
											    SCREAMING_SNAKE_CASE__
											)      ,      (
											    SCREAMING_SNAKE_CASE__
											)      ,      (
											    SCREAMING_SNAKE_CASE__
											)      ,      (
											    SCREAMING_SNAKE_CASE__
											)      ,      (
											    SCREAMING_SNAKE_CASE__
											)      ,      
											)						:       Optional[int]           =   config_and_inputs
											SCREAMING_SNAKE_CASE__						:       List[str]           =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
											return config, inputs_dict






@require_torch
class 	lowerCAmelCase_						(a__ ,					a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   False

				__UpperCamelCase      :						int					       =   (
				    (
				        EsmForMaskedLM,
				        EsmModel,
				        EsmForSequenceClassification,
				        EsmForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__UpperCamelCase      :						Optional[int]					       =   ()
				__UpperCamelCase      :						List[Any]					       =   (
				    {
				        '''feature-extraction''': EsmModel,
				        '''fill-mask''': EsmForMaskedLM,
				        '''text-classification''': EsmForSequenceClassification,
				        '''token-classification''': EsmForTokenClassification,
				        '''zero-shot''': EsmForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__UpperCamelCase      :						Any					       =   True



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   EsmModelTester(self    )
											SCREAMING_SNAKE_CASE__						:       int           =   ConfigTester(self   ,       config_class=SCREAMING_SNAKE_CASE__   ,       hidden_size=37    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											self.config_tester.run_common_tests()



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   type
																		self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__    )



				@slow
				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   EsmModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
																		self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model_tester.prepare_config_and_inputs()[0]
											SCREAMING_SNAKE_CASE__						:       Dict           =   EsmEmbeddings(config=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.as_tensor([[12, 31, 13, model.padding_idx]]    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.as_tensor(
											    [
											        [
											            0 + model.padding_idx + 1,
											            1 + model.padding_idx + 1,
											            2 + model.padding_idx + 1,
											            model.padding_idx,
											        ]
											    ]    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   create_position_ids_from_input_ids(SCREAMING_SNAKE_CASE__   ,       model.padding_idx    )
											self.assertEqual(position_ids.shape   ,       expected_positions.shape    )
											self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )    )    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.model_tester.prepare_config_and_inputs()[0]
											SCREAMING_SNAKE_CASE__						:       Any           =   EsmEmbeddings(config=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.empty(2   ,       4   ,       30    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [
											    0 + embeddings.padding_idx + 1,
											    1 + embeddings.padding_idx + 1,
											    2 + embeddings.padding_idx + 1,
											    3 + embeddings.padding_idx + 1,
											]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.as_tensor([expected_single_positions, expected_single_positions]    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   embeddings.create_position_ids_from_inputs_embeds(SCREAMING_SNAKE_CASE__    )
											self.assertEqual(position_ids.shape   ,       expected_positions.shape    )
											self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )    )    )



				@unittest.skip("""Esm does not support embedding resizing"""    )
				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											pass



				@unittest.skip("""Esm does not support embedding resizing"""    )
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											pass



				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											pass






@require_torch
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				@slow
				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""    )
																		model.eval()
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.tensor([[0, 1, 2, 3, 4, 5]]    )
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model(SCREAMING_SNAKE_CASE__    )[0]

																		SCREAMING_SNAKE_CASE__						:       Tuple           =   33

																		SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.Size((1, 6, vocab_size)    )
																		self.assertEqual(output.shape   ,       SCREAMING_SNAKE_CASE__    )

																		SCREAMING_SNAKE_CASE__						:       Dict           =   torch.tensor(
																		    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]    )
																		self.assertTrue(torch.allclose(output[:, :3, :3]   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-4    )    )



				@slow
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__						:       int           =   EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""    )
																		model.eval()

																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]    )
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   model(SCREAMING_SNAKE_CASE__    )[0]
																		# compare the actual values for a slice.
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.tensor(
																		    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]    )
																		self.assertTrue(torch.allclose(output[:, :3, :3]   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-4    )    )








'''simple docstring'''
from PIL import Image


def 			a      (			__a ,					__a   )  ->	Image:



					'''simple docstring'''






					def brightness(__a   ) -> float:
										return 128 + level + (c - 128)

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
					return img.point(__a   )


if __name__ == "__main__":
		# Load image
		with Image.open('''image_data/lena.jpg''') as img:
				# Change brightness to 100
				__snake_case							      =     change_brightness(img, 100)
				brigt_img.save('''image_data/lena_brightness.png''', format='''png''')