




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 			a__    :






						def __init__(   self		,			_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       parent
											__lowerCAmelCase				   =       1_3
											__lowerCAmelCase				   =       7
											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       9_9
											__lowerCAmelCase				   =       3_2
											__lowerCAmelCase				   =       2
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       3_7
											__lowerCAmelCase				   =       "gelu"
											__lowerCAmelCase				   =       0.1
											__lowerCAmelCase				   =       0.1
											__lowerCAmelCase				   =       5_1_2
											__lowerCAmelCase				   =       1_6
											__lowerCAmelCase				   =       2
											__lowerCAmelCase				   =       0.02
											__lowerCAmelCase				   =       3
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       None






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

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

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

											__lowerCAmelCase				   =       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			):




											"""simple docstring"""



											(
											    (
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							
											)				   =       self.prepare_config_and_inputs()

											__lowerCAmelCase				   =       True
											__lowerCAmelCase				   =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
											__lowerCAmelCase				   =       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		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmModel(config=_A			)
											__lowerCAmelCase				   =       {"input_ids": input_ids, "attention_mask": input_mask}
											__lowerCAmelCase				   =       model(_A			)

											__lowerCAmelCase				   =       [input_ids, input_mask]
											__lowerCAmelCase				   =       model(_A			)

											__lowerCAmelCase				   =       model(_A			)

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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       True

											__lowerCAmelCase				   =       TFEsmModel(config=_A			)
											__lowerCAmelCase				   =       {
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "encoder_hidden_states": encoder_hidden_states,
											    "encoder_attention_mask": encoder_attention_mask,
											}
											__lowerCAmelCase				   =       model(_A			)

											__lowerCAmelCase				   =       [input_ids, input_mask]
											__lowerCAmelCase				   =       model(_A		,			encoder_hidden_states=_A			)

											# Also check the case where encoder outputs are not passed
											__lowerCAmelCase				   =       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		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmForMaskedLM(config=_A			)
											__lowerCAmelCase				   =       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		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.num_labels
											__lowerCAmelCase				   =       TFEsmForTokenClassification(config=_A			)
											__lowerCAmelCase				   =       {"input_ids": input_ids, "attention_mask": input_mask}
											__lowerCAmelCase				   =       model(_A			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											(
											    (
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							(
											    __lowerCAmelCase
											)   ,							
											)				   =       config_and_inputs
											__lowerCAmelCase				   =       {"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict






@require_tf
class 			a__    (					snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    Any					=    (
						    (
						        TFEsmModel,
						        TFEsmForMaskedLM,
						        TFEsmForSequenceClassification,
						        TFEsmForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:    List[Any]					=    (
						    {
						        """feature-extraction""": TFEsmModel,
						        """fill-mask""": TFEsmForMaskedLM,
						        """text-classification""": TFEsmForSequenceClassification,
						        """token-classification""": TFEsmForTokenClassification,
						        """zero-shot""": TFEsmForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_a							:    Union[str, Any]					=    False
						_a							:    List[Any]					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmModelTester(self			)
											__lowerCAmelCase				   =       ConfigTester(self		,			config_class=_A		,			hidden_size=3_7			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase				   =       TFEsmModel.from_pretrained(_A			)
																self.assertIsNotNone(_A			)






						@unittest.skip("Protein models do not support embedding resizing."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						@unittest.skip("Protein models do not support embedding resizing."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase				   =       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
																					__lowerCAmelCase				   =       model.get_bias()
																					assert isinstance(_A		,			_A			)
																					for k, v in name.items():
																										assert isinstance(_A		,			tf.Variable			)
																else:
																					__lowerCAmelCase				   =       model.get_output_embeddings()
																					assert x is None
																					__lowerCAmelCase				   =       model.get_bias()
																					assert name is None






@require_tf
class 			a__    (					unittest.TestCase      ):






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"			)

											__lowerCAmelCase				   =       tf.constant([[0, 1, 2, 3, 4, 5]]			)
											__lowerCAmelCase				   =       model(_A			)[0]
											__lowerCAmelCase				   =       [1, 6, 3_3]
											self.assertEqual(list(output.numpy().shape			)		,			_A			)
											# compare the actual values for a slice.
											__lowerCAmelCase				   =       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			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"			)

											__lowerCAmelCase				   =       tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]			)
											__lowerCAmelCase				   =       model(_A			)[0]
											# compare the actual values for a slice.
											__lowerCAmelCase				   =       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			)			)










import unittest

import numpy as np

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

if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor




class       __lowerCAmelCase				(							unittest.TestCase):






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Optional[Any]=3				,    _lowerCAmelCase:    List[Any]=18				,    _lowerCAmelCase:    int=30				,    _lowerCAmelCase:    Union[str, Any]=4_00				,    _lowerCAmelCase:    Any=True				,    _lowerCAmelCase:    Union[str, Any]=32				,    _lowerCAmelCase:    int=True				,    ):
         lowercase			:Any							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:Any							=  num_channels
         lowercase			:Optional[Any]							=  image_size
         lowercase			:Optional[Any]							=  min_resolution
         lowercase			:str							=  max_resolution
         lowercase			:str							=  do_resize
         lowercase			:Optional[int]							=  size_divisor
         lowercase			:Tuple							=  do_rescale






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return {
             "do_resize": self.do_resize,
             "size_divisor": self.size_divisor,
             "do_rescale": self.do_rescale,
         }







@require_torch
@require_vision
class       __lowerCAmelCase				(							lowerCAmelCase					,       unittest.TestCase):
       _a           =  GLPNImageProcessor if is_vision_available() else None






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:int							=  GLPNImageProcessingTester(self )






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return self.image_processor_tester.prepare_image_processor_dict()






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:Optional[Any]							=  self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "do_resize" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "size_divisor" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "resample" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "do_rescale" ) )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         pass






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         lowercase			:List[str]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    Image.Image )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:str							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         lowercase			:Union[str, Any]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase				,    numpify=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    np.ndarray )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:Optional[int]							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         # Initialize image_processing
         lowercase			:Optional[Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         lowercase			:List[str]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase				,    torchify=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    torch.Tensor )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:Dict							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )



