

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 YolosImageProcessor

class    _a (       unittest.TestCase							):




      """simple docstring"""

      def __init__(					self				:							Any		,       UpperCAmelCase				:							int		,       UpperCAmelCase				:							List[str]=7		,       UpperCAmelCase				:							Any=3		,       UpperCAmelCase				:							int=30		,       UpperCAmelCase				:							Any=400		,       UpperCAmelCase				:							Optional[int]=True		,       UpperCAmelCase				:							Union[str, Any]=None		,       UpperCAmelCase				:							Optional[Any]=True		,       UpperCAmelCase				:							Tuple=[0.5, 0.5, 0.5]		,       UpperCAmelCase				:							str=[0.5, 0.5, 0.5]		,       UpperCAmelCase				:							Tuple=True		,       UpperCAmelCase				:							Tuple=1 / 255		,       UpperCAmelCase				:							Optional[int]=True		,       ):
            # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
            A_		   =							size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
            A_		   =							parent
            A_		   =							batch_size
            A_		   =							num_channels
            A_		   =							min_resolution
            A_		   =							max_resolution
            A_		   =							do_resize
            A_		   =							size
            A_		   =							do_normalize
            A_		   =							image_mean
            A_		   =							image_std
            A_		   =							do_rescale
            A_		   =							rescale_factor
            A_		   =							do_pad





      def 						__A     (					self				:							List[Any]     ):
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
            }







      def 						__A     (					self				:							Tuple		,       UpperCAmelCase				:							Optional[Any]		,       UpperCAmelCase				:							Tuple=False     ):
            if not batched:
                  A_		   =							image_inputs[0]
                  if isinstance(_a		,       Image.Image     ):
                        A_   ,			A_		   =							image.size
                  else:
                        A_   ,			A_		   =							image.shape[1], image.shape[2]
                  if w < h:
                        A_		   =							int(self.size["shortest_edge"] * h / w     )
                        A_		   =							self.size["shortest_edge"]
                  elif w > h:
                        A_		   =							self.size["shortest_edge"]
                        A_		   =							int(self.size["shortest_edge"] * w / h     )
                  else:
                        A_		   =							self.size["shortest_edge"]
                        A_		   =							self.size["shortest_edge"]

            else:
                  A_		   =							[]
                  for image in image_inputs:
                        A_   ,			A_		   =							self.get_expected_values([image]     )
                        expected_values.append((expected_height, expected_width)     )
                  A_		   =							max(_a		,       key=lambda UpperCAmelCase     : item[0]     )[0]
                  A_		   =							max(_a		,       key=lambda UpperCAmelCase     : item[1]     )[1]

            return expected_height, expected_width




@require_torch
@require_vision
class    _a (       __SCREAMING_SNAKE_CASE			,							unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase    :       Union[str, Any]							=    YolosImageProcessor if is_vision_available() else None





      def 						__A     (					self				:							List[Any]     ):
            A_		   =							YolosImageProcessingTester(self     )





      @property
      def 						__A     (					self				:							Tuple     ):
            return self.image_processor_tester.prepare_image_processor_dict()





      def 						__A     (					self				:							Tuple     ):
            A_		   =							self.image_processing_class(**self.image_processor_dict     )
            self.assertTrue(hasattr(_a		,       "image_mean"     )     )
            self.assertTrue(hasattr(_a		,       "image_std"     )     )
            self.assertTrue(hasattr(_a		,       "do_normalize"     )     )
            self.assertTrue(hasattr(_a		,       "do_resize"     )     )
            self.assertTrue(hasattr(_a		,       "size"     )     )





      def 						__A     (					self				:							Tuple     ):
            A_		   =							self.image_processing_class.from_dict(self.image_processor_dict     )
            self.assertEqual(image_processor.size		,       {"shortest_edge": 18, "longest_edge": 1333}     )
            self.assertEqual(image_processor.do_pad		,       _a     )

            A_		   =							self.image_processing_class.from_dict(
                self.image_processor_dict		,       size=42		,       max_size=84		,       pad_and_return_pixel_mask=_a     )
            self.assertEqual(image_processor.size		,       {"shortest_edge": 42, "longest_edge": 84}     )
            self.assertEqual(image_processor.do_pad		,       _a     )





      def 						__A     (					self				:							List[str]     ):
            pass





      def 						__A     (					self				:							Dict     ):
            # Initialize image_processing
            A_		   =							self.image_processing_class(**self.image_processor_dict     )
            # create random PIL images
            A_		   =							prepare_image_inputs(self.image_processor_tester		,       equal_resolution=_a     )
            for image in image_inputs:
                  self.assertIsInstance(_a		,       Image.Image     )

            # Test not batched input
            A_		   =							image_processing(image_inputs[0]		,       return_tensors="pt"     ).pixel_values

            A_   ,			A_		   =							self.image_processor_tester.get_expected_values(_a     )

            self.assertEqual(
                encoded_images.shape		,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,       )

            # Test batched
            A_   ,			A_		   =							self.image_processor_tester.get_expected_values(_a		,       batched=_a     )

            A_		   =							image_processing(_a		,       return_tensors="pt"     ).pixel_values
            self.assertEqual(
                encoded_images.shape		,       (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )		,       )





      def 						__A     (					self				:							List[str]     ):
            # Initialize image_processing
            A_		   =							self.image_processing_class(**self.image_processor_dict     )
            # create random numpy tensors
            A_		   =							prepare_image_inputs(self.image_processor_tester		,       equal_resolution=_a		,       numpify=_a     )
            for image in image_inputs:
                  self.assertIsInstance(_a		,       np.ndarray     )

            # Test not batched input
            A_		   =							image_processing(image_inputs[0]		,       return_tensors="pt"     ).pixel_values

            A_   ,			A_		   =							self.image_processor_tester.get_expected_values(_a     )

            self.assertEqual(
                encoded_images.shape		,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,       )

            # Test batched
            A_		   =							image_processing(_a		,       return_tensors="pt"     ).pixel_values

            A_   ,			A_		   =							self.image_processor_tester.get_expected_values(_a		,       batched=_a     )

            self.assertEqual(
                encoded_images.shape		,       (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )		,       )





      def 						__A     (					self				:							Optional[int]     ):
            # Initialize image_processing
            A_		   =							self.image_processing_class(**self.image_processor_dict     )
            # create random PyTorch tensors
            A_		   =							prepare_image_inputs(self.image_processor_tester		,       equal_resolution=_a		,       torchify=_a     )
            for image in image_inputs:
                  self.assertIsInstance(_a		,       torch.Tensor     )

            # Test not batched input
            A_		   =							image_processing(image_inputs[0]		,       return_tensors="pt"     ).pixel_values

            A_   ,			A_		   =							self.image_processor_tester.get_expected_values(_a     )

            self.assertEqual(
                encoded_images.shape		,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,       )

            # Test batched
            A_		   =							image_processing(_a		,       return_tensors="pt"     ).pixel_values

            A_   ,			A_		   =							self.image_processor_tester.get_expected_values(_a		,       batched=_a     )

            self.assertEqual(
                encoded_images.shape		,       (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )		,       )





      def 						__A     (					self				:							int     ):
            # Initialize image_processings
            A_		   =							self.image_processing_class(**self.image_processor_dict     )
            A_		   =							self.image_processing_class(do_resize=_a		,       do_normalize=_a		,       do_rescale=_a     )
            # create random PyTorch tensors
            A_		   =							prepare_image_inputs(self.image_processor_tester		,       equal_resolution=_a		,       torchify=_a     )
            for image in image_inputs:
                  self.assertIsInstance(_a		,       torch.Tensor     )

            # Test whether the method "pad" and calling the image processor return the same tensors
            A_		   =							image_processing_a.pad(_a		,       return_tensors="pt"     )
            A_		   =							image_processing_a(_a		,       return_tensors="pt"     )

            self.assertTrue(
                torch.allclose(encoded_images_with_method["pixel_values"]		,       encoded_images["pixel_values"]		,       atol=1E-4     )     )





      @slow
      def 						__A     (					self				:							Union[str, Any]     ):
            # prepare image and target
            A_		   =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
            with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,       "r"     ) as f:
                  A_		   =							json.loads(f.read()     )

            A_		   =							{"image_id": 39769, "annotations": target}

            # encode them
            A_		   =							YolosImageProcessor.from_pretrained("hustvl/yolos-small"     )
            A_		   =							image_processing(images=_a		,       annotations=_a		,       return_tensors="pt"     )

            # verify pixel values
            A_		   =							torch.Size([1, 3, 800, 1066]     )
            self.assertEqual(encoding["pixel_values"].shape		,       _a     )

            A_		   =							torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
            self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,       _a		,       atol=1E-4     )     )

            # verify area
            A_		   =							torch.tensor([5_887.9_600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,       _a     )     )
            # verify boxes
            A_		   =							torch.Size([6, 4]     )
            self.assertEqual(encoding["labels"][0]["boxes"].shape		,       _a     )
            A_		   =							torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,       _a		,       atol=1E-3     )     )
            # verify image_id
            A_		   =							torch.tensor([39769]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,       _a     )     )
            # verify is_crowd
            A_		   =							torch.tensor([0, 0, 0, 0, 0, 0]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,       _a     )     )
            # verify class_labels
            A_		   =							torch.tensor([75, 75, 63, 65, 17, 17]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,       _a     )     )
            # verify orig_size
            A_		   =							torch.tensor([480, 640]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,       _a     )     )
            # verify size
            A_		   =							torch.tensor([800, 1066]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,       _a     )     )







      @slow
      def 						__A     (					self				:							Optional[Any]     ):
            # prepare image, target and masks_path
            A_		   =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
            with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"		,       "r"     ) as f:
                  A_		   =							json.loads(f.read()     )

            A_		   =							{"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

            A_		   =							pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"     )

            # encode them
            A_		   =							YolosImageProcessor(format="coco_panoptic"     )
            A_		   =							image_processing(images=_a		,       annotations=_a		,       masks_path=_a		,       return_tensors="pt"     )

            # verify pixel values
            A_		   =							torch.Size([1, 3, 800, 1066]     )
            self.assertEqual(encoding["pixel_values"].shape		,       _a     )

            A_		   =							torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
            self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,       _a		,       atol=1E-4     )     )

            # verify area
            A_		   =							torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5_879.6_562, 7_634.1_147]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,       _a     )     )
            # verify boxes
            A_		   =							torch.Size([6, 4]     )
            self.assertEqual(encoding["labels"][0]["boxes"].shape		,       _a     )
            A_		   =							torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,       _a		,       atol=1E-3     )     )
            # verify image_id
            A_		   =							torch.tensor([39769]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,       _a     )     )
            # verify is_crowd
            A_		   =							torch.tensor([0, 0, 0, 0, 0, 0]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,       _a     )     )
            # verify class_labels
            A_		   =							torch.tensor([17, 17, 63, 75, 75, 93]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,       _a     )     )
            # verify masks
            A_		   =							822873
            self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,       _a     )
            # verify orig_size
            A_		   =							torch.tensor([480, 640]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,       _a     )     )
            # verify size
            A_		   =							torch.tensor([800, 1066]     )
            self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,       _a     )     )



"""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							, _a							, ):
				__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 			__UpperCAmelCase       (  self      ):
				__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 			__UpperCAmelCase       (  self      ):
				(
				    (
				    __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 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__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 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, ):
				__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 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__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 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__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 			__UpperCAmelCase       (  self      ):
				__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					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, hidden_size=37      )

		def 			__UpperCAmelCase       (  self      ):
				self.config_tester.run_common_tests()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_a      )

		@slow
		def 			__UpperCAmelCase       (  self      ):
				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 			__UpperCAmelCase       (  self      ):
				pass

		@unittest.skip('''Protein models do not support embedding resizing.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass





		def 			__UpperCAmelCase       (  self      ):
				__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 			__UpperCAmelCase       (  self      ):
				__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_1518, -10.58_9814, -6.467_1307],
				            [-6.396_7156, -13.91_1377, -1.121_1915],
				            [-7.78_1247, -13.95_1557, -3.74_0592],
				        ]
				    ]      )
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							, expected_slice.numpy()							, atol=1E-2      )      )





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__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(
				    [
				        [
				            [0.1444_3092, 0.5412_5327, 0.324_7739],
				            [0.3034_0484, 0.0052_6676, 0.3107_7722],
				            [0.3227_8043, -0.2498_7096, 0.341_4628],
				        ]
				    ]      )
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							, expected_slice.numpy()							, atol=1E-4      )      )






