


"""simple docstring"""




import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import CvtForImageClassification, CvtModel
				from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.config_class(**self.inputs_dict      )
				self.parent.assertTrue(hasattr(_a							, '''embed_dim'''      )      )
				self.parent.assertTrue(hasattr(_a							, '''num_heads'''      )      )







class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, _a=13							, _a=64							, _a=3							, _a=[16, 48, 96]							, _a=[1, 3, 6]							, _a=[1, 2, 10]							, _a=[7, 3, 3]							, _a=[4, 2, 2]							, _a=[2, 1, 1]							, _a=[2, 2, 2]							, _a=[False, False, True]							, _a=[0.0, 0.0, 0.0]							, _a=0.02							, _a=1E-12							, _a=True							, _a=True							, _a=2							, ):
				__a           =			parent
				__a           =			batch_size
				__a           =			image_size
				__a           =			patch_sizes
				__a           =			patch_stride
				__a           =			patch_padding
				__a           =			is_training
				__a           =			use_labels
				__a           =			num_labels
				__a           =			num_channels
				__a           =			embed_dim
				__a           =			num_heads
				__a           =			stride_kv
				__a           =			depth
				__a           =			cls_token
				__a           =			attention_drop_rate
				__a           =			initializer_range
				__a           =			layer_norm_eps

		def 			__UpperCAmelCase       (  self      ):
				__a           =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

				__a           =			None
				if self.use_labels:
						__a           =			ids_tensor([self.batch_size]							, self.num_labels      )

				__a           =			self.get_config()
				return config, pixel_values, labels

		def 			__UpperCAmelCase       (  self      ):
				return CvtConfig(
				    image_size=self.image_size							, num_labels=self.num_labels							, num_channels=self.num_channels							, embed_dim=self.embed_dim							, num_heads=self.num_heads							, patch_sizes=self.patch_sizes							, patch_padding=self.patch_padding							, patch_stride=self.patch_stride							, stride_kv=self.stride_kv							, depth=self.depth							, cls_token=self.cls_token							, attention_drop_rate=self.attention_drop_rate							, initializer_range=self.initializer_range							, )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a      ):
				__a           =			CvtModel(config=_a      )
				model.to(_a      )
				model.eval()
				__a           =			model(_a      )
				__a           =			(self.image_size, self.image_size)
				__a      ,     __a           =			image_size[0], image_size[1]
				for i in range(len(self.depth      )      ):
						__a           =			floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1      )
						__a           =			floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1      )
				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.embed_dim[-1], height, width)      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a      ):
				__a           =			self.num_labels
				__a           =			CvtForImageClassification(_a      )
				model.to(_a      )
				model.eval()
				__a           =			model(_a							, labels=_a      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)      )





		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.prepare_config_and_inputs()
				__a      ,     __a      ,     __a           =			config_and_inputs
				__a           =			{'''pixel_values''': pixel_values}
				return config, inputs_dict







@require_torch
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Union[str, Any]										=				(CvtModel, CvtForImageClassification) if is_torch_available() else ()
		__UpperCAmelCase		:			List[Any]										=				(
		    {'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__UpperCAmelCase		:			Any										=				False
		__UpperCAmelCase		:			Optional[int]										=				False
		__UpperCAmelCase		:			Union[str, Any]										=				False
		__UpperCAmelCase		:			Optional[Any]										=				False
		__UpperCAmelCase		:			List[str]										=				False

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

		def 			__UpperCAmelCase       (  self      ):
				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()

		def 			__UpperCAmelCase       (  self      ):
				return

		@unittest.skip(reason='''Cvt does not output attentions'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass

		@unittest.skip(reason='''Cvt does not use inputs_embeds'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass

		@unittest.skip(reason='''Cvt does not support input and output embeddings'''      )
		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      )
						__a           =			inspect.signature(model.forward      )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						__a           =			[*signature.parameters.keys()]

						__a           =			['''pixel_values''']
						self.assertListEqual(arg_names[:1]							, _a      )

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

		def 			__UpperCAmelCase       (  self      ):
				def check_hidden_states_output(_a							, _a							, _a      ):
						__a           =			model_class(_a      )
						model.to(_a      )
						model.eval()

						with torch.no_grad():
								__a           =			model(**self._prepare_for_class(_a							, _a      )      )

						__a           =			outputs.hidden_states

						__a           =			len(self.model_tester.depth      )
						self.assertEqual(len(_a      )							, _a      )

						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-3:]      )							, [
						        self.model_tester.embed_dim[0],
						        self.model_tester.image_size // 4,
						        self.model_tester.image_size // 4,
						    ]							, )

				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						__a           =			True
						check_hidden_states_output(_a							, _a							, _a      )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						__a           =			True

						check_hidden_states_output(_a							, _a							, _a      )

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

		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass





		@slow
		def 			__UpperCAmelCase       (  self      ):
				for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__a           =			CvtModel.from_pretrained(_a      )
						self.assertIsNotNone(_a      )







def        lowercase							(		) ->      Optional[Any]:
		__a           =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
		return image




@require_torch
@require_vision
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		@cached_property
		def 			__UpperCAmelCase       (  self      ):
				return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(_a      )

				__a           =			self.default_image_processor
				__a           =			prepare_img()
				__a           =			image_processor(images=_a							, return_tensors='''pt'''      ).to(_a      )

				# forward pass
				with torch.no_grad():
						__a           =			model(**_a      )

				# verify the logits
				__a           =			torch.Size((1, 1_000)      )
				self.assertEqual(outputs.logits.shape							, _a      )

				__a           =			torch.tensor([0.9285, 0.9015, -0.3150]      ).to(_a      )

				self.assertTrue(torch.allclose(outputs.logits[0, :3]							, _a							, atol=1E-4      )      )










"""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      )      )






