
'''simple docstring'''







import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY






@is_pipeline_test
class  lowerCAmelCase__   (  unittest.TestCase     ):
					lowerCAmelCase_        =     MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
					lowerCAmelCase_        =     TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     str													=       AudioClassificationPipeline(model=__SCREAMING_SNAKE_CASE				,							feature_extractor=__SCREAMING_SNAKE_CASE		)

											# test with a raw waveform
											lowercase_	:     Tuple													=       np.zeros((3_40_00,)		)
											lowercase_	:     str													=       np.zeros((1_40_00,)		)
											return audio_classifier, [audioa, audio]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     List[str]													=       examples
											lowercase_	:     Tuple													=       audio_classifier(__SCREAMING_SNAKE_CASE		)
											# by default a model is initialized with num_labels=2
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        {'''score''': ANY(__SCREAMING_SNAKE_CASE		), '''label''': ANY(__SCREAMING_SNAKE_CASE		)},
											        {'''score''': ANY(__SCREAMING_SNAKE_CASE		), '''label''': ANY(__SCREAMING_SNAKE_CASE		)},
											    ]				,							)
											lowercase_	:     int													=       audio_classifier(__SCREAMING_SNAKE_CASE				,							top_k=1		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        {'''score''': ANY(__SCREAMING_SNAKE_CASE		), '''label''': ANY(__SCREAMING_SNAKE_CASE		)},
											    ]				,							)

											self.run_torchaudio(__SCREAMING_SNAKE_CASE		)






					@require_torchaudio
					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											import datasets

											# test with a local file
											lowercase_	:     Optional[int]													=       datasets.load_dataset('''hf-internal-testing/librispeech_asr_dummy'''				,							'''clean'''				,							split='''validation'''		)
											lowercase_	:     Any													=       dataset[0]['''audio''']['''array''']
											lowercase_	:     Tuple													=       audio_classifier(__SCREAMING_SNAKE_CASE		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        {'''score''': ANY(__SCREAMING_SNAKE_CASE		), '''label''': ANY(__SCREAMING_SNAKE_CASE		)},
											        {'''score''': ANY(__SCREAMING_SNAKE_CASE		), '''label''': ANY(__SCREAMING_SNAKE_CASE		)},
											    ]				,							)






					@require_torch
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       '''anton-l/wav2vec2-random-tiny-classifier'''

											lowercase_	:     str													=       pipeline('''audio-classification'''				,							model=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       np.ones((80_00,)		)
											lowercase_	:     List[Any]													=       audio_classifier(__SCREAMING_SNAKE_CASE				,							top_k=4		)

											lowercase_	:     Dict													=       [
											    {'''score''': 0.0_842, '''label''': '''no'''},
											    {'''score''': 0.0_838, '''label''': '''up'''},
											    {'''score''': 0.0_837, '''label''': '''go'''},
											    {'''score''': 0.0_834, '''label''': '''right'''},
											]
											lowercase_	:     Optional[int]													=       [
											    {'''score''': 0.0_845, '''label''': '''stop'''},
											    {'''score''': 0.0_844, '''label''': '''on'''},
											    {'''score''': 0.0_841, '''label''': '''right'''},
											    {'''score''': 0.0_834, '''label''': '''left'''},
											]
											self.assertIn(nested_simplify(__SCREAMING_SNAKE_CASE				,							decimals=4		)				,							[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)

											lowercase_	:     List[str]													=       {'''array''': np.ones((80_00,)		), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
											lowercase_	:     Dict													=       audio_classifier(__SCREAMING_SNAKE_CASE				,							top_k=4		)
											self.assertIn(nested_simplify(__SCREAMING_SNAKE_CASE				,							decimals=4		)				,							[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)






					@require_torch
					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											import datasets

											lowercase_	:     Any													=       '''superb/wav2vec2-base-superb-ks'''

											lowercase_	:     Optional[Any]													=       pipeline('''audio-classification'''				,							model=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       datasets.load_dataset('''anton-l/superb_dummy'''				,							'''ks'''				,							split='''test'''		)

											lowercase_	:     int													=       np.array(dataset[3]['''speech''']				,							dtype=np.floataa		)
											lowercase_	:     Optional[Any]													=       audio_classifier(__SCREAMING_SNAKE_CASE				,							top_k=4		)
											self.assertEqual(
											    nested_simplify(__SCREAMING_SNAKE_CASE				,							decimals=3		)				,							[
											        {'''score''': 0.981, '''label''': '''go'''},
											        {'''score''': 0.007, '''label''': '''up'''},
											        {'''score''': 0.006, '''label''': '''_unknown_'''},
											        {'''score''': 0.001, '''label''': '''down'''},
											    ]				,							)






					@require_tf
					@unittest.skip('''Audio classification is not implemented for TF'''		)
					def 	_snake_case    (   self		):


											"""simple docstring"""





											pass







"""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      )      )






