




import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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 (
						    MODEL_MAPPING,
						    SegformerForImageClassification,
						    SegformerForSemanticSegmentation,
						    SegformerModel,
						)
						from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import SegformerImageProcessor







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      self.config_class(**self.inputs_dict )
							self.parent.assertTrue(hasattr(snake_case__      ,'hidden_sizes' ) )
							self.parent.assertTrue(hasattr(snake_case__      ,'num_attention_heads' ) )
							self.parent.assertTrue(hasattr(snake_case__      ,'num_encoder_blocks' ) )







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__      ,snake_case__=13      ,snake_case__=64      ,snake_case__=3      ,snake_case__=4      ,snake_case__=[2, 2, 2, 2]      ,snake_case__=[8, 4, 2, 1]      ,snake_case__=[16, 32, 64, 128]      ,snake_case__=[1, 4, 8, 16]      ,snake_case__=[1, 2, 4, 8]      ,snake_case__=True      ,snake_case__=True      ,snake_case__="gelu"      ,snake_case__=0.1      ,snake_case__=0.1      ,snake_case__=0.02      ,snake_case__=3      ,snake_case__=None      ,):
							SCREAMING_SNAKE_CASE_   : Dict					  =      parent
							SCREAMING_SNAKE_CASE_   : Tuple					  =      batch_size
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      image_size
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      num_channels
							SCREAMING_SNAKE_CASE_   : str					  =      num_encoder_blocks
							SCREAMING_SNAKE_CASE_   : List[str]					  =      sr_ratios
							SCREAMING_SNAKE_CASE_   : int					  =      depths
							SCREAMING_SNAKE_CASE_   : int					  =      hidden_sizes
							SCREAMING_SNAKE_CASE_   : str					  =      downsampling_rates
							SCREAMING_SNAKE_CASE_   : Tuple					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      is_training
							SCREAMING_SNAKE_CASE_   : List[str]					  =      use_labels
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      hidden_dropout_prob
							SCREAMING_SNAKE_CASE_   : Dict					  =      attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_   : int					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : List[str]					  =      num_labels
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      scope






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							SCREAMING_SNAKE_CASE_   : Dict					  =      None
							if self.use_labels:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      ids_tensor([self.batch_size, self.image_size, self.image_size]      ,self.num_labels )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.get_config()
							return config, pixel_values, labels






				def 		snake_case	(			self ):
							return SegformerConfig(
							    image_size=self.image_size      ,num_channels=self.num_channels      ,num_encoder_blocks=self.num_encoder_blocks      ,depths=self.depths      ,hidden_sizes=self.hidden_sizes      ,num_attention_heads=self.num_attention_heads      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,initializer_range=self.initializer_range      ,)






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      SegformerModel(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape      ,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.num_labels
							SCREAMING_SNAKE_CASE_   : List[str]					  =      SegformerForSemanticSegmentation(snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      model(snake_case__ )
							self.parent.assertEqual(
							    result.logits.shape      ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      model(snake_case__      ,labels=snake_case__ )
							self.parent.assertEqual(
							    result.logits.shape      ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
							self.parent.assertGreater(result.loss      ,0.0 )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      1
							SCREAMING_SNAKE_CASE_   : List[str]					  =      SegformerForSemanticSegmentation(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Any					  =      torch.randint(0      ,1      ,(self.batch_size, self.image_size, self.image_size) ).to(snake_case__ )
							SCREAMING_SNAKE_CASE_   : int					  =      model(snake_case__      ,labels=snake_case__ )
							self.parent.assertGreater(result.loss      ,0.0 )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      config_and_inputs
							SCREAMING_SNAKE_CASE_   : Dict					  =      {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class 				lowerCAmelCase_       (					lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase							):
				__a     :       Any						=     (
				    (
				        SegformerModel,
				        SegformerForSemanticSegmentation,
				        SegformerForImageClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__a     :       Union[str, Any]						=     (
				    {
				        "feature-extraction": SegformerModel,
				        "image-classification": SegformerForImageClassification,
				        "image-segmentation": SegformerForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__a     :       Tuple						=     True
				__a     :       List[Any]						=     False
				__a     :       Any						=     False
				__a     :       Tuple						=     False






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      SegformerModelTester(self )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      SegformerConfigTester(self      ,config_class=snake_case__ )






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






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*snake_case__ )






				@unittest.skip('SegFormer does not use inputs_embeds' )
				def 		snake_case	(			self ):
							pass






				@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods' )
				def 		snake_case	(			self ):
							pass






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   : Dict					  =      model_class(snake_case__ )
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      inspect.signature(model.forward )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      [*signature.parameters.keys()]

										SCREAMING_SNAKE_CASE_   : str					  =      ['pixel_values']
										self.assertListEqual(arg_names[:1]      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE_   : Dict					  =      True

							for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   : Any					  =      True
										SCREAMING_SNAKE_CASE_   : Any					  =      False
										SCREAMING_SNAKE_CASE_   : Any					  =      True
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model_class(snake_case__ )
										model.to(snake_case__ )
										model.eval()
										with torch.no_grad():
													SCREAMING_SNAKE_CASE_   : Any					  =      model(**self._prepare_for_class(snake_case__      ,snake_case__ ) )
										SCREAMING_SNAKE_CASE_   : Dict					  =      outputs.attentions

										SCREAMING_SNAKE_CASE_   : Dict					  =      sum(self.model_tester.depths )
										self.assertEqual(len(snake_case__ )      ,snake_case__ )

										# check that output_attentions also work using config
										del inputs_dict["output_attentions"]
										SCREAMING_SNAKE_CASE_   : int					  =      True
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model_class(snake_case__ )
										model.to(snake_case__ )
										model.eval()
										with torch.no_grad():
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model(**self._prepare_for_class(snake_case__      ,snake_case__ ) )
										SCREAMING_SNAKE_CASE_   : str					  =      outputs.attentions

										self.assertEqual(len(snake_case__ )      ,snake_case__ )

										# verify the first attentions (first block, first layer)
										SCREAMING_SNAKE_CASE_   : List[str]					  =      (self.model_tester.image_size // 4) ** 2
										SCREAMING_SNAKE_CASE_   : Tuple					  =      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
										self.assertListEqual(
										    list(attentions[0].shape[-3:] )      ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,)

										# verify the last attentions (last block, last layer)
										SCREAMING_SNAKE_CASE_   : Any					  =      (self.model_tester.image_size // 32) ** 2
										SCREAMING_SNAKE_CASE_   : Tuple					  =      (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
										self.assertListEqual(
										    list(attentions[-1].shape[-3:] )      ,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]      ,)
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      len(snake_case__ )

										# Check attention is always last and order is fine
										SCREAMING_SNAKE_CASE_   : int					  =      True
										SCREAMING_SNAKE_CASE_   : str					  =      True
										SCREAMING_SNAKE_CASE_   : str					  =      model_class(snake_case__ )
										model.to(snake_case__ )
										model.eval()
										with torch.no_grad():
													SCREAMING_SNAKE_CASE_   : str					  =      model(**self._prepare_for_class(snake_case__      ,snake_case__ ) )

										self.assertEqual(out_len + 1      ,len(snake_case__ ) )

										SCREAMING_SNAKE_CASE_   : List[str]					  =      outputs.attentions

										self.assertEqual(len(snake_case__ )      ,snake_case__ )
										# verify the first attentions (first block, first layer)
										SCREAMING_SNAKE_CASE_   : Any					  =      (self.model_tester.image_size // 4) ** 2
										SCREAMING_SNAKE_CASE_   : Any					  =      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
										self.assertListEqual(
										    list(self_attentions[0].shape[-3:] )      ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,)






				def 		snake_case	(			self ):
							def check_hidden_states_output(snake_case__      ,snake_case__      ,snake_case__ ):
										SCREAMING_SNAKE_CASE_   : int					  =      model_class(snake_case__ )
										model.to(snake_case__ )
										model.eval()

										with torch.no_grad():
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      model(**self._prepare_for_class(snake_case__      ,snake_case__ ) )

										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      outputs.hidden_states

										SCREAMING_SNAKE_CASE_   : List[str]					  =      self.model_tester.num_encoder_blocks
										self.assertEqual(len(snake_case__ )      ,snake_case__ )

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

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Any					  =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      True
										check_hidden_states_output(snake_case__      ,snake_case__      ,snake_case__ )

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										SCREAMING_SNAKE_CASE_   : Any					  =      True

										check_hidden_states_output(snake_case__      ,snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							if not self.model_tester.is_training:
										return

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Tuple					  =      self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE_   : Tuple					  =      True

							for model_class in self.all_model_classes:
										if model_class in get_values(snake_case__ ):
													continue

										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      model_class(snake_case__ )
										model.to(snake_case__ )
										model.train()
										SCREAMING_SNAKE_CASE_   : Any					  =      self._prepare_for_class(snake_case__      ,snake_case__      ,return_labels=snake_case__ )
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model(**snake_case__ ).loss
										loss.backward()






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






				@slow
				def 		snake_case	(			self ):
							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   : Any					  =      SegformerModel.from_pretrained(snake_case__ )
										self.assertIsNotNone(snake_case__ )





def        __UpperCAmelCase						(							)       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
			return image







@require_torch
class 				lowerCAmelCase_       (					unittest.TestCase							):






				@slow
				def 		snake_case	(			self ):
							# only resize + normalize
							SCREAMING_SNAKE_CASE_   : List[str]					  =      SegformerImageProcessor(
							    image_scale=(512, 512)      ,keep_ratio=snake_case__      ,align=snake_case__      ,do_random_crop=snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512' ).to(
							    snake_case__ )

							SCREAMING_SNAKE_CASE_   : Dict					  =      prepare_img()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      image_processor(images=snake_case__      ,return_tensors='pt' )
							SCREAMING_SNAKE_CASE_   : Any					  =      encoded_inputs.pixel_values.to(snake_case__ )

							with torch.no_grad():
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model(snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.Size((1, model.config.num_labels, 128, 128) )
							self.assertEqual(outputs.logits.shape      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch.tensor(
							    [
							        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
							        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
							        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
							    ] ).to(snake_case__ )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,snake_case__      ,atol=1E-4 ) )






				@slow
				def 		snake_case	(			self ):
							# only resize + normalize
							SCREAMING_SNAKE_CASE_   : Dict					  =      SegformerImageProcessor(
							    image_scale=(512, 512)      ,keep_ratio=snake_case__      ,align=snake_case__      ,do_random_crop=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Dict					  =      SegformerForSemanticSegmentation.from_pretrained(
							    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024' ).to(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      prepare_img()
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      image_processor(images=snake_case__      ,return_tensors='pt' )
							SCREAMING_SNAKE_CASE_   : int					  =      encoded_inputs.pixel_values.to(snake_case__ )

							with torch.no_grad():
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model(snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.Size((1, model.config.num_labels, 128, 128) )
							self.assertEqual(outputs.logits.shape      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch.tensor(
							    [
							        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
							        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
							        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
							    ] ).to(snake_case__ )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,snake_case__      ,atol=1E-1 ) )






				@slow
				def 		snake_case	(			self ):
							# only resize + normalize
							SCREAMING_SNAKE_CASE_   : List[str]					  =      SegformerImageProcessor(
							    image_scale=(512, 512)      ,keep_ratio=snake_case__      ,align=snake_case__      ,do_random_crop=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512' ).to(
							    snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      prepare_img()
							SCREAMING_SNAKE_CASE_   : Any					  =      image_processor(images=snake_case__      ,return_tensors='pt' )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      encoded_inputs.pixel_values.to(snake_case__ )

							with torch.no_grad():
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      model(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      outputs.logits.detach().cpu()

							SCREAMING_SNAKE_CASE_   : List[str]					  =      image_processor.post_process_semantic_segmentation(outputs=snake_case__      ,target_sizes=[(500, 300)] )
							SCREAMING_SNAKE_CASE_   : int					  =      torch.Size((500, 300) )
							self.assertEqual(segmentation[0].shape      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : int					  =      image_processor.post_process_semantic_segmentation(outputs=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Dict					  =      torch.Size((128, 128) )
							self.assertEqual(segmentation[0].shape      ,snake_case__ )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__ :   Optional[Any]						=							{
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   str						=							['''WhisperTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   List[Any]						=							[
						    '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''WhisperForConditionalGeneration''',
						    '''WhisperModel''',
						    '''WhisperPreTrainedModel''',
						    '''WhisperForAudioClassification''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Tuple						=							[
						    '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFWhisperForConditionalGeneration''',
						    '''TFWhisperModel''',
						    '''TFWhisperPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   List[str]						=							[
						    '''FlaxWhisperForConditionalGeneration''',
						    '''FlaxWhisperModel''',
						    '''FlaxWhisperPreTrainedModel''',
						    '''FlaxWhisperForAudioClassification''',
						]


if TYPE_CHECKING:
						from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
						from .feature_extraction_whisper import WhisperFeatureExtractor
						from .processing_whisper import WhisperProcessor
						from .tokenization_whisper import WhisperTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_whisper_fast import WhisperTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_whisper import (
												    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WhisperForAudioClassification,
												    WhisperForConditionalGeneration,
												    WhisperModel,
												    WhisperPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_whisper import (
												    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFWhisperForConditionalGeneration,
												    TFWhisperModel,
												    TFWhisperPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_whisper import (
												    FlaxWhisperForAudioClassification,
												    FlaxWhisperForConditionalGeneration,
												    FlaxWhisperModel,
												    FlaxWhisperPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :   Optional[Any]						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



