





import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
       import torch


a__:      Union[str, Any]									=		random.Random()


def 	UpperCamelCase__(       UpperCamelCase__			:       List[str] ,     UpperCamelCase__			:       Optional[Any]=1.0 ,     UpperCamelCase__			:       List[Any]=None ,     UpperCamelCase__			:       Union[str, Any]=None  )->Any:
       if rng is None:
              A__											=						global_rng

       A__											=						[]
       for batch_idx in range(shape[0]  ):
              values.append([]  )
              for _ in range(shape[1]  ):
                     values[-1].append(rng.random() * scale  )

       return values




@require_torch
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=7,__lowerCamelCase=400,__lowerCamelCase=2000,__lowerCamelCase=1,__lowerCamelCase=0.0,__lowerCamelCase=1_6000,__lowerCamelCase=True,__lowerCamelCase=80,__lowerCamelCase=16,__lowerCamelCase=64,__lowerCamelCase="hann_window",__lowerCamelCase=80,__lowerCamelCase=7600,__lowerCamelCase=1E-10,__lowerCamelCase=True,):
        A__											=						parent
        A__											=						batch_size
        A__											=						min_seq_length
        A__											=						max_seq_length
        A__											=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        A__											=						feature_size
        A__											=						padding_value
        A__											=						sampling_rate
        A__											=						do_normalize
        A__											=						num_mel_bins
        A__											=						hop_length
        A__											=						win_length
        A__											=						win_function
        A__											=						fmin
        A__											=						fmax
        A__											=						mel_floor
        A__											=						return_attention_mask



 def 				UpperCamelCase			(							self ):
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "do_normalize": self.do_normalize,
            "num_mel_bins": self.num_mel_bins,
            "hop_length": self.hop_length,
            "win_length": self.win_length,
            "win_function": self.win_function,
            "fmin": self.fmin,
            "fmax": self.fmax,
            "mel_floor": self.mel_floor,
            "return_attention_mask": self.return_attention_mask,
        }



 def 				UpperCamelCase			(							self,__lowerCamelCase=False,__lowerCamelCase=False ):
        def _flatten(__lowerCamelCase ):
               return list(itertools.chain(*__lowerCamelCase ) )

        if equal_length:
               A__											=						floats_list((self.batch_size, self.max_seq_length) )
        else:
               # make sure that inputs increase in size
               A__											=						[
                   _flatten(floats_list((x, self.feature_size) ) )
                   for x in range(self.min_seq_length,self.max_seq_length,self.seq_length_diff )
               ]

        if numpify:
               A__											=						[np.asarray(__lowerCamelCase ) for x in speech_inputs]

        return speech_inputs




 def 				UpperCamelCase			(							self,__lowerCamelCase=False,__lowerCamelCase=False ):
        if equal_length:
               A__											=						[floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
        else:
               # make sure that inputs increase in size
               A__											=						[
                   floats_list((x, self.num_mel_bins) )
                   for x in range(self.min_seq_length,self.max_seq_length,self.seq_length_diff )
               ]

        if numpify:
               A__											=						[np.asarray(__lowerCamelCase ) for x in speech_inputs]

        return speech_inputs




@require_torch
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	SpeechTaFeatureExtractor



 def 				UpperCamelCase			(							self ):
        A__											=						SpeechTaFeatureExtractionTester(self )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        self.assertTrue(np.all(np.mean(__lowerCamelCase,axis=0 ) < 1E-3 ) )
        self.assertTrue(np.all(np.abs(np.var(__lowerCamelCase,axis=0 ) - 1 ) < 1E-3 ) )



 def 				UpperCamelCase			(							self ):
        # Tests that all call wrap to encode_plus and batch_encode_plus
        A__											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        # create three inputs of length 800, 1000, and 1200
        A__											=						[floats_list((1, x) )[0] for x in range(800,1400,200 )]
        A__											=						[np.asarray(__lowerCamelCase ) for speech_input in speech_inputs]

        # Test not batched input
        A__											=						feat_extract(speech_inputs[0],return_tensors='''np''' ).input_values
        A__											=						feat_extract(np_speech_inputs[0],return_tensors='''np''' ).input_values
        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-3 ) )

        # Test batched
        A__											=						feat_extract(__lowerCamelCase,return_tensors='''np''' ).input_values
        A__											=						feat_extract(__lowerCamelCase,return_tensors='''np''' ).input_values
        for enc_seq_a, enc_seq_a in zip(__lowerCamelCase,__lowerCamelCase ):
               self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-3 ) )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        A__											=						[floats_list((1, x) )[0] for x in range(800,1400,200 )]

        A__											=						['''longest''', '''max_length''', '''do_not_pad''']
        A__											=						[None, 1600, None]
        for max_length, padding in zip(__lowerCamelCase,__lowerCamelCase ):
               A__											=						feat_extract(__lowerCamelCase,padding=__lowerCamelCase,max_length=__lowerCamelCase,return_tensors='''np''' )
               A__											=						processed.input_values

               self._check_zero_mean_unit_variance(input_values[0][:800] )
               self.assertTrue(input_values[0][800:].sum() < 1E-6 )
               self._check_zero_mean_unit_variance(input_values[1][:1000] )
               self.assertTrue(input_values[0][1000:].sum() < 1E-6 )
               self._check_zero_mean_unit_variance(input_values[2][:1200] )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        A__											=						range(800,1400,200 )
        A__											=						[floats_list((1, x) )[0] for x in lengths]

        A__											=						['''longest''', '''max_length''', '''do_not_pad''']
        A__											=						[None, 1600, None]

        for max_length, padding in zip(__lowerCamelCase,__lowerCamelCase ):
               A__											=						feat_extract(__lowerCamelCase,max_length=__lowerCamelCase,padding=__lowerCamelCase )
               A__											=						processed.input_values

               self._check_zero_mean_unit_variance(input_values[0][:800] )
               self._check_zero_mean_unit_variance(input_values[1][:1000] )
               self._check_zero_mean_unit_variance(input_values[2][:1200] )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        A__											=						[floats_list((1, x) )[0] for x in range(800,1400,200 )]
        A__											=						feat_extract(
            __lowerCamelCase,truncation=__lowerCamelCase,max_length=1000,padding='''max_length''',return_tensors='''np''' )
        A__											=						processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800] )
        self._check_zero_mean_unit_variance(input_values[1] )
        self._check_zero_mean_unit_variance(input_values[2] )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        A__											=						[floats_list((1, x) )[0] for x in range(800,1400,200 )]
        A__											=						feat_extract(
            __lowerCamelCase,truncation=__lowerCamelCase,max_length=1000,padding='''longest''',return_tensors='''np''' )
        A__											=						processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800] )
        self._check_zero_mean_unit_variance(input_values[1, :1000] )
        self._check_zero_mean_unit_variance(input_values[2] )

        # make sure that if max_length < longest -> then pad to max_length
        self.assertTrue(input_values.shape == (3, 1000) )

        A__											=						[floats_list((1, x) )[0] for x in range(800,1400,200 )]
        A__											=						feat_extract(
            __lowerCamelCase,truncation=__lowerCamelCase,max_length=2000,padding='''longest''',return_tensors='''np''' )
        A__											=						processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800] )
        self._check_zero_mean_unit_variance(input_values[1, :1000] )
        self._check_zero_mean_unit_variance(input_values[2] )

        # make sure that if max_length > longest -> then pad to longest
        self.assertTrue(input_values.shape == (3, 1200) )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        A__											=						np.random.rand(100 ).astype(np.floataa )
        A__											=						np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
               A__											=						feature_extractor.pad([{'''input_values''': inputs}],return_tensors='''np''' )
               self.assertTrue(np_processed.input_values.dtype == np.floataa )
               A__											=						feature_extractor.pad([{'''input_values''': inputs}],return_tensors='''pt''' )
               self.assertTrue(pt_processed.input_values.dtype == torch.floataa )



 def 				UpperCamelCase			(							self ):
        # Tests that all call wrap to encode_plus and batch_encode_plus
        A__											=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        # create three inputs of length 800, 1000, and 1200
        A__											=						[floats_list((1, x) )[0] for x in range(800,1400,200 )]
        A__											=						[np.asarray(__lowerCamelCase ) for speech_input in speech_inputs]

        # Test feature size
        A__											=						feature_extractor(audio_target=__lowerCamelCase,padding=__lowerCamelCase,return_tensors='''np''' ).input_values
        self.assertTrue(input_values.ndim == 3 )
        self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )

        # Test not batched input
        A__											=						feature_extractor(speech_inputs[0],return_tensors='''np''' ).input_values
        A__											=						feature_extractor(np_speech_inputs[0],return_tensors='''np''' ).input_values
        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-3 ) )

        # Test batched
        A__											=						feature_extractor(__lowerCamelCase,return_tensors='''np''' ).input_values
        A__											=						feature_extractor(__lowerCamelCase,return_tensors='''np''' ).input_values
        for enc_seq_a, enc_seq_a in zip(__lowerCamelCase,__lowerCamelCase ):
               self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-3 ) )

        # Test 2-D numpy arrays are batched.
        A__											=						[floats_list((1, x) )[0] for x in (800, 800, 800)]
        A__											=						np.asarray(__lowerCamelCase )
        A__											=						feature_extractor(__lowerCamelCase,return_tensors='''np''' ).input_values
        A__											=						feature_extractor(__lowerCamelCase,return_tensors='''np''' ).input_values
        for enc_seq_a, enc_seq_a in zip(__lowerCamelCase,__lowerCamelCase ):
               self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase,atol=1E-3 ) )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feat_extract_tester.prepare_inputs_for_target()
        A__											=						self.feature_extraction_class(**self.feat_extract_dict )
        A__											=						feat_extract.model_input_names[0]

        A__											=						BatchFeature({input_name: speech_inputs} )

        self.assertTrue(all(len(__lowerCamelCase ) == len(__lowerCamelCase ) for x, y in zip(__lowerCamelCase,processed_features[input_name] ) ) )

        A__											=						self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase )
        A__											=						BatchFeature({input_name: speech_inputs},tensor_type='''np''' )

        A__											=						processed_features[input_name]

        if len(batch_features_input.shape ) < 3:
               A__											=						batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase )
        A__											=						self.feature_extraction_class(**self.feat_extract_dict )
        A__											=						feat_extract.model_input_names[0]

        A__											=						BatchFeature({input_name: speech_inputs},tensor_type='''pt''' )

        A__											=						processed_features[input_name]

        if len(batch_features_input.shape ) < 3:
               A__											=						batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						self.feature_extraction_class(**self.feat_extract_dict )
        A__											=						self.feat_extract_tester.prepare_inputs_for_target()
        A__											=						feat_extract.model_input_names[0]

        A__											=						BatchFeature({input_name: speech_inputs} )

        A__											=						feat_extract.num_mel_bins  # hack!

        A__											=						feat_extract.pad(__lowerCamelCase,padding='''longest''',return_tensors='''np''' )[input_name]
        A__											=						feat_extract.pad(__lowerCamelCase,padding='''longest''',return_tensors='''pt''' )[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feat_extract_dict
        A__											=						True
        A__											=						self.feature_extraction_class(**__lowerCamelCase )
        A__											=						self.feat_extract_tester.prepare_inputs_for_target()
        A__											=						[len(__lowerCamelCase ) for x in speech_inputs]
        A__											=						feat_extract.model_input_names[0]

        A__											=						BatchFeature({input_name: speech_inputs} )

        A__											=						feat_extract.num_mel_bins  # hack!

        A__											=						feat_extract.pad(__lowerCamelCase,padding='''longest''',return_tensors='''np''' )
        self.assertIn('''attention_mask''',__lowerCamelCase )
        self.assertListEqual(list(processed.attention_mask.shape ),list(processed[input_name].shape[:2] ) )
        self.assertListEqual(processed.attention_mask.sum(-1 ).tolist(),__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feat_extract_dict
        A__											=						True
        A__											=						self.feature_extraction_class(**__lowerCamelCase )
        A__											=						self.feat_extract_tester.prepare_inputs_for_target()
        A__											=						[len(__lowerCamelCase ) for x in speech_inputs]
        A__											=						feat_extract.model_input_names[0]

        A__											=						BatchFeature({input_name: speech_inputs} )
        A__											=						min(__lowerCamelCase )

        A__											=						feat_extract.num_mel_bins  # hack!

        A__											=						feat_extract.pad(
            __lowerCamelCase,padding='''max_length''',max_length=__lowerCamelCase,truncation=__lowerCamelCase,return_tensors='''np''' )
        self.assertIn('''attention_mask''',__lowerCamelCase )
        self.assertListEqual(
            list(processed_pad.attention_mask.shape ),[processed_pad[input_name].shape[0], max_length] )
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist(),[max_length for x in speech_inputs] )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        from datasets import load_dataset

        A__											=						load_dataset('''hf-internal-testing/librispeech_asr_dummy''','''clean''',split='''validation''' )
        # automatic decoding with librispeech
        A__											=						ds.sort('''id''' ).select(range(__lowerCamelCase ) )[:num_samples]['''audio''']

        return [x["array"] for x in speech_samples]



 def 				UpperCamelCase			(							self ):
        # fmt: off
        A__											=						torch.tensor(
            [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
             3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
             2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
             4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
             7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
             4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03] )
        # fmt: on

        A__											=						self._load_datasamples(1 )
        A__											=						SpeechTaFeatureExtractor()
        A__											=						feature_extractor(__lowerCamelCase,return_tensors='''pt''' ).input_values
        self.assertEquals(input_values.shape,(1, 9_3680) )
        self.assertTrue(torch.allclose(input_values[0, :30],__lowerCamelCase,atol=1E-6 ) )




 def 				UpperCamelCase			(							self ):
        # fmt: off
        A__											=						torch.tensor(
            [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
             -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
             -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
             -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
        # fmt: on

        A__											=						self._load_datasamples(1 )
        A__											=						SpeechTaFeatureExtractor()
        A__											=						feature_extractor(audio_target=__lowerCamelCase,return_tensors='''pt''' ).input_values
        self.assertEquals(input_values.shape,(1, 366, 80) )
        self.assertTrue(torch.allclose(input_values[0, 0, :30],__lowerCamelCase,atol=1E-4 ) )












import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
       import jax
       import jax.numpy as jnp

       from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=3,__lowerCamelCase=32,__lowerCamelCase=3,__lowerCamelCase=10,__lowerCamelCase=[10, 20, 30, 40],__lowerCamelCase=[1, 1, 2, 1],__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=3,__lowerCamelCase=None,):
        A__											=						parent
        A__											=						batch_size
        A__											=						image_size
        A__											=						num_channels
        A__											=						embeddings_size
        A__											=						hidden_sizes
        A__											=						depths
        A__											=						is_training
        A__											=						use_labels
        A__											=						hidden_act
        A__											=						num_labels
        A__											=						scope
        A__											=						len(__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        A__											=						self.get_config()

        return config, pixel_values



 def 				UpperCamelCase			(							self ):
        return RegNetConfig(
            num_channels=self.num_channels,embeddings_size=self.embeddings_size,hidden_sizes=self.hidden_sizes,depths=self.depths,hidden_act=self.hidden_act,num_labels=self.num_labels,image_size=self.image_size,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						FlaxRegNetModel(config=__lowerCamelCase )
        A__											=						model(__lowerCamelCase )

        # Output shape (b, c, h, w)
        self.parent.assertEqual(
            result.last_hidden_state.shape,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.num_labels
        A__											=						FlaxRegNetForImageClassification(config=__lowerCamelCase )
        A__											=						model(__lowerCamelCase )
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )




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




@require_flax
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						FlaxRegNetModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase )



 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



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )



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



 @unittest.skip(reason='''RegNet 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(__lowerCamelCase )
               A__											=						inspect.signature(model.__call__ )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               A__											=						[*signature.parameters.keys()]

               A__											=						['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        def check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
               A__											=						model_class(__lowerCamelCase )

               A__											=						model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase ) )

               A__											=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               A__											=						self.model_tester.num_stages
               self.assertEqual(len(__lowerCamelCase ),expected_num_stages + 1 )

        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(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )

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

               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )




 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                      A__											=						self._prepare_for_class(__lowerCamelCase,__lowerCamelCase )
                      A__											=						model_class(__lowerCamelCase )

                      @jax.jit
                      def model_jitted(__lowerCamelCase,**__lowerCamelCase ):
                             return model(pixel_values=__lowerCamelCase,**__lowerCamelCase )

                      with self.subTest('''JIT Enabled''' ):
                             A__											=						model_jitted(**__lowerCamelCase ).to_tuple()

                      with self.subTest('''JIT Disabled''' ):
                             with jax.disable_jit():
                                    A__											=						model_jitted(**__lowerCamelCase ).to_tuple()

                      self.assertEqual(len(__lowerCamelCase ),len(__lowerCamelCase ) )
                      for jitted_output, output in zip(__lowerCamelCase,__lowerCamelCase ):
                             self.assertEqual(jitted_output.shape,output.shape )


def 	UpperCamelCase__(       )->Optional[int]:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




@require_flax
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @cached_property
 def 				UpperCamelCase			(							self ):
        return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None




 @slow
 def 				UpperCamelCase			(							self ):
        A__											=						FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )

        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(images=__lowerCamelCase,return_tensors='''np''' )

        A__											=						model(**__lowerCamelCase )

        # verify the logits
        A__											=						(1, 1000)
        self.assertEqual(outputs.logits.shape,__lowerCamelCase )

        A__											=						jnp.array([-0.4180, -1.5051, -3.4836] )

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





