
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 __snake_case		(				unittest.TestCase					):
    def __init__(  self		,snake_case		,snake_case=3		,snake_case=32		,snake_case=3		,snake_case=10		,snake_case=[10, 20, 30, 40]		,snake_case=[1, 1, 2, 1]		,snake_case=True		,snake_case=True		,snake_case="relu"		,snake_case=3		,snake_case=None		,):



           '''simple docstring'''







           lowercase							:					Dict	     =				parent
           lowercase							:					Dict	     =				batch_size
           lowercase							:					int	     =				image_size
           lowercase							:					List[Any]	     =				num_channels
           lowercase							:					List[str]	     =				embeddings_size
           lowercase							:					Tuple	     =				hidden_sizes
           lowercase							:					int	     =				depths
           lowercase							:					Union[str, Any]	     =				is_training
           lowercase							:					Optional[Any]	     =				use_labels
           lowercase							:					List[Any]	     =				hidden_act
           lowercase							:					int	     =				num_labels
           lowercase							:					Any	     =				scope
           lowercase							:					List[str]	     =				len(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           lowercase							:					Any	     =				self.get_config()

           return config, pixel_values
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Any	     =				FlaxRegNetModel(config=snake_case				)
           lowercase							:					Any	     =				model(snake_case				)

           # 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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				self.num_labels
           lowercase							:					int	     =				FlaxRegNetForImageClassification(config=snake_case				)
           lowercase							:					Tuple	     =				model(snake_case				)
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.prepare_config_and_inputs()
           lowercase							:					Optional[Any]	     =				config_and_inputs
           lowercase							:					Optional[int]	     =				{"""pixel_values""": pixel_values}
           return config, inputs_dict






@require_flax
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Any=    (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

    _a		:						Optional[int]=    False
    _a		:						Tuple=    False
    _a		:						List[Any]=    False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				FlaxRegNetModelTester(self				)
           lowercase							:					List[Any]	     =				ConfigTester(self		,config_class=snake_case		,has_text_modality=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case				)
    @unittest.skip(reason="""RegNet does not use inputs_embeds"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""RegNet does not support input and output embeddings"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:					Union[str, Any]	     =				model_class(snake_case				)
                  lowercase							:					str	     =				inspect.signature(model.__call__				)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowercase							:					str	     =				[*signature.parameters.keys()]

                  lowercase							:					str	     =				["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           def check_hidden_states_output(snake_case		,snake_case		,snake_case				):
                  lowercase							:					Dict	     =				model_class(snake_case				)

                  lowercase							:					List[Any]	     =				model(**self._prepare_for_class(snake_case		,snake_case				)				)

                  lowercase							:					Union[str, Any]	     =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  lowercase							:					Tuple	     =				self.model_tester.num_stages
                  self.assertEqual(len(snake_case				)		,expected_num_stages + 1				)

           lowercase							:					Optional[int]	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:					str	     =				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"]
                  lowercase							:					int	     =				True

                  check_hidden_states_output(snake_case		,snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__				):
                         lowercase							:					Optional[int]	     =				self._prepare_for_class(snake_case		,snake_case				)
                         lowercase							:					Optional[int]	     =				model_class(snake_case				)

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

                         with self.subTest("""JIT Enabled"""				):
                                lowercase							:					List[Any]	     =				model_jitted(**snake_case				).to_tuple()

                         with self.subTest("""JIT Disabled"""				):
                                with jax.disable_jit():
                                       lowercase							:					int	     =				model_jitted(**snake_case				).to_tuple()

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







def 						_snake_case(  )			->   int:
       lowercase							:					Dict	     =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
       return image




@require_flax
class __snake_case		(				unittest.TestCase					):
    @cached_property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""				) if is_vision_available() else None
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""				)

           lowercase							:					str	     =				self.default_image_processor
           lowercase							:					List[str]	     =				prepare_img()
           lowercase							:					str	     =				image_processor(images=snake_case		,return_tensors="""np"""				)

           lowercase							:					Optional[int]	     =				model(**snake_case				)

           # verify the logits
           lowercase							:					List[Any]	     =				(1, 1000)
           self.assertEqual(outputs.logits.shape		,snake_case				)

           lowercase							:					int	     =				jnp.array([-0.4_180, -1.5_051, -3.4_836]				)

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


from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowercase  : Tuple						   =							logging.get_logger(__name__)

class __snake_case		(				lowerCAmelCase					):
    _a		:						Optional[Any]=    ["input_features", "attention_mask"]
    def __init__(  self		,snake_case=80		,snake_case=16000		,snake_case=0.0		,snake_case=10		,snake_case=25		,snake_case="hamming_window"		,snake_case=32_768.0		,snake_case=0.97		,snake_case=1.0		,snake_case=True		,snake_case=True		,snake_case=False		,**snake_case		,):



           '''simple docstring'''







           super().__init__(feature_size=snake_case		,sampling_rate=snake_case		,padding_value=snake_case		,**snake_case				)

           lowercase							:					Optional[Any]	     =				feature_size
           lowercase							:					List[Any]	     =				sampling_rate
           lowercase							:					int	     =				padding_value
           lowercase							:					Dict	     =				hop_length
           lowercase							:					List[str]	     =				win_length
           lowercase							:					List[Any]	     =				frame_signal_scale
           lowercase							:					List[Any]	     =				preemphasis_coeff
           lowercase							:					str	     =				mel_floor
           lowercase							:					int	     =				normalize_means
           lowercase							:					List[Any]	     =				normalize_vars
           lowercase							:					List[Any]	     =				win_function
           lowercase							:					int	     =				return_attention_mask

           lowercase							:					Any	     =				win_length * sampling_rate // 1000
           lowercase							:					Tuple	     =				hop_length * sampling_rate // 1000

           lowercase							:					Tuple	     =				optimal_fft_length(self.sample_size				)
           lowercase							:					Dict	     =				(self.n_fft // 2) + 1
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.win_function == "hamming_window":
                  lowercase							:					Optional[Any]	     =				window_function(window_length=self.sample_size		,name=self.win_function		,periodic=snake_case				)
           else:
                  lowercase							:					Optional[Any]	     =				window_function(window_length=self.sample_size		,name=self.win_function				)

           lowercase							:					int	     =				mel_filter_bank(
               num_frequency_bins=self.n_freqs		,num_mel_filters=self.feature_size		,min_frequency=0.0		,max_frequency=self.sampling_rate / 2.0		,sampling_rate=self.sampling_rate		,)

           lowercase							:					Dict	     =				spectrogram(
               one_waveform * self.frame_signal_scale		,window=snake_case		,frame_length=self.sample_size		,hop_length=self.sample_stride		,fft_length=self.n_fft		,center=snake_case		,preemphasis=self.preemphasis_coeff		,mel_filters=snake_case		,mel_floor=self.mel_floor		,log_mel="""log"""		,)
           return msfc_features.T
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           if self.normalize_means:
                  lowercase							:					List[Any]	     =				x[:input_length].mean(axis=0				)
                  lowercase							:					Dict	     =				np.subtract(snake_case		,snake_case				)
           if self.normalize_vars:
                  lowercase							:					List[Any]	     =				x[:input_length].std(axis=0				)
                  lowercase							:					List[Any]	     =				np.divide(snake_case		,snake_case				)

           if input_length < x.shape[0]:
                  lowercase							:					Any	     =				padding_value

           # make sure array is in float32
           lowercase							:					Tuple	     =				x.astype(np.floataa				)

           return x
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
           return [self._normalize_one(snake_case		,snake_case		,self.padding_value				) for x, n in zip(snake_case		,snake_case				)]
    def __call__(  self		,snake_case		,snake_case = False		,snake_case = None		,snake_case = False		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           if sampling_rate is not None:
                  if sampling_rate != self.sampling_rate:
                         raise ValueError(
                             f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                             f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
                             f" {self.sampling_rate} and not {sampling_rate}."				)
           else:
                  logger.warning(
                      """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
                      """Failing to do so can result in silent errors that might be hard to debug."""				)

           lowercase							:					List[Any]	     =				isinstance(snake_case		,np.ndarray				) and len(raw_speech.shape				) > 1
           if is_batched_numpy and len(raw_speech.shape				) > 2:
                  raise ValueError(f"Only mono-channel audio is supported for input to {self}"				)
           lowercase							:					str	     =				is_batched_numpy or (
               isinstance(snake_case		,(list, tuple)				) and (isinstance(raw_speech[0]		,(np.ndarray, tuple, list)				))
           )

           if is_batched:
                  lowercase							:					List[str]	     =				[np.asarray(snake_case		,dtype=np.floataa				) for speech in raw_speech]
           elif not is_batched and not isinstance(snake_case		,np.ndarray				):
                  lowercase							:					int	     =				np.asarray(snake_case		,dtype=np.floataa				)
           elif isinstance(snake_case		,np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
                  lowercase							:					Any	     =				raw_speech.astype(np.floataa				)

           # always return batch
           if not is_batched:
                  lowercase							:					Dict	     =				[raw_speech]

           # extract fbank features
           lowercase							:					Tuple	     =				[self._extract_mfsc_features(snake_case				) for one_waveform in raw_speech]

           # convert into correct format for padding
           lowercase							:					Union[str, Any]	     =				BatchFeature({"""input_features""": features}				)

           lowercase							:					Optional[int]	     =				self.pad(
               snake_case		,padding=snake_case		,max_length=snake_case		,truncation=snake_case		,pad_to_multiple_of=snake_case		,return_attention_mask=snake_case		,**snake_case		,)
           # make sure list is in array format
           lowercase							:					Tuple	     =				padded_inputs.get("""input_features"""				)
           if isinstance(input_features[0]		,snake_case				):
                  lowercase							:					List[Any]	     =				[np.asarray(snake_case		,dtype=np.floataa				) for feature in input_features]

           lowercase							:					int	     =				padded_inputs.get("""attention_mask"""				)
           if attention_mask is not None:
                  lowercase							:					Any	     =				[np.asarray(snake_case		,dtype=np.intaa				) for array in attention_mask]

           if self.normalize_means or self.normalize_vars:
                  lowercase							:					List[str]	     =				(
                      np.array(snake_case		,dtype=np.intaa				)
                      if self._get_padding_strategies(snake_case		,max_length=snake_case				) is not PaddingStrategy.DO_NOT_PAD
                      and padding
                      else None
                  )
                  lowercase							:					List[str]	     =				self.normalize(
                      padded_inputs["""input_features"""]		,attention_mask=snake_case				)

           if return_tensors is not None:
                  lowercase							:					str	     =				padded_inputs.convert_to_tensors(snake_case				)

           return padded_inputs
