


from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case				:    List[Any]       				=     logging.get_logger(__name__)







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ['input_values', 'padding_mask']




 def __init__(		self					,  _lowerCamelCase = 1					,  _lowerCamelCase = 2_4000					,  _lowerCamelCase = 0.0					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(feature_size=_lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  padding_value=_lowerCamelCase					,  **_lowerCamelCase    )
       a						:Optional[int] 							=       chunk_length_s
       a						:Union[str, Any] 							=       overlap




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.chunk_length_s is None:
             return None
       else:
             return int(self.chunk_length_s * self.sampling_rate    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.chunk_length_s is None or self.overlap is None:
             return None
       else:
             return max(1					,  int((1.0 - self.overlap) * self.chunk_length    )    )




 def __call__(		self					,  _lowerCamelCase					,  _lowerCamelCase = None					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  ):
       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 audio 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.'''    )

       if padding and truncation:
             raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''    )
       elif padding is None:
             # by default let's pad the inputs
             a						:Optional[Any] 							=       True

       a						:Dict 							=       bool(
           isinstance(_lowerCamelCase					,  (list, tuple)    ) and (isinstance(raw_audio[0]					,  (np.ndarray, tuple, list)    ))    )

       if is_batched:
             a						:Dict 							=       [np.asarray(_lowerCamelCase					,  dtype=np.floataa    ).T for audio in raw_audio]
       elif not is_batched and not isinstance(_lowerCamelCase					,  np.ndarray    ):
             a						:Union[str, Any] 							=       np.asarray(_lowerCamelCase					,  dtype=np.floataa    )
       elif isinstance(_lowerCamelCase					,  np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
             a						:List[Any] 							=       raw_audio.astype(np.floataa    )

       # always return batch
       if not is_batched:
             a						:Tuple 							=       [np.asarray(_lowerCamelCase    ).T]

       # verify inputs are valid
       for idx, example in enumerate(_lowerCamelCase    ):
             if example.ndim > 2:
                   raise ValueError(F'''Expected input shape (channels, length) but got shape {example.shape}'''    )
             if self.feature_size == 1 and example.ndim != 1:
                   raise ValueError(F'''Expected mono audio but example has {example.shape[-1]} channels'''    )
             if self.feature_size == 2 and example.shape[-1] != 2:
                   raise ValueError(F'''Expected stereo audio but example has {example.shape[-1]} channels'''    )

       a						:Tuple 							=       None
       a						:Dict 							=       BatchFeature({'''input_values''': raw_audio}    )
       if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                   a						:Dict 							=       min(array.shape[0] for array in raw_audio    )
                   a						:List[Any] 							=       int(np.floor(max_length / self.chunk_stride    )    )
                   a						:Optional[Any] 							=       (nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                   a						:str 							=       max(array.shape[0] for array in raw_audio    )
                   a						:Union[str, Any] 							=       int(np.ceil(max_length / self.chunk_stride    )    )
                   a						:List[Any] 							=       (nb_step - 1) * self.chunk_stride + self.chunk_length
                   a						:List[Any] 							=       '''max_length'''
             else:
                   a						:str 							=       input_values

        # normal padding on batch
       if padded_inputs is None:
             a						:Any 							=       self.pad(
                 _lowerCamelCase					,  max_length=_lowerCamelCase					,  truncation=_lowerCamelCase					,  padding=_lowerCamelCase					,  return_attention_mask=_lowerCamelCase					,  )
             if padding:
                   a						:Tuple 							=       padded_inputs.pop('''attention_mask'''    )

       a						:Union[str, Any] 							=       []
       for example in padded_inputs.pop('''input_values'''    ):
             if self.feature_size == 1:
                   a						:Optional[int] 							=       example[..., None]
             input_values.append(example.T    )

       a						:int 							=       input_values
       if return_tensors is not None:
             a						:Any 							=       padded_inputs.convert_to_tensors(_lowerCamelCase    )

       return padded_inputs









'''simple docstring'''



import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


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





@slow
@require_flax
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         return f'gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE_	) for s in shape]	)}.npy'







    def       lowercase_				(							self	)				->     Optional[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 4, 64, 64)			,     SCREAMING_SNAKE_CASE_=False	)				->     str:
         __lowerCamelCase   :	List[str]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Tuple								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return image







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="CompVis/stable-diffusion-v1-4"	)				->     Dict:
         __lowerCamelCase   :	Union[str, Any]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		'bf16' if fpaa else None

         __lowerCamelCase				,       __lowerCamelCase   :	str								=		FlaxUNetaDConditionModel.from_pretrained(
             SCREAMING_SNAKE_CASE_			,     subfolder='unet'			,     dtype=SCREAMING_SNAKE_CASE_			,     revision=SCREAMING_SNAKE_CASE_	)
         return model, params







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 77, 7_68)			,     SCREAMING_SNAKE_CASE_=False	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return hidden_states







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
            [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
            [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
            [3, 10_00, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Any								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
            [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
            [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
            [3, 10_00, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		self.get_unet_model(model_id='stabilityai/stable-diffusion-2'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     shape=(4, 4, 96, 96)			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     shape=(4, 77, 10_24)			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Optional[int]								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Tuple								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)






