






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase		:  Dict						=		logging.get_logger(__name__)

_UpperCamelCase		:  List[Any]						=		{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}

class        a     (   a_	):
    UpperCAmelCase_ :     Any	     ="speech_to_text"
    UpperCAmelCase_ :     Dict	     =["past_key_values"]
    UpperCAmelCase_ :     Union[str, Any]	     ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







    def __init__(			self							,		_lowerCamelCase=1_0_0_0_0							,		_lowerCamelCase=1_2							,		_lowerCamelCase=2_0_4_8							,		_lowerCamelCase=4							,		_lowerCamelCase=6							,		_lowerCamelCase=2_0_4_8							,		_lowerCamelCase=4							,		_lowerCamelCase=0.0							,		_lowerCamelCase=0.0							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase="relu"							,		_lowerCamelCase=2_5_6							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.0							,		_lowerCamelCase=0.0							,		_lowerCamelCase=0.0_2							,		_lowerCamelCase=2							,		_lowerCamelCase=True							,		_lowerCamelCase=1							,		_lowerCamelCase=0							,		_lowerCamelCase=2							,		_lowerCamelCase=6_0_0_0							,		_lowerCamelCase=1_0_2_4							,		_lowerCamelCase=2							,		_lowerCamelCase=(5, 5)							,		_lowerCamelCase=1_0_2_4							,		_lowerCamelCase=8_0							,		_lowerCamelCase=1							,		**_lowerCamelCase							,		):
      lowercase							    =       vocab_size
      lowercase							    =       d_model
      lowercase							    =       encoder_ffn_dim
      lowercase							    =       encoder_layers
      lowercase							    =       encoder_attention_heads
      lowercase							    =       decoder_ffn_dim
      lowercase							    =       decoder_layers
      lowercase							    =       decoder_attention_heads
      lowercase							    =       dropout
      lowercase							    =       attention_dropout
      lowercase							    =       activation_dropout
      lowercase							    =       activation_function
      lowercase							    =       init_std
      lowercase							    =       encoder_layerdrop
      lowercase							    =       decoder_layerdrop
      lowercase							    =       use_cache
      lowercase							    =       encoder_layers
      lowercase							    =       scale_embedding  # scale factor will be sqrt(d_model) if True
      lowercase							    =       max_source_positions
      lowercase							    =       max_target_positions
      lowercase							    =       num_conv_layers
      lowercase							    =       list(_lowerCamelCase       )
      lowercase							    =       conv_channels
      lowercase							    =       input_feat_per_channel
      lowercase							    =       input_channels

      if len(self.conv_kernel_sizes       ) != self.num_conv_layers:
        raise ValueError(
            'Configuration for convolutional module is incorrect. '
            'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
            F'but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes       )}`, '
            F'`config.num_conv_layers = {self.num_conv_layers}`.'       )

      super().__init__(
          pad_token_id=_lowerCamelCase							,		bos_token_id=_lowerCamelCase							,		eos_token_id=_lowerCamelCase							,		is_encoder_decoder=_lowerCamelCase							,		decoder_start_token_id=_lowerCamelCase							,		**_lowerCamelCase							,		)










"""simple docstring"""



import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
    import jax
    import jax.numpy as jnp
    from flax.jax_utils import replicate
    from flax.training.common_utils import shard

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







    def 			UpperCamelCase_      (			self       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()







    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     lowercase							    =       FlaxStableDiffusionPipeline.from_pretrained(
          'stabilityai/stable-diffusion-2'							,		revision='bf16'							,		dtype=jnp.bfloataa							,		)

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

      lowercase							    =       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
      lowercase							    =       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       'stabilityai/stable-diffusion-2'
      lowercase    ,     lowercase							    =       FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCamelCase							,		subfolder='scheduler'       )
      lowercase    ,     lowercase							    =       FlaxStableDiffusionPipeline.from_pretrained(
          _lowerCamelCase							,		scheduler=_lowerCamelCase							,		revision='bf16'							,		dtype=jnp.bfloataa							,		)
      lowercase							    =       scheduler_params

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

      lowercase							    =       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
      lowercase							    =       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2


