


import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






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






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxControlNetModel.from_pretrained(
             'lllyasviel/sd-controlnet-canny'					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxStableDiffusionControlNetPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'					,   controlnet=A_					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase    							=controlnet_params

         __UpperCamelCase    							='bird'
         __UpperCamelCase    							=jax.device_count()
         __UpperCamelCase    							=pipe.prepare_text_inputs([prompts] * num_samples    )

         __UpperCamelCase    							=load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'    )
         __UpperCamelCase    							=pipe.prepare_image_inputs([canny_image] * num_samples    )

         __UpperCamelCase    							=jax.random.PRNGKey(0    )
         __UpperCamelCase    							=jax.random.split(A_					,   jax.device_count()    )

         __UpperCamelCase    							=replicate(A_    )
         __UpperCamelCase    							=shard(A_    )
         __UpperCamelCase    							=shard(A_    )

         __UpperCamelCase    							=pipe(
             prompt_ids=A_					,   image=A_					,   params=A_					,   prng_seed=A_					,   num_inference_steps=50					,   jit=A_					,   ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __UpperCamelCase    							=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
         __UpperCamelCase    							=images[0, 253:256, 253:256, -1]

         __UpperCamelCase    							=jnp.asarray(jax.device_get(image_slice.flatten()    )    )
         __UpperCamelCase    							=jnp.array(
             [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
         print(f'output_slice: {output_slice}'    )
         assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxControlNetModel.from_pretrained(
             'lllyasviel/sd-controlnet-openpose'					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxStableDiffusionControlNetPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'					,   controlnet=A_					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase    							=controlnet_params

         __UpperCamelCase    							='Chef in the kitchen'
         __UpperCamelCase    							=jax.device_count()
         __UpperCamelCase    							=pipe.prepare_text_inputs([prompts] * num_samples    )

         __UpperCamelCase    							=load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'    )
         __UpperCamelCase    							=pipe.prepare_image_inputs([pose_image] * num_samples    )

         __UpperCamelCase    							=jax.random.PRNGKey(0    )
         __UpperCamelCase    							=jax.random.split(A_					,   jax.device_count()    )

         __UpperCamelCase    							=replicate(A_    )
         __UpperCamelCase    							=shard(A_    )
         __UpperCamelCase    							=shard(A_    )

         __UpperCamelCase    							=pipe(
             prompt_ids=A_					,   image=A_					,   params=A_					,   prng_seed=A_					,   num_inference_steps=50					,   jit=A_					,   ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __UpperCamelCase    							=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
         __UpperCamelCase    							=images[0, 253:256, 253:256, -1]

         __UpperCamelCase    							=jnp.asarray(jax.device_get(image_slice.flatten()    )    )
         __UpperCamelCase    							=jnp.array(
             [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
         print(f'output_slice: {output_slice}'    )
         assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






'''simple docstring'''




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       '''EncodecFeatureExtractor'''
			UpperCAmelCase_			:     Dict      =       ('''T5Tokenizer''', '''T5TokenizerFast''')



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  self.feature_extractor
					lowerCAmelCase   					=  False



			def 							a_       (   self       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True):




					"""simple docstring"""


					return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase       ,    language=__lowerCAmelCase       ,    no_timestamps=__lowerCAmelCase)



			def __call__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					if self._in_target_context_manager:
							return self.current_processor(*__lowerCAmelCase       ,    **__lowerCAmelCase)

					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""sampling_rate"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""text"""       ,    __lowerCAmelCase)
					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

					if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process.""")

					if text is not None:
							lowerCAmelCase   					=  self.tokenizer(__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is not None:
							lowerCAmelCase   					=  self.feature_extractor(__lowerCAmelCase       ,    *__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is None:
							return inputs

					elif text is None:
							return audio_inputs

					else:
							lowerCAmelCase   					=  audio_inputs["""input_values"""]
							if "padding_mask" in audio_inputs:
									lowerCAmelCase   					=  audio_inputs["""padding_mask"""]
							return inputs



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""padding_mask"""       ,    __lowerCAmelCase)

					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

					if audio_values is not None:
							return self._decode_audio(__lowerCAmelCase       ,    padding_mask=__lowerCAmelCase)
					else:
							return self.tokenizer.batch_decode(*__lowerCAmelCase       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.decode(*__lowerCAmelCase       ,    **__lowerCAmelCase)







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  audio_values.shape

					if padding_mask is None:
							return list(__lowerCAmelCase)

					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)

					# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
					# token (so that the generated audio values are **not** treated as padded tokens)
					lowerCAmelCase   					=  seq_len - padding_mask.shape[-1]
					lowerCAmelCase   					=  1 - self.feature_extractor.padding_value
					lowerCAmelCase   					=  np.pad(__lowerCAmelCase       ,    ((0, 0), (0, difference))       ,    """constant"""       ,    constant_values=__lowerCAmelCase)

					lowerCAmelCase   					=  audio_values.tolist()
					for i in range(__lowerCAmelCase):
							lowerCAmelCase   					=  np.asarray(audio_values[i])[
							    padding_mask[i][None, :] != self.feature_extractor.padding_value
							]
							lowerCAmelCase   					=  sliced_audio.reshape(__lowerCAmelCase       ,    -1)

					return audio_values




