
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 							A__		(	unittest.TestCase):
      def 						UpperCamelCase__							(      self      ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      FlaxControlNetModel.from_pretrained(
           """lllyasviel/sd-controlnet-canny"""    ,  from_pt=__magic_name__    ,  dtype=jnp.bfloataa      )
       lowerCamelCase			,    lowerCamelCase		:	Any							=      FlaxStableDiffusionControlNetPipeline.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""    ,  controlnet=__magic_name__    ,  from_pt=__magic_name__    ,  dtype=jnp.bfloataa      )
       lowerCamelCase		:	List[Any]							=      controlnet_params

       lowerCamelCase		:	Tuple							=      """bird"""
       lowerCamelCase		:	int							=      jax.device_count()
       lowerCamelCase		:	Union[str, Any]							=      pipe.prepare_text_inputs([prompts] * num_samples      )

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

       lowerCamelCase		:	Tuple							=      jax.random.PRNGKey(0      )
       lowerCamelCase		:	Union[str, Any]							=      jax.random.split(__magic_name__    ,  jax.device_count()      )

       lowerCamelCase		:	Any							=      replicate(__magic_name__      )
       lowerCamelCase		:	int							=      shard(__magic_name__      )
       lowerCamelCase		:	Any							=      shard(__magic_name__      )

       lowerCamelCase		:	Union[str, Any]							=      pipe(
           prompt_ids=__magic_name__    ,  image=__magic_name__    ,  params=__magic_name__    ,  prng_seed=__magic_name__    ,  num_inference_steps=5_0    ,  jit=__magic_name__    ,  ).images
       assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

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

       lowerCamelCase		:	str							=      jnp.asarray(jax.device_get(image_slice.flatten()      )      )
       lowerCamelCase		:	int							=      jnp.array(
           [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]      )
       print(F'''output_slice: {output_slice}'''      )
       assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	str							=      FlaxControlNetModel.from_pretrained(
           """lllyasviel/sd-controlnet-openpose"""    ,  from_pt=__magic_name__    ,  dtype=jnp.bfloataa      )
       lowerCamelCase			,    lowerCamelCase		:	Tuple							=      FlaxStableDiffusionControlNetPipeline.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""    ,  controlnet=__magic_name__    ,  from_pt=__magic_name__    ,  dtype=jnp.bfloataa      )
       lowerCamelCase		:	Tuple							=      controlnet_params

       lowerCamelCase		:	List[Any]							=      """Chef in the kitchen"""
       lowerCamelCase		:	Any							=      jax.device_count()
       lowerCamelCase		:	Tuple							=      pipe.prepare_text_inputs([prompts] * num_samples      )

       lowerCamelCase		:	Tuple							=      load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""      )
       lowerCamelCase		:	Union[str, Any]							=      pipe.prepare_image_inputs([pose_image] * num_samples      )

       lowerCamelCase		:	Optional[int]							=      jax.random.PRNGKey(0      )
       lowerCamelCase		:	List[Any]							=      jax.random.split(__magic_name__    ,  jax.device_count()      )

       lowerCamelCase		:	Union[str, Any]							=      replicate(__magic_name__      )
       lowerCamelCase		:	int							=      shard(__magic_name__      )
       lowerCamelCase		:	Union[str, Any]							=      shard(__magic_name__      )

       lowerCamelCase		:	int							=      pipe(
           prompt_ids=__magic_name__    ,  image=__magic_name__    ,  params=__magic_name__    ,  prng_seed=__magic_name__    ,  num_inference_steps=5_0    ,  jit=__magic_name__    ,  ).images
       assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

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

       lowerCamelCase		:	Tuple							=      jnp.asarray(jax.device_get(image_slice.flatten()      )      )
       lowerCamelCase		:	List[Any]							=      jnp.array(
           [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]      )
       print(F'''output_slice: {output_slice}'''      )
       assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			field(default="""automatic-speech-recognition"""  ,   metadata={"""include_in_asdict_even_if_is_default""": True})
      _UpperCAmelCase				:    ClassVar[Features]           =			Features({"""audio""": Audio()})
      _UpperCAmelCase				:    ClassVar[Features]           =			Features({"""transcription""": Value("""string""")})
      _UpperCAmelCase				:    str           =			"audio"
      _UpperCAmelCase				:    str           =			"transcription"
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       if self.audio_column not in features:
        raise ValueError(F'''Column {self.audio_column} is not present in features.'''      )
       if not isinstance(features[self.audio_column]    ,  __magic_name__      ):
        raise ValueError(F'''Column {self.audio_column} is not an Audio type.'''      )
       lowerCamelCase		:	Optional[Any]							=      copy.deepcopy(self      )
       lowerCamelCase		:	List[Any]							=      self.input_schema.copy()
       lowerCamelCase		:	Tuple							=      features[self.audio_column]
       lowerCamelCase		:	int							=      input_schema
       return task_template






      @property
      def 						UpperCamelCase__							(      self      ):
       return {self.audio_column: "audio", self.transcription_column: "transcription"}




