





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 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


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




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							FlaxControlNetModel.from_pretrained(
                "lllyasviel/sd-controlnet-canny"    ,						from_pt=__lowerCamelCase    ,						dtype=jnp.bfloataa							)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
                "runwayml/stable-diffusion-v1-5"    ,						controlnet=__lowerCamelCase    ,						from_pt=__lowerCamelCase    ,						dtype=jnp.bfloataa							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							controlnet_params

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

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

            _SCREAMING_SNAKE_CASE       :	Dict						    =							jax.random.PRNGKey(0							)
            _SCREAMING_SNAKE_CASE       :	int						    =							jax.random.split(__lowerCamelCase    ,						jax.device_count()							)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							replicate(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							shard(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							shard(__lowerCamelCase							)

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

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

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							jnp.asarray(jax.device_get(image_slice.flatten()							)							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[Any]						    =							FlaxControlNetModel.from_pretrained(
                "lllyasviel/sd-controlnet-openpose"    ,						from_pt=__lowerCamelCase    ,						dtype=jnp.bfloataa							)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Tuple						    =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
                "runwayml/stable-diffusion-v1-5"    ,						controlnet=__lowerCamelCase    ,						from_pt=__lowerCamelCase    ,						dtype=jnp.bfloataa							)
            _SCREAMING_SNAKE_CASE       :	int						    =							controlnet_params

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							"Chef in the kitchen"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							jax.device_count()
            _SCREAMING_SNAKE_CASE       :	Any						    =							pipe.prepare_text_inputs([prompts] * num_samples							)

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

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							jax.random.PRNGKey(0							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							jax.random.split(__lowerCamelCase    ,						jax.device_count()							)

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							replicate(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							shard(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							shard(__lowerCamelCase							)

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

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

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							jnp.asarray(jax.device_get(image_slice.flatten()							)							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							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






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


UpperCamelCase__      =logging.get_logger(__name__)

UpperCamelCase__      ={
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    'speech_to_text_2'
       __snake_case 			=    ['past_key_values']
       __snake_case 			=    {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}




       def __init__(      self    ,						__lowerCamelCase=1_0_0_0_0    ,						__lowerCamelCase=6    ,						__lowerCamelCase=2_0_4_8    ,						__lowerCamelCase=4    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=True    ,						__lowerCamelCase="relu"    ,						__lowerCamelCase=2_5_6    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=2    ,						__lowerCamelCase=True    ,						__lowerCamelCase=1    ,						__lowerCamelCase=0    ,						__lowerCamelCase=2    ,						__lowerCamelCase=1_0_2_4    ,						**__lowerCamelCase    ,						)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							vocab_size
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							d_model
            _SCREAMING_SNAKE_CASE       :	Dict						    =							decoder_ffn_dim
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							decoder_layers
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							decoder_attention_heads
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							dropout
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							attention_dropout
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							activation_dropout
            _SCREAMING_SNAKE_CASE       :	Dict						    =							activation_function
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							init_std
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							decoder_layerdrop
            _SCREAMING_SNAKE_CASE       :	int						    =							use_cache
            _SCREAMING_SNAKE_CASE       :	int						    =							decoder_layers
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							scale_embedding  # scale factor will be sqrt(d_model) if True
            _SCREAMING_SNAKE_CASE       :	str						    =							max_target_positions

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