


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 ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						int:




											'''simple docstring'''

											super().tearDown()
											gc.collect()


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		FlaxControlNetModel.from_pretrained(
											    "lllyasviel/sd-controlnet-canny"  ,     from_pt=__lowerCamelCase  ,     dtype=jnp.bfloataa    )
											__snake_case		,       __snake_case				=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    "runwayml/stable-diffusion-v1-5"  ,     controlnet=__lowerCamelCase  ,     from_pt=__lowerCamelCase  ,     dtype=jnp.bfloataa    )
											__snake_case				=		controlnet_params

											__snake_case				=		"bird"
											__snake_case				=		jax.device_count()
											__snake_case				=		pipe.prepare_text_inputs([prompts] * num_samples    )

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

											__snake_case				=		jax.random.PRNGKey(0    )
											__snake_case				=		jax.random.split(__lowerCamelCase  ,     jax.device_count()    )

											__snake_case				=		replicate(__lowerCamelCase    )
											__snake_case				=		shard(__lowerCamelCase    )
											__snake_case				=		shard(__lowerCamelCase    )

											__snake_case				=		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)

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

											__snake_case				=		jnp.asarray(jax.device_get(image_slice.flatten()    )    )
											__snake_case				=		jnp.array(
											    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]    )
											print(f'''output_slice: {output_slice}'''    )
											assert jnp.abs(output_slice - expected_slice    ).max() < 1e-2






					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Dict:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		FlaxControlNetModel.from_pretrained(
											    "lllyasviel/sd-controlnet-openpose"  ,     from_pt=__lowerCamelCase  ,     dtype=jnp.bfloataa    )
											__snake_case		,       __snake_case				=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    "runwayml/stable-diffusion-v1-5"  ,     controlnet=__lowerCamelCase  ,     from_pt=__lowerCamelCase  ,     dtype=jnp.bfloataa    )
											__snake_case				=		controlnet_params

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

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

											__snake_case				=		jax.random.PRNGKey(0    )
											__snake_case				=		jax.random.split(__lowerCamelCase  ,     jax.device_count()    )

											__snake_case				=		replicate(__lowerCamelCase    )
											__snake_case				=		shard(__lowerCamelCase    )
											__snake_case				=		shard(__lowerCamelCase    )

											__snake_case				=		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)

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

											__snake_case				=		jnp.asarray(jax.device_get(image_slice.flatten()    )    )
											__snake_case				=		jnp.array(
											    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]    )
											print(f'''output_slice: {output_slice}'''    )
											assert jnp.abs(output_slice - expected_slice    ).max() < 1e-2












import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      ComputeEnvironment.AMAZON_SAGEMAKER
 __SCREAMING_SNAKE_CASE							=      True
 __SCREAMING_SNAKE_CASE							=      '''ml.p3.2xlarge'''
 __SCREAMING_SNAKE_CASE							=      '''accelerate_sagemaker_execution_role'''
 __SCREAMING_SNAKE_CASE							=      '''hf-sm'''
 __SCREAMING_SNAKE_CASE							=      '''us-east-1'''
 __SCREAMING_SNAKE_CASE							=      1
 __SCREAMING_SNAKE_CASE							=      '''accelerate-sagemaker-1'''
 __SCREAMING_SNAKE_CASE							=      '''1.6'''
 __SCREAMING_SNAKE_CASE							=      '''4.4'''
 __SCREAMING_SNAKE_CASE							=      '''train.py'''
 __SCREAMING_SNAKE_CASE							=      [
     '''--model_name_or_path''',
     '''bert''',
     '''--do_train''',
     '''False''',
     '''--epochs''',
     '''3''',
     '''--learning_rate''',
     '''5e-5''',
     '''--max_steps''',
     '''50.5''',
 ]
 __SCREAMING_SNAKE_CASE							=      [
     '''--model_name_or_path''',
     '''bert''',
     '''--do_train''',
     '''--do_test''',
     '''False''',
     '''--do_predict''',
     '''--epochs''',
     '''3''',
     '''--learning_rate''',
     '''5e-5''',
     '''--max_steps''',
     '''50.5''',
 ]




class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        # If no defaults are changed, `to_kwargs` returns an empty dict.
        A__		   =		_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
        assert isinstance(converted_args['''model_name_or_path'''],__lowerCamelCase					)
        assert isinstance(converted_args['''do_train'''],__lowerCamelCase					)
        assert isinstance(converted_args['''epochs'''],__lowerCamelCase					)
        assert isinstance(converted_args['''learning_rate'''],__lowerCamelCase					)
        assert isinstance(converted_args['''max_steps'''],__lowerCamelCase					)

        with pytest.raises(__lowerCamelCase					):
               _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args					)





