





import re
from pathlib import Path
from unittest import TestCase

import pytest

@pytest.mark.integration
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def        a			(self	:				Dict    ,      a__	:				str				):





         """simple docstring"""



         with open(a__    ,      encoding='''utf-8'''				) as input_file:
            __snake_case								=       re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''				)
            __snake_case								=       input_file.read()
            __snake_case								=       regexp.search(a__				)

         return match


      def        a			(self	:				Union[str, Any]    ,      a__	:				str				):





         """simple docstring"""



         with open(a__    ,      encoding='''utf-8'''				) as input_file:
            __snake_case								=       re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''    ,      re.DOTALL				)
            __snake_case								=       input_file.read()
            # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
            __snake_case								=       regexp.finditer(a__				)

         __snake_case								=       [match for match in matches if match is not None and match.group(1				) is not None]
         return matches[0] if matches else None


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       Path('''./datasets'''				)
         __snake_case								=       list(dataset_paths.absolute().glob('''**/*.py'''				)				)

         for dataset in dataset_files:
            if self._no_encoding_on_file_open(str(a__				)				):
               raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       Path('''./datasets'''				)
         __snake_case								=       list(dataset_paths.absolute().glob('''**/*.py'''				)				)

         for dataset in dataset_files:
            if self._no_print_statements(str(a__				)				):
               raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""				)








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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				int				):





         """simple docstring"""



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


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-canny'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      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(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       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)

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

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-openpose'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      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(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       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)

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

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2

