





'''simple docstring'''

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowercase__						:      Dict	=    logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.'			)
@require_torch
@require_tf
@slow
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		Path	,			lowerCAmelCase__       :		Union[str, None] = None	,			lowerCAmelCase__       :		Union[List[str], None] = None	,			lowerCAmelCase__       :		Union[str, List[str], None] = None	,			lowerCAmelCase__       :		bool = True	,			)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	[file for file in os.listdir(lowerCAmelCase__					) if os.path.isfile(os.path.join(lowerCAmelCase__	,			lowerCAmelCase__					)					)]

            if identifier is not None:
                 _UpperCamelCase       =	[file for file in files if identifier in file]

            if n_identifier is not None:
                 if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					):
                      for n_ in n_identifier:
                           _UpperCamelCase       =	[file for file in files if n_ not in file]
                 else:
                      _UpperCamelCase       =	[file for file in files if n_identifier not in file]

            _UpperCamelCase       =	ignore_files or []
            ignore_files.append('''__init__.py'''					)
            _UpperCamelCase       =	[file for file in files if file not in ignore_files]

            for file in files:
                 # Open all files
                 print('''Testing'''	,			lowerCAmelCase__					)

                 if only_modules:
                      _UpperCamelCase       =	file.split('''.'''					)[0]
                      try:
                           _UpperCamelCase       =	getattr(lowerCAmelCase__	,			lowerCAmelCase__					)
                           _UpperCamelCase       =	doctest.DocTestSuite(lowerCAmelCase__					)
                           _UpperCamelCase       =	unittest.TextTestRunner().run(lowerCAmelCase__					)
                           self.assertIs(len(result.failures					)	,			0					)
                      except AttributeError:
                           logger.info(f"""{module_identifier} is not a module."""					)
                 else:
                      _UpperCamelCase       =	doctest.testfile(str('''..''' / directory / file					)	,			optionflags=doctest.ELLIPSIS					)
                      self.assertIs(result.failed	,			0					)





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

            '''simple docstring'''




            _UpperCamelCase       =	Path('''src/transformers'''					)
            _UpperCamelCase       =	'''modeling'''
            _UpperCamelCase       =	[
                '''modeling_ctrl.py''',
                '''modeling_tf_ctrl.py''',
            ]
            self.analyze_directory(lowerCAmelCase__	,			identifier=lowerCAmelCase__	,			ignore_files=lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	Path('''src/transformers'''					)
            _UpperCamelCase       =	'''tokenization'''
            self.analyze_directory(lowerCAmelCase__	,			identifier=lowerCAmelCase__					)





       def snake_case__						(							self       :		List[str]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	Path('''src/transformers'''					)
            _UpperCamelCase       =	'''configuration'''
            self.analyze_directory(lowerCAmelCase__	,			identifier=lowerCAmelCase__					)





       def snake_case__						(							self       :		List[Any]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	Path('''src/transformers'''					)
            _UpperCamelCase       =	['''configuration''', '''modeling''', '''tokenization''']
            self.analyze_directory(lowerCAmelCase__	,			n_identifier=lowerCAmelCase__					)







       def snake_case__						(							self       :		List[str]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	Path('''docs/source'''					)
            _UpperCamelCase       =	['''favicon.ico''']
            self.analyze_directory(lowerCAmelCase__	,			ignore_files=lowerCAmelCase__	,			only_modules=lowerCAmelCase__					)













'''simple docstring'''

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 snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




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





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

            '''simple docstring'''




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

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	pipe(
                prompt_ids=lowerCAmelCase__	,			image=lowerCAmelCase__	,			params=lowerCAmelCase__	,			prng_seed=lowerCAmelCase__	,			num_inference_steps=50	,			jit=lowerCAmelCase__	,			).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(
                [0.167969, 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 snake_case__						(							self       :		Union[str, Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




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

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	pipe(
                prompt_ids=lowerCAmelCase__	,			image=lowerCAmelCase__	,			params=lowerCAmelCase__	,			prng_seed=lowerCAmelCase__	,			num_inference_steps=50	,			jit=lowerCAmelCase__	,			).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(
                [[0.271484, 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



