





import argparse
import os
import re

import packaging.version


_lowerCamelCase			       =      'examples/'
_lowerCamelCase			       =      {
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
_lowerCamelCase			       =      {
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
_lowerCamelCase			       =      'README.md'




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       with open(lowercase_ ,    '''r''' ,    encoding='''utf-8''' ,    newline='''\n'''      ) as f:
              _lowerCamelCase							:     Any	=		f.read()
       _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		REPLACE_PATTERNS[pattern]
       _lowerCamelCase							:     List[Any]	=		replace.replace('''VERSION''' ,    lowercase_      )
       _lowerCamelCase							:     Tuple	=		re_pattern.sub(lowercase_ ,    lowercase_      )
       with open(lowercase_ ,    '''w''' ,    encoding='''utf-8''' ,    newline='''\n'''      ) as f:
              f.write(lowercase_      )




def    __UpperCAmelCase(      lowercase_      ):
       for folder, directories, fnames in os.walk(lowercase_      ):
              # Removing some of the folders with non-actively maintained examples from the walk
              if "research_projects" in directories:
                     directories.remove('''research_projects'''      )
              if "legacy" in directories:
                     directories.remove('''legacy'''      )
              for fname in fnames:
                     if fname.endswith('''.py'''      ):
                            update_version_in_file(os.path.join(lowercase_ ,    lowercase_      ) ,    lowercase_ ,    pattern='''examples'''      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_=False      ):
       for pattern, fname in REPLACE_FILES.items():
              update_version_in_file(lowercase_ ,    lowercase_ ,    lowercase_      )
       if not patch:
              update_version_in_examples(lowercase_      )




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Any	=		'''🤗 Transformers currently provides the following architectures'''
       _lowerCamelCase							:     List[str]	=		'''1. Want to contribute a new model?'''
       with open(lowercase_ ,    '''r''' ,    encoding='''utf-8''' ,    newline='''\n'''      ) as f:
              _lowerCamelCase							:     Optional[Any]	=		f.readlines()

       # Find the start of the list.
       _lowerCamelCase							:     Union[str, Any]	=		0
       while not lines[start_index].startswith(_start_prompt      ):
              start_index += 1
       start_index += 1

       _lowerCamelCase							:     List[Any]	=		start_index
       # Update the lines in the model list.
       while not lines[index].startswith(_end_prompt      ):
              if lines[index].startswith('''1.'''      ):
                     _lowerCamelCase							:     Dict	=		lines[index].replace(
                         '''https://huggingface.co/docs/transformers/main/model_doc''' ,    '''https://huggingface.co/docs/transformers/model_doc''' ,    )
              index += 1

       with open(lowercase_ ,    '''w''' ,    encoding='''utf-8''' ,    newline='''\n'''      ) as f:
              f.writelines(lowercase_      )




def    __UpperCAmelCase(      ):
       with open(REPLACE_FILES['''init'''] ,    '''r'''      ) as f:
              _lowerCamelCase							:     List[Any]	=		f.read()
       _lowerCamelCase							:     Union[str, Any]	=		REPLACE_PATTERNS['''init'''][0].search(lowercase_      ).groups()[0]
       return packaging.version.parse(lowercase_      )




def    __UpperCAmelCase(      lowercase_=False      ):
       _lowerCamelCase							:     Union[str, Any]	=		get_version()
       if patch and default_version.is_devrelease:
              raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''      )
       if default_version.is_devrelease:
              _lowerCamelCase							:     List[Any]	=		default_version.base_version
       elif patch:
              _lowerCamelCase							:     Tuple	=		F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
       else:
              _lowerCamelCase							:     Optional[Any]	=		F"""{default_version.major}.{default_version.minor + 1}.0"""

       # Now let's ask nicely if that's the right one.
       _lowerCamelCase							:     Optional[Any]	=		input(F"""Which version are you releasing? [{default_version}]"""      )
       if len(lowercase_      ) == 0:
              _lowerCamelCase							:     str	=		default_version

       print(F"""Updating version to {version}."""      )
       global_version_update(lowercase_ ,    patch=lowercase_      )
       if not patch:
              print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''      )
              clean_main_ref_in_model_list()




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Dict	=		get_version()
       _lowerCamelCase							:     int	=		F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
       _lowerCamelCase							:     str	=		current_version.base_version

       # Check with the user we got that right.
       _lowerCamelCase							:     Optional[Any]	=		input(F"""Which version are we developing now? [{dev_version}]"""      )
       if len(lowercase_      ) == 0:
              _lowerCamelCase							:     Union[str, Any]	=		dev_version

       print(F"""Updating version to {version}."""      )
       global_version_update(lowercase_      )
       print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''      )
       clean_main_ref_in_model_list()


if __name__ == "__main__":
       _lowerCamelCase			       =      argparse.ArgumentParser()
       parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
       parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
       _lowerCamelCase			       =      parser.parse_args()
       if not args.post_release:
              pre_release_work(patch=args.patch)
       elif args.patch:
              print('Nothing to do after a patch :-)')
       else:
              post_release_work()








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  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-canny'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Union[str, Any]	=		controlnet_params

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

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

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

             _lowerCamelCase							:     Dict	=		replicate(a__)
             _lowerCamelCase							:     Tuple	=		shard(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)

             _lowerCamelCase							:     Optional[Any]	=		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)

             _lowerCamelCase							:     Optional[int]	=		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
             _lowerCamelCase							:     List[Any]	=		images[0, 253:256, 253:256, -1]

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



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Any	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-openpose'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase,  _lowerCamelCase							:     Any	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Optional[Any]	=		controlnet_params

             _lowerCamelCase							:     Optional[Any]	=		'''Chef in the kitchen'''
             _lowerCamelCase							:     int	=		jax.device_count()
             _lowerCamelCase							:     Dict	=		pipe.prepare_text_inputs([prompts] * num_samples)

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

             _lowerCamelCase							:     Optional[Any]	=		jax.random.PRNGKey(0)
             _lowerCamelCase							:     List[str]	=		jax.random.split(a__      ,							jax.device_count())

             _lowerCamelCase							:     str	=		replicate(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)
             _lowerCamelCase							:     List[Any]	=		shard(a__)

             _lowerCamelCase							:     Any	=		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)

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

             _lowerCamelCase							:     Optional[int]	=		jnp.asarray(jax.device_get(image_slice.flatten()))
             _lowerCamelCase							:     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

