

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def  __lowerCamelCase     (			UpperCamelCase__				):







       '''simple docstring'''






       return EnvironmentCommand()





def  __lowerCamelCase     (			UpperCamelCase__				):







       '''simple docstring'''






       return EnvironmentCommand(args.accelerate_config_file				)







class 			lowercase    (	lowercase_      ):




    @staticmethod
    def    a							( snake_case	):
           snake_case_          =   parser.add_parser('env'	)
           download_parser.set_defaults(func=_A	)
           download_parser.add_argument(
               '--accelerate-config_file'				,       default=_A				,       help='The accelerate config file to use for the default values in the launching script.'				,       )
           download_parser.set_defaults(func=_A	)




    def __init__( self				,       snake_case				,       *snake_case	):
           snake_case_          =   accelerate_config_file




    def    a							( self	):
           snake_case_          =   'not installed'
           if is_safetensors_available():
                  import safetensors

                  snake_case_          =   safetensors.__version__
           elif importlib.util.find_spec('safetensors'	) is not None:
                  import safetensors

                  snake_case_          =   F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

           snake_case_          =   'not installed'
           snake_case_          =   'not found'
           if is_accelerate_available():
                  import accelerate
                  from accelerate.commands.config import default_config_file, load_config_from_file

                  snake_case_          =   accelerate.__version__
                  # Get the default from the config file.
                  if self._accelerate_config_file is not None or os.path.isfile(_A	):
                         snake_case_          =   load_config_from_file(self._accelerate_config_file	).to_dict()

                  snake_case_          =   (
                      '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
                      if isinstance(_A				,       _A	)
                      else F'''\t{accelerate_config}'''
                  )

           snake_case_          =   'not installed'
           snake_case_          =   'NA'
           if is_torch_available():
                  import torch

                  snake_case_          =   torch.__version__
                  snake_case_          =   torch.cuda.is_available()

           snake_case_          =   'not installed'
           snake_case_          =   'NA'
           if is_tf_available():
                  import tensorflow as tf

                  snake_case_          =   tf.__version__
                  try:
                         # deprecated in v2.1
                         snake_case_          =   tf.test.is_gpu_available()
                  except AttributeError:
                         # returns list of devices, convert to bool
                         snake_case_          =   bool(tf.config.list_physical_devices('GPU'	)	)

           snake_case_          =   'not installed'
           snake_case_          =   'not installed'
           snake_case_          =   'not installed'
           snake_case_          =   'NA'
           if is_flax_available():
                  import flax
                  import jax
                  import jaxlib

                  snake_case_          =   flax.__version__
                  snake_case_          =   jax.__version__
                  snake_case_          =   jaxlib.__version__
                  snake_case_          =   jax.lib.xla_bridge.get_backend().platform

           snake_case_          =   {
               '`transformers` version': version,
               'Platform': platform.platform(),
               'Python version': platform.python_version(),
               'Huggingface_hub version': huggingface_hub.__version__,
               'Safetensors version': F'''{safetensors_version}''',
               'Accelerate version': F'''{accelerate_version}''',
               'Accelerate config': F'''{accelerate_config_str}''',
               'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
               'Tensorflow version (GPU?)': F'''{tf_version} ({tf_cuda_available})''',
               'Flax version (CPU?/GPU?/TPU?)': F'''{flax_version} ({jax_backend})''',
               'Jax version': F'''{jax_version}''',
               'JaxLib version': F'''{jaxlib_version}''',
               'Using GPU in script?': '<fill in>',
               'Using distributed or parallel set-up in script?': '<fill in>',
           }

           print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'	)
           print(self.format_dict(_A	)	)

           return info




    @staticmethod
    def    a							( snake_case	):
           return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]	) + "\n"







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 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-canny'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       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(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).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(
											    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




				def    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-openpose'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       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(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).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(
											    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




