






import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp





@slow
@require_flax
class       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					return f'''gaussian_noise_s={seed}_shape={'_'.join([str(__a						) for s in shape]						)}.npy'''


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a=0  ,					__a=(4, 4, 64, 64)  ,					__a=False						):


					"""simple docstring"""

					A__							 =       jnp.bfloataa if fpaa else jnp.floataa
					A__							 =       jnp.array(load_hf_numpy(self.get_file_format(__a  ,					__a						)						)  ,					dtype=__a						)
					return image


	def       _UpperCAmelCase						(      self  ,					__a=False  ,					__a="CompVis/stable-diffusion-v1-4"						):


					"""simple docstring"""

					A__							 =       jnp.bfloataa if fpaa else jnp.floataa
					A__							 =       'bf16' if fpaa else None

					A__ ,			A__							 =       FlaxUNetaDConditionModel.from_pretrained(
					    __a  ,					subfolder='unet'  ,					dtype=__a  ,					revision=__a						)
					return model, params


	def       _UpperCAmelCase						(      self  ,					__a=0  ,					__a=(4, 77, 768)  ,					__a=False						):


					"""simple docstring"""

					A__							 =       jnp.bfloataa if fpaa else jnp.floataa
					A__							 =       jnp.array(load_hf_numpy(self.get_file_format(__a  ,					__a						)						)  ,					dtype=__a						)
					return hidden_states


	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
	        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
	        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
	        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
	        # fmt: on
	    ]						)
	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

					A__ ,			A__							 =       self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'  ,					fpaa=__a						)
					A__							 =       self.get_latents(__a  ,					fpaa=__a						)
					A__							 =       self.get_encoder_hidden_states(__a  ,					fpaa=__a						)

					A__							 =       model.apply(
					    {'params': params}  ,					__a  ,					jnp.array(__a  ,					dtype=jnp.intaa						)  ,					encoder_hidden_states=__a  ,					).sample

					assert sample.shape == latents.shape

					A__							 =       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)  ,					dtype=jnp.floataa						)
					A__							 =       jnp.array(__a  ,					dtype=jnp.floataa						)

					# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
					assert jnp.allclose(__a  ,					__a  ,					atol=1E-2						)






	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
	        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
	        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
	        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
	        # fmt: on
	    ]						)
	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

					A__ ,			A__							 =       self.get_unet_model(model_id='stabilityai/stable-diffusion-2'  ,					fpaa=__a						)
					A__							 =       self.get_latents(__a  ,					shape=(4, 4, 96, 96)  ,					fpaa=__a						)
					A__							 =       self.get_encoder_hidden_states(__a  ,					shape=(4, 77, 1024)  ,					fpaa=__a						)

					A__							 =       model.apply(
					    {'params': params}  ,					__a  ,					jnp.array(__a  ,					dtype=jnp.intaa						)  ,					encoder_hidden_states=__a  ,					).sample

					assert sample.shape == latents.shape

					A__							 =       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)  ,					dtype=jnp.floataa						)
					A__							 =       jnp.array(__a  ,					dtype=jnp.floataa						)

					# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
					assert jnp.allclose(__a  ,					__a  ,					atol=1E-2						)










"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE						:						Dict					   =       '''Create a default config file for Accelerate with only a few flags set.'''



def  __lowerCamelCase					(					lowerCAmelCase__="no"       ,lowerCAmelCase__ = default_json_config_file       ,lowerCAmelCase__ = False				):
				A__							 =       Path(lowerCAmelCase__				)
				path.parent.mkdir(parents=lowerCAmelCase__       ,exist_ok=lowerCAmelCase__				)
				if path.exists():
								print(
								    f'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
								return False
				A__							 =       mixed_precision.lower()
				if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
								raise ValueError(
								    f'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
				A__							 =       {
				    'compute_environment': 'LOCAL_MACHINE',
				    'mixed_precision': mixed_precision,
				}
				if torch.cuda.is_available():
								A__							 =       torch.cuda.device_count()
								A__							 =       num_gpus
								A__							 =       False
								if num_gpus > 1:
												A__							 =       'MULTI_GPU'
								else:
												A__							 =       'NO'
				elif is_xpu_available() and use_xpu:
								A__							 =       torch.xpu.device_count()
								A__							 =       num_xpus
								A__							 =       False
								if num_xpus > 1:
												A__							 =       'MULTI_XPU'
								else:
												A__							 =       'NO'
				elif is_npu_available():
								A__							 =       torch.npu.device_count()
								A__							 =       num_npus
								A__							 =       False
								if num_npus > 1:
												A__							 =       'MULTI_NPU'
								else:
												A__							 =       'NO'
				else:
								A__							 =       0
								A__							 =       True
								A__							 =       1
								A__							 =       'NO'
				A__							 =       ClusterConfig(**lowerCAmelCase__				)
				config.to_json_file(lowerCAmelCase__				)
				return path



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       parser.add_parser('default'       ,parents=lowerCAmelCase__       ,help=lowerCAmelCase__       ,formatter_class=lowerCAmelCase__				)
				parser.add_argument(
				    '--config_file'       ,default=lowerCAmelCase__       ,help=(
				        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
				        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
				        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
				        'with \'huggingface\'.'
				    )       ,dest='save_location'       ,)

				parser.add_argument(
				    '--mixed_precision'       ,choices=['no', 'fp16', 'bf16']       ,type=lowerCAmelCase__       ,help='Whether or not to use mixed precision training. '
				    'Choose between FP16 and BF16 (bfloat16) training. '
				    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'       ,default='no'       ,)
				parser.set_defaults(func=lowerCAmelCase__				)
				return parser







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       write_basic_config(args.mixed_precision       ,args.save_location				)
				if config_file:
								print(f'''accelerate configuration saved at {config_file}'''				)


