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_  (		lowercase:				str		)     ->					Optional[Any]:







 '''simple docstring'''
 return EnvironmentCommand()




def    lowerCAmelCase_  (		lowercase:				List[Any]		)     ->					Tuple:







 '''simple docstring'''
 return EnvironmentCommand(args.accelerate_config_file		)





class 			__magic_name__	(		__a     ):





   """simple docstring"""




   @staticmethod
   def 	lowerCAmelCase    (						_lowercase       :	ArgumentParser				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			parser.add_parser('''env'''				)
    download_parser.set_defaults(func=_lowercase				)
    download_parser.add_argument(
        '''--accelerate-config_file'''		,	default=_lowercase		,	help='''The accelerate config file to use for the default values in the launching script.'''		,	)
    download_parser.set_defaults(func=_lowercase				)



   def __init__(						self       :	Union[str, Any]		,	_lowercase       :	List[Any]		,	*_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			accelerate_config_file



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			'''not installed'''
    if is_safetensors_available():
     import safetensors

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

     _UpperCamelCase:  Optional[Any]    						=			f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

    _UpperCamelCase:  List[str]    						=			'''not installed'''
    _UpperCamelCase:  Optional[int]    						=			'''not found'''
    if is_accelerate_available():
     import accelerate
     from accelerate.commands.config import default_config_file, load_config_from_file

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

     _UpperCamelCase:  Any    						=			(
         '''\n'''.join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]				)
         if isinstance(_lowercase		,	_lowercase				)
         else f"""\t{accelerate_config}"""
     )

    _UpperCamelCase:  int    						=			'''not installed'''
    _UpperCamelCase:  Dict    						=			'''NA'''
    if is_torch_available():
     import torch

     _UpperCamelCase:  List[Any]    						=			torch.__version__
     _UpperCamelCase:  List[Any]    						=			torch.cuda.is_available()

    _UpperCamelCase:  str    						=			'''not installed'''
    _UpperCamelCase:  Optional[int]    						=			'''NA'''
    if is_tf_available():
     import tensorflow as tf

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

    _UpperCamelCase:  Optional[int]    						=			'''not installed'''
    _UpperCamelCase:  str    						=			'''not installed'''
    _UpperCamelCase:  Tuple    						=			'''not installed'''
    _UpperCamelCase:  Tuple    						=			'''NA'''
    if is_flax_available():
     import flax
     import jax
     import jaxlib

     _UpperCamelCase:  Optional[Any]    						=			flax.__version__
     _UpperCamelCase:  Union[str, Any]    						=			jax.__version__
     _UpperCamelCase:  Optional[Any]    						=			jaxlib.__version__
     _UpperCamelCase:  Optional[int]    						=			jax.lib.xla_bridge.get_backend().platform

    _UpperCamelCase:  Union[str, Any]    						=			{
        '''`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(_lowercase				)				)

    return info



   @staticmethod
   def 	lowerCAmelCase    (						_lowercase       :	List[str]				):



    """simple docstring"""



    return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]				) + "\n"
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     List[str]	      = StableDiffusionSAGPipeline
   lowerCAmelCase   :     List[Any]	      = TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase   :     List[str]	      = TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase   :     str	      = TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase   :     int	      = TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase   :     int	      = False



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			UNetaDConditionModel(
        block_out_channels=(32, 64)		,	layers_per_block=2		,	sample_size=32		,	in_channels=4		,	out_channels=4		,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,	cross_attention_dim=32		,	)
    _UpperCamelCase:  List[Any]    						=			DDIMScheduler(
        beta_start=0.00085		,	beta_end=0.012		,	beta_schedule='''scaled_linear'''		,	clip_sample=_lowercase		,	set_alpha_to_one=_lowercase		,	)
    torch.manual_seed(0				)
    _UpperCamelCase:  Optional[int]    						=			AutoencoderKL(
        block_out_channels=[32, 64]		,	in_channels=3		,	out_channels=3		,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,	latent_channels=4		,	)
    torch.manual_seed(0				)
    _UpperCamelCase:  int    						=			CLIPTextConfig(
        bos_token_id=0		,	eos_token_id=2		,	hidden_size=32		,	intermediate_size=37		,	layer_norm_eps=1E-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=1_000		,	)
    _UpperCamelCase:  Any    						=			CLIPTextModel(_lowercase				)
    _UpperCamelCase:  List[str]    						=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

    _UpperCamelCase:  int    						=			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	List[Any]		,	_lowercase       :	int=0				):



    """simple docstring"""



    if str(_lowercase				).startswith('''mps'''				):
     _UpperCamelCase:  Tuple    						=			torch.manual_seed(_lowercase				)
    else:
     _UpperCamelCase:  List[str]    						=			torch.Generator(device=_lowercase				).manual_seed(_lowercase				)
    _UpperCamelCase:  Dict    						=			{
        '''prompt''': '''.''',
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 1.0,
        '''sag_scale''': 1.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




@slow
@require_torch_gpu
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
    _UpperCamelCase:  Any    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Tuple    						=			'''.'''
    _UpperCamelCase:  Tuple    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Union[str, Any]    						=			sag_pipe(
        [prompt]		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Optional[Any]    						=			output.images

    _UpperCamelCase:  Tuple    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  Any    						=			np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  int    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  List[Any]    						=			'''.'''
    _UpperCamelCase:  str    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			sag_pipe(
        [prompt]		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Union[str, Any]    						=			output.images

    _UpperCamelCase:  List[str]    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  int    						=			np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  int    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Any    						=			'''.'''
    _UpperCamelCase:  List[Any]    						=			torch.manual_seed(0				)
    _UpperCamelCase:  str    						=			sag_pipe(
        [prompt]		,	width=768		,	height=512		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''		,	)

    _UpperCamelCase:  str    						=			output.images

    assert image.shape == (1, 512, 768, 3)