


'''simple docstring'''
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class    lowercase	:

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :int												= TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''							)

							torch.manual_seed(0							)
							UpperCamelCase__   :int												= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''							)

							torch.manual_seed(0							)
							UpperCamelCase__   :int												= UNetaDConditionModel(
							    sample_size=32    ,   layers_per_block=1    ,   block_out_channels=[32, 64]    ,   down_block_types=[
							        '''ResnetDownsampleBlock2D''',
							        '''SimpleCrossAttnDownBlock2D''',
							    ]    ,   mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''    ,   up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']    ,   in_channels=3    ,   out_channels=6    ,   cross_attention_dim=32    ,   encoder_hid_dim=32    ,   attention_head_dim=8    ,   addition_embed_type='''text'''    ,   addition_embed_type_num_heads=2    ,   cross_attention_norm='''group_norm'''    ,   resnet_time_scale_shift='''scale_shift'''    ,   act_fn='''gelu'''    ,   )
							unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

							torch.manual_seed(0							)
							UpperCamelCase__   :int												= DDPMScheduler(
							    num_train_timesteps=1000    ,   beta_schedule='''squaredcos_cap_v2'''    ,   beta_start=0.0001    ,   beta_end=0.02    ,   thresholding=UpperCamelCase_    ,   dynamic_thresholding_ratio=0.95    ,   sample_max_value=1.0    ,   prediction_type='''epsilon'''    ,   variance_type='''learned_range'''    ,   )

							torch.manual_seed(0							)
							UpperCamelCase__   :Optional[Any]												= IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :int												= TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''							)

							torch.manual_seed(0							)
							UpperCamelCase__   :List[str]												= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''							)

							torch.manual_seed(0							)
							UpperCamelCase__   :List[str]												= UNetaDConditionModel(
							    sample_size=32    ,   layers_per_block=[1, 2]    ,   block_out_channels=[32, 64]    ,   down_block_types=[
							        '''ResnetDownsampleBlock2D''',
							        '''SimpleCrossAttnDownBlock2D''',
							    ]    ,   mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''    ,   up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']    ,   in_channels=6    ,   out_channels=6    ,   cross_attention_dim=32    ,   encoder_hid_dim=32    ,   attention_head_dim=8    ,   addition_embed_type='''text'''    ,   addition_embed_type_num_heads=2    ,   cross_attention_norm='''group_norm'''    ,   resnet_time_scale_shift='''scale_shift'''    ,   act_fn='''gelu'''    ,   class_embed_type='''timestep'''    ,   mid_block_scale_factor=1.414    ,   time_embedding_act_fn='''gelu'''    ,   time_embedding_dim=32    ,   )
							unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

							torch.manual_seed(0							)
							UpperCamelCase__   :Optional[int]												= DDPMScheduler(
							    num_train_timesteps=1000    ,   beta_schedule='''squaredcos_cap_v2'''    ,   beta_start=0.0001    ,   beta_end=0.02    ,   thresholding=UpperCamelCase_    ,   dynamic_thresholding_ratio=0.95    ,   sample_max_value=1.0    ,   prediction_type='''epsilon'''    ,   variance_type='''learned_range'''    ,   )

							torch.manual_seed(0							)
							UpperCamelCase__   :Tuple												= DDPMScheduler(
							    num_train_timesteps=1000    ,   beta_schedule='''squaredcos_cap_v2'''    ,   beta_start=0.0001    ,   beta_end=0.02    ,   )

							torch.manual_seed(0							)
							UpperCamelCase__   :str												= IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.get_dummy_components()
							UpperCamelCase__   :List[Any]												= self.pipeline_class(**UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :int												= self.get_dummy_inputs(UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= inputs['''prompt''']
							UpperCamelCase__   :Tuple												= inputs['''generator''']
							UpperCamelCase__   :Optional[Any]												= inputs['''num_inference_steps''']
							UpperCamelCase__   :List[str]												= inputs['''output_type''']

							if "image" in inputs:
												UpperCamelCase__   :Optional[int]												= inputs['''image''']
							else:
												UpperCamelCase__   :Optional[Any]												= None

							if "mask_image" in inputs:
												UpperCamelCase__   :List[Any]												= inputs['''mask_image''']
							else:
												UpperCamelCase__   :Union[str, Any]												= None

							if "original_image" in inputs:
												UpperCamelCase__   :Optional[Any]												= inputs['''original_image''']
							else:
												UpperCamelCase__   :Optional[Any]												= None

							UpperCamelCase__					,				UpperCamelCase__   :Tuple												= pipe.encode_prompt(UpperCamelCase_							)

							# inputs with prompt converted to embeddings
							UpperCamelCase__   :List[str]												= {
							    '''prompt_embeds''': prompt_embeds,
							    '''negative_prompt_embeds''': negative_prompt_embeds,
							    '''generator''': generator,
							    '''num_inference_steps''': num_inference_steps,
							    '''output_type''': output_type,
							}

							if image is not None:
												UpperCamelCase__   :List[str]												= image

							if mask_image is not None:
												UpperCamelCase__   :List[Any]												= mask_image

							if original_image is not None:
												UpperCamelCase__   :Optional[Any]												= original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
												setattr(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)

							UpperCamelCase__   :List[Any]												= pipe(**UpperCamelCase_							)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(UpperCamelCase_							)
												UpperCamelCase__   :Dict												= self.pipeline_class.from_pretrained(UpperCamelCase_							)
												pipe_loaded.to(UpperCamelCase_							)
												pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_							)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

							for optional_component in pipe._optional_components:
												self.assertTrue(
												    getattr(UpperCamelCase_    ,   UpperCamelCase_							) is None    ,   F'''`{optional_component}` did not stay set to None after loading.'''    ,   )

							UpperCamelCase__   :List[Any]												= self.get_dummy_inputs(UpperCamelCase_							)

							UpperCamelCase__   :str												= inputs['''generator''']
							UpperCamelCase__   :Optional[int]												= inputs['''num_inference_steps''']
							UpperCamelCase__   :Any												= inputs['''output_type''']

							# inputs with prompt converted to embeddings
							UpperCamelCase__   :List[str]												= {
							    '''prompt_embeds''': prompt_embeds,
							    '''negative_prompt_embeds''': negative_prompt_embeds,
							    '''generator''': generator,
							    '''num_inference_steps''': num_inference_steps,
							    '''output_type''': output_type,
							}

							if image is not None:
												UpperCamelCase__   :Dict												= image

							if mask_image is not None:
												UpperCamelCase__   :int												= mask_image

							if original_image is not None:
												UpperCamelCase__   :Optional[int]												= original_image

							UpperCamelCase__   :str												= pipe_loaded(**UpperCamelCase_							)[0]

							UpperCamelCase__   :Optional[Any]												= np.abs(to_np(UpperCamelCase_							) - to_np(UpperCamelCase_							)							).max()
							self.assertLess(UpperCamelCase_    ,   1e-4							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= self.get_dummy_components()
							UpperCamelCase__   :List[str]												= self.pipeline_class(**UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :Optional[Any]												= self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__   :Optional[int]												= pipe(**UpperCamelCase_							)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(UpperCamelCase_							)
												UpperCamelCase__   :Tuple												= self.pipeline_class.from_pretrained(UpperCamelCase_							)
												pipe_loaded.to(UpperCamelCase_							)
												pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_							)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

							UpperCamelCase__   :Tuple												= self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__   :int												= pipe_loaded(**UpperCamelCase_							)[0]

							UpperCamelCase__   :Tuple												= np.abs(to_np(UpperCamelCase_							) - to_np(UpperCamelCase_							)							).max()
							self.assertLess(UpperCamelCase_    ,   1e-4							)



'''simple docstring'''
import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				ComputeEnvironment.AMAZON_SAGEMAKER
		_a     =				True
		_a     =				'ml.p3.2xlarge'
		_a     =				'accelerate_sagemaker_execution_role'
		_a     =				'hf-sm'
		_a     =				'us-east-1'
		_a     =				1
		_a     =				'accelerate-sagemaker-1'
		_a     =				'1.6'
		_a     =				'4.4'
		_a     =				'train.py'
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    'False',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    '--do_test',
		    'False',
		    '--do_predict',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]



class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args							)
							assert isinstance(converted_args['''model_name_or_path''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''do_train''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''epochs''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''learning_rate''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''max_steps''']    ,   UpperCamelCase_							)

							with pytest.raises(UpperCamelCase_							):
												_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args							)