



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        lowerCAmelCase__					:


				def __UpperCamelCase						(							self      :  int       )		->     Dict:
											torch.manual_seed(0       )
											A      						=    TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'       )

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

											torch.manual_seed(0       )
											A      						=    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       )
											A      						=    DDPMScheduler(
											    num_train_timesteps=1_000						, beta_schedule='squaredcos_cap_v2'						, beta_start=0.0_0_0_1						, beta_end=0.0_2						, thresholding=__UpperCamelCase						, dynamic_thresholding_ratio=0.9_5						, sample_max_value=1.0						, prediction_type='epsilon'						, variance_type='learned_range'						, )

											torch.manual_seed(0       )
											A      						=    IFWatermarker()

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


				def __UpperCamelCase						(							self      :  Any       )		->     List[Any]:
											torch.manual_seed(0       )
											A      						=    TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'       )

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

											torch.manual_seed(0       )
											A      						=    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=1.4_1_4						, time_embedding_act_fn='gelu'						, time_embedding_dim=32						, )
											unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

											torch.manual_seed(0       )
											A      						=    DDPMScheduler(
											    num_train_timesteps=1_000						, beta_schedule='squaredcos_cap_v2'						, beta_start=0.0_0_0_1						, beta_end=0.0_2						, thresholding=__UpperCamelCase						, dynamic_thresholding_ratio=0.9_5						, sample_max_value=1.0						, prediction_type='epsilon'						, variance_type='learned_range'						, )

											torch.manual_seed(0       )
											A      						=    DDPMScheduler(
											    num_train_timesteps=1_000						, beta_schedule='squaredcos_cap_v2'						, beta_start=0.0_0_0_1						, beta_end=0.0_2						, )

											torch.manual_seed(0       )
											A      						=    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 __UpperCamelCase						(							self      :  List[Any]       )		->     Dict:
											A      						=    self.get_dummy_components()
											A      						=    self.pipeline_class(**__UpperCamelCase       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )

											A      						=    inputs['prompt']
											A      						=    inputs['generator']
											A      						=    inputs['num_inference_steps']
											A      						=    inputs['output_type']

											if "image" in inputs:
																		A      						=    inputs['image']
											else:
																		A      						=    None

											if "mask_image" in inputs:
																		A      						=    inputs['mask_image']
											else:
																		A      						=    None

											if "original_image" in inputs:
																		A      						=    inputs['original_image']
											else:
																		A      						=    None

											A						,   A      						=    pipe.encode_prompt(__UpperCamelCase       )

											# inputs with prompt converted to embeddings
											A      						=    {
											    '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:
																		A      						=    image

											if mask_image is not None:
																		A      						=    mask_image

											if original_image is not None:
																		A      						=    original_image

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

											A      						=    pipe(**__UpperCamelCase       )[0]

											with tempfile.TemporaryDirectory() as tmpdir:
																		pipe.save_pretrained(__UpperCamelCase       )
																		A      						=    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.'''						, )

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )

											A      						=    inputs['generator']
											A      						=    inputs['num_inference_steps']
											A      						=    inputs['output_type']

											# inputs with prompt converted to embeddings
											A      						=    {
											    '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:
																		A      						=    image

											if mask_image is not None:
																		A      						=    mask_image

											if original_image is not None:
																		A      						=    original_image

											A      						=    pipe_loaded(**__UpperCamelCase       )[0]

											A      						=    np.abs(to_np(__UpperCamelCase       ) - to_np(__UpperCamelCase       )       ).max()
											self.assertLess(__UpperCamelCase						, 1e-4       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Dict:
											A      						=    self.get_dummy_components()
											A      						=    self.pipeline_class(**__UpperCamelCase       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )
											A      						=    pipe(**__UpperCamelCase       )[0]

											with tempfile.TemporaryDirectory() as tmpdir:
																		pipe.save_pretrained(__UpperCamelCase       )
																		A      						=    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

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )
											A      						=    pipe_loaded(**__UpperCamelCase       )[0]

											A      						=    np.abs(to_np(__UpperCamelCase       ) - to_np(__UpperCamelCase       )       ).max()
											self.assertLess(__UpperCamelCase						, 1e-4       )




import argparse
from collections import defaultdict






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	List[str]				)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    F'''{file}_{class_name}_{test_name}'''
							done_test[_id] += 1

							with open(lowerCAmelCase__			,	'r'				) as f:
														A      						=    f.readlines()

							A      						=    F'''class {class_name}('''
							A      						=    F'''{4 * " "}def {test_name}('''
							A      						=    F'''{8 * " "}{correct_line.split()[0]}'''
							A      						=    F'''{16 * " "}{correct_line.split()[0]}'''
							A      						=    False
							A      						=    False
							A      						=    False
							A      						=    False
							A      						=    0
							A      						=    0

							A      						=    []
							for line in lines:
														if line.startswith(lowerCAmelCase__				):
																					A      						=    True
														elif in_class and line.startswith(lowerCAmelCase__				):
																					A      						=    True
														elif in_class and in_func and (line.startswith(lowerCAmelCase__				) or line.startswith(lowerCAmelCase__				)):
																					A      						=    len(line.split(correct_line.split()[0]				)[0]				)
																					count += 1

																					if count == done_test[_id]:
																												A      						=    True

														if in_class and in_func and in_line:
																					if ")" not in line:
																												continue
																					else:
																												A      						=    True

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(F'''{spaces * " "}{correct_line}'''				)
																					A      						=    A      						=    A      						=    A      						=    False
														else:
																					new_lines.append(lowerCAmelCase__				)

							with open(lowerCAmelCase__			,	'w'				) as f:
														for line in new_lines:
																					f.write(lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Tuple=None				)       ->							Union[str, Any]:

							'''simple docstring'''






							if fail is not None:
														with open(lowerCAmelCase__			,	'r'				) as f:
																					A      						=    {l.strip() for l in f.readlines()}
							else:
														A      						=    None

							with open(lowerCAmelCase__			,	'r'				) as f:
														A      						=    f.readlines()

							A      						=    defaultdict(lowerCAmelCase__				)
							for line in correct_lines:
														A						,   A						,   A						,   A      						=    line.split(';'				)
														if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
																					overwrite_file(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)


if __name__ == "__main__":
				__snake_case     :List[Any]			       =argparse.ArgumentParser()
				parser.add_argument('--correct_filename', help='filename of tests with expected result')
				parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
				__snake_case     :Optional[int]			       =parser.parse_args()

				main(args.correct_filename, args.fail_filename)