




'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       A   (		_a					,_a					,unittest.TestCase   ):
							lowercase_          =    StableDiffusionXLImgaImgPipeline
							lowercase_          =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
							lowercase_          =    PipelineTesterMixin.required_optional_params - {'latents'}
							lowercase_          =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowercase_          =    IMAGE_TO_IMAGE_IMAGE_PARAMS
							lowercase_          =    IMAGE_TO_IMAGE_IMAGE_PARAMS







							def        __lowerCAmelCase      (  self	:  str )      ->							int:


														"""simple docstring"""


														torch.manual_seed(0 )
														_a     					=    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''')							,      attention_head_dim=(2, 4)							,      use_linear_projection=lowerCAmelCase_							,      addition_embed_type='''text_time'''							,      addition_time_embed_dim=8							,      transformer_layers_per_block=(1, 2)							,      projection_class_embeddings_input_dim=80							,      cross_attention_dim=64							,      )
														_a     					=    EulerDiscreteScheduler(
														    beta_start=0.0_0_0_8_5							,      beta_end=0.0_1_2							,      steps_offset=1							,      beta_schedule='''scaled_linear'''							,      timestep_spacing='''leading'''							,      )
														torch.manual_seed(0 )
														_a     					=    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							,      sample_size=1_28							,      )
														torch.manual_seed(0 )
														_a     					=    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=10_00							,      hidden_act='''gelu'''							,      projection_dim=32							,      )
														_a     					=    CLIPTextModel(lowerCAmelCase_ )
														_a     					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,      local_files_only=lowerCAmelCase_ )

														_a     					=    CLIPTextModelWithProjection(lowerCAmelCase_ )
														_a     					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,      local_files_only=lowerCAmelCase_ )

														_a     					=    {
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''text_encoder_2''': text_encoder_a,
														    '''tokenizer_2''': tokenizer_a,
														    # "safety_checker": None,
														    # "feature_extractor": None,
														}
														return components







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  Tuple=0 )      ->							str:


														"""simple docstring"""


														_a     					=    floats_tensor((1, 3, 32, 32)							,      rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
														_a     					=    image / 2 + 0.5
														if str(lowerCAmelCase_ ).startswith('''mps''' ):
																					_a     					=    torch.manual_seed(lowerCAmelCase_ )
														else:
																					_a     					=    torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
														_a     					=    {
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''image''': image,
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 5.0,
														    '''output_type''': '''numpy''',
														    '''strength''': 0.7_5,
														}
														return inputs







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Tuple:


														"""simple docstring"""


														_a     					=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
														_a     					=    self.get_dummy_components()
														_a     					=    StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_ )
														_a     					=    sd_pipe.to(lowerCAmelCase_ )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

														_a     					=    self.get_dummy_inputs(lowerCAmelCase_ )
														_a     					=    sd_pipe(**lowerCAmelCase_ ).images
														_a     					=    image[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)

														_a     					=    np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )

														assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Optional[int]:


														"""simple docstring"""


														super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )







							def        __lowerCAmelCase      (  self	:  str )      ->							Tuple:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							List[str]:


														"""simple docstring"""


														pass







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    self.get_dummy_components()
														_a     					=    StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_ )
														_a     					=    sd_pipe.to(lowerCAmelCase_ )
														_a     					=    sd_pipe.to(lowerCAmelCase_ )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

														# forward without prompt embeds
														_a     					=    self.get_dummy_inputs(lowerCAmelCase_ )
														_a     					=    3 * ['''this is a negative prompt''']
														_a     					=    negative_prompt
														_a     					=    3 * [inputs['''prompt''']]

														_a     					=    sd_pipe(**lowerCAmelCase_ )
														_a     					=    output.images[0, -3:, -3:, -1]

														# forward with prompt embeds
														_a     					=    self.get_dummy_inputs(lowerCAmelCase_ )
														_a     					=    3 * ['''this is a negative prompt''']
														_a     					=    3 * [inputs.pop('''prompt''' )]

														(
														    (
														    _a
														)					,							(
														    _a
														)					,							(
														    _a
														)					,							(
														    _a
														)					,							
														)     					=    sd_pipe.encode_prompt(lowerCAmelCase_							,      negative_prompt=lowerCAmelCase_ )

														_a     					=    sd_pipe(
														    **lowerCAmelCase_							,      prompt_embeds=lowerCAmelCase_							,      negative_prompt_embeds=lowerCAmelCase_							,      pooled_prompt_embeds=lowerCAmelCase_							,      negative_pooled_prompt_embeds=lowerCAmelCase_							,      )
														_a     					=    output.images[0, -3:, -3:, -1]

														# make sure that it's equal
														assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4



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







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							int:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  Tuple							,      lowerCAmelCase_	:  List[Any]="cpu"							,      lowerCAmelCase_	:  Any=torch.floataa							,      lowerCAmelCase_	:  Optional[Any]=0 )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
														_a     					=    np.random.RandomState(lowerCAmelCase_ ).standard_normal((1, 4, 64, 64) )
														_a     					=    torch.from_numpy(lowerCAmelCase_ ).to(device=lowerCAmelCase_							,      dtype=lowerCAmelCase_ )
														_a     					=    {
														    '''prompt''': '''a photograph of an astronaut riding a horse''',
														    '''latents''': latents,
														    '''generator''': generator,
														    '''num_inference_steps''': 3,
														    '''guidance_scale''': 7.5,
														    '''output_type''': '''numpy''',
														}
														return inputs







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
														pipe.to(lowerCAmelCase_ )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

														_a     					=    self.get_inputs(lowerCAmelCase_ )
														_a     					=    pipe(**lowerCAmelCase_ ).images
														_a     					=    image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														_a     					=    np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
														assert np.abs(image_slice - expected_slice ).max() < 7e-3







'''simple docstring'''


import argparse
from collections import defaultdict

import yaml


_snake_case				:				int       							=		'docs/source/en/_toctree.yml'
def 		snake_case_   (UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							_a     					=    defaultdict(UpperCamelCase				)
							for doc in model_doc:
														counts[doc["local"]] += 1
							_a     					=    [key for key, value in counts.items() if value > 1]

							_a     					=    []
							for duplicate_key in duplicates:
														_a     					=    list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}				)
														if len(UpperCamelCase				) > 1:
																					raise ValueError(
																					    f'{duplicate_key} is present several times in the documentation table of content at '
																					    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																					    '''others.'''				)
														# Only add this once
														new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}				)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]				)

							# Sort
							return sorted(UpperCamelCase			,						key=lambda UpperCamelCase				: s["title"].lower()				)
def 		snake_case_   (UpperCamelCase	:  str=False				):





							'''simple docstring'''


							with open(UpperCamelCase			,						encoding='''utf-8'''				) as f:
														_a     					=    yaml.safe_load(f.read()				)

							# Get to the API doc
							_a     					=    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_a     					=    content[api_idx]['''sections''']

							# Then to the model doc
							_a     					=    0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							_a     					=    api_doc[model_idx]['''sections''']

							_a     					=    [(idx, section) for idx, section in enumerate(UpperCamelCase				) if '''sections''' in section]
							_a     					=    False
							for idx, modality_doc in modalities_docs:
														_a     					=    modality_doc['''sections''']
														_a     					=    clean_model_doc_toc(UpperCamelCase				)

														if old_modality_doc != new_modality_doc:
																					_a     					=    True
																					if overwrite:
																												_a     					=    new_modality_doc

							if diff:
														if overwrite:
																					_a     					=    model_doc
																					_a     					=    api_doc
																					with open(UpperCamelCase			,						'''w'''			,						encoding='''utf-8'''				) as f:
																												f.write(yaml.dump(UpperCamelCase			,						allow_unicode=UpperCamelCase				)				)
														else:
																					raise ValueError(
																					    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''				)


if __name__ == "__main__":
				_snake_case				:				List[str]       							=		argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				_snake_case				:				List[Any]       							=		parser.parse_args()

				check_model_doc(args.fix_and_overwrite)

