
import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
SCREAMING_SNAKE_CASE   :		str					 =       "src/diffusers"
SCREAMING_SNAKE_CASE   :		Union[str, Any]					 =       "docs/source/en"
SCREAMING_SNAKE_CASE   :		Tuple					 =       "."



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				with open(__UpperCamelCase ,						'''r''' ,						encoding='''utf-8''' ,						newline='''\n'''     ) as f:
								A_       	=  f.readlines()
				# Find the start prompt.
				A_       	=  0
				while not lines[start_index].startswith(__UpperCamelCase     ):
								start_index += 1
				start_index += 1

				A_       	=  start_index
				while not lines[end_index].startswith(__UpperCamelCase     ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]     ) <= 1:
								start_index += 1
				while len(lines[end_index]     ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]     ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
SCREAMING_SNAKE_CASE   :		int					 =       "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
SCREAMING_SNAKE_CASE   :		List[str]					 =       re.compile(R"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE   :		Optional[int]					 =       re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the diffusers module imported is the one in the repo.
SCREAMING_SNAKE_CASE   :		int					 =       importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(TRANSFORMERS_PATH, "__init__.py"),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
SCREAMING_SNAKE_CASE   :		Any					 =       spec.loader.load_module()



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' ,						__UpperCamelCase     )
				return [m.group(0     ) for m in matches]



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  2 if text == '''✅''' or text == '''❌''' else len(__UpperCamelCase     )
				A_       	=  (width - text_length) // 2
				A_       	=  width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def        lowerCamelCase_  (   ):
				A_       	=  diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				A_       	=  {
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				A_       	=  {name: config.replace('''ConfigMixin''' ,						''''''     ) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				A_       	=  collections.defaultdict(__UpperCamelCase     )
				A_       	=  collections.defaultdict(__UpperCamelCase     )
				A_       	=  collections.defaultdict(__UpperCamelCase     )
				A_       	=  collections.defaultdict(__UpperCamelCase     )
				A_       	=  collections.defaultdict(__UpperCamelCase     )

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(__UpperCamelCase     ):
								A_       	=  None
								if attr_name.endswith('''Tokenizer'''     ):
												A_       	=  slow_tokenizers
												A_       	=  attr_name[:-9]
								elif attr_name.endswith('''TokenizerFast'''     ):
												A_       	=  fast_tokenizers
												A_       	=  attr_name[:-13]
								elif _re_tf_models.match(__UpperCamelCase     ) is not None:
												A_       	=  tf_models
												A_       	=  _re_tf_models.match(__UpperCamelCase     ).groups()[0]
								elif _re_flax_models.match(__UpperCamelCase     ) is not None:
												A_       	=  flax_models
												A_       	=  _re_flax_models.match(__UpperCamelCase     ).groups()[0]
								elif _re_pt_models.match(__UpperCamelCase     ) is not None:
												A_       	=  pt_models
												A_       	=  _re_pt_models.match(__UpperCamelCase     ).groups()[0]

								if lookup_dict is not None:
												while len(__UpperCamelCase     ) > 0:
																if attr_name in model_name_to_prefix.values():
																				A_       	=  True
																				break
																# Try again after removing the last word in the name
																A_       	=  ''''''.join(camel_case_split(__UpperCamelCase     )[:-1]     )

    # Let's build that table!
				A_       	=  list(model_name_to_config.keys()     )
				model_names.sort(key=str.lower     )
				A_       	=  ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				A_       	=  [len(__UpperCamelCase     ) + 2 for c in columns]
				A_       	=  max([len(__UpperCamelCase     ) for name in model_names]     ) + 2

				# Build the table per se
				A_       	=  '''|''' + '''|'''.join([_center_text(__UpperCamelCase ,						__UpperCamelCase     ) for c, w in zip(__UpperCamelCase ,						__UpperCamelCase     )]     ) + '''|\n'''
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]     ) + "|\n"

				A_       	=  {True: '''✅''', False: '''❌'''}
				for name in model_names:
								A_       	=  model_name_to_prefix[name]
								A_       	=  [
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(__UpperCamelCase ,						__UpperCamelCase     ) for l, w in zip(__UpperCamelCase ,						__UpperCamelCase     )]     ) + "|\n"
				return table



def        lowerCamelCase_  (   __UpperCamelCase=False     ):
				A_	,					A_	,					A_	,					A_       	=  _find_text_in_file(
				    filename=os.path.join(__UpperCamelCase ,						'''index.mdx'''     ) ,						start_prompt='''<!--This table is updated automatically from the auto modules''' ,						end_prompt='''<!-- End table-->''' ,						)
				A_       	=  get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(__UpperCamelCase ,						'''index.mdx'''     ) ,						'''w''' ,						encoding='''utf-8''' ,						newline='''\n'''     ) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:]     )
								else:
												raise ValueError(
												    '''The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'''     )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE   :		str					 =       argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						SCREAMING_SNAKE_CASE   :		int					 =       parser.parse_args()

						check_model_table(args.fix_and_overwrite)

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()






@skip_mps
class 					__lowercase		(					A							,       A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							List[str]     	=	StableDiffusionModelEditingPipeline
					__magic_name__						:							List[str]     	=	TEXT_TO_IMAGE_PARAMS
					__magic_name__						:							Any     	=	TEXT_TO_IMAGE_BATCH_PARAMS
					__magic_name__						:							Optional[Any]     	=	TEXT_TO_IMAGE_IMAGE_PARAMS
					__magic_name__						:							Tuple     	=	TEXT_TO_IMAGE_IMAGE_PARAMS


					def        lowerCAmelCase_    (	self						)		->							Optional[int]:





									'''simple docstring'''
									torch.manual_seed(0						)
									A_       	=  UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)       ,       layers_per_block=2       ,       sample_size=3_2       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,       cross_attention_dim=3_2       ,       )
									A_       	=  DDIMScheduler()
									torch.manual_seed(0						)
									A_       	=  AutoencoderKL(
									    block_out_channels=[3_2, 6_4]       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,       latent_channels=4       ,       )
									torch.manual_seed(0						)
									A_       	=  CLIPTextConfig(
									    bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=3_2       ,       intermediate_size=3_7       ,       layer_norm_eps=1E-05       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_0_0_0       ,       )
									A_       	=  CLIPTextModel(a__						)
									A_       	=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=0						)		->							List[Any]:





									'''simple docstring'''
									A_       	=  torch.manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''A field of roses''',
									    '''generator''': generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    '''height''': None,
									    '''width''': None,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  sd_pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

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


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  '''french fries'''
									A_       	=  sd_pipe(**a__       ,       negative_prompt=a__						)
									A_       	=  output.images
									A_       	=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

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


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       beta_schedule='''scaled_linear'''						)
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

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

									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

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


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  PNDMScheduler()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(a__						):
													A_       	=  sd_pipe(**a__						).images


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=5E-3						)


					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)






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


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





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


					def        lowerCAmelCase_    (	self       ,       a__=0						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  torch.manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''A field of roses''',
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs


					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  '''CompVis/stable-diffusion-v1-4'''
									A_       	=  StableDiffusionModelEditingPipeline.from_pretrained(a__       ,       safety_checker=a__						)
									pipe.to(a__						)
									pipe.set_progress_bar_config(disable=a__						)
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									A_       	=  np.array(
									    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

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

									# make sure image changes after editing
									pipe.edit_model('''A pack of roses'''       ,       '''A pack of blue roses'''						)

									A_       	=  pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									assert np.abs(expected_slice - image_slice						).max() > 1E-1


					def        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A_       	=  '''CompVis/stable-diffusion-v1-4'''
									A_       	=  DDIMScheduler.from_pretrained(a__       ,       subfolder='''scheduler'''						)
									A_       	=  StableDiffusionModelEditingPipeline.from_pretrained(
									    a__       ,       scheduler=a__       ,       safety_checker=a__						)
									A_       	=  pipe.to(a__						)
									pipe.set_progress_bar_config(disable=a__						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									A_       	=  self.get_inputs()
									A_       	=  pipe(**a__						)

									A_       	=  torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 1_0**9