



import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


A_		:  str										=  10_00



class 		_lowercase		:

   def __init__(   self      :							Tuple				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Optional[int]=1.2				, __lowerCAmelCase      :							Dict="g"				, __lowerCAmelCase      :							List[Any]=0.5				, __lowerCAmelCase      :							int="-"				, __lowerCAmelCase      :							Optional[Any]="test_out.jpg"				, __lowerCAmelCase      :							Optional[int]=True				, __lowerCAmelCase      :							List[Any]=False				, __lowerCAmelCase      :							Optional[int]=None				, __lowerCAmelCase      :							Optional[Any]=None				, __lowerCAmelCase      :							str=0.7				, )							->					Dict:


    """simple docstring"""






    if isinstance(__lowerCAmelCase				, torch.Tensor				):
     a							=		img.numpy().astype("np.uint8"				)
    if isinstance(__lowerCAmelCase				, __lowerCAmelCase				):
     a							=		img_tensorize(__lowerCAmelCase				)
    assert isinstance(__lowerCAmelCase				, np.ndarray				)

    a		,	a							=		img.shape[1], img.shape[0]
    a							=		mplfigure.Figure(frameon=__lowerCAmelCase				)
    a							=		fig.get_dpi()
    a							=		(width * scale + 1E-2) / dpi
    a							=		(height * scale + 1E-2) / dpi
    fig.set_size_inches(__lowerCAmelCase				, __lowerCAmelCase				)
    a							=		fig.add_axes([0.0, 0.0, 1.0, 1.0]				)
    ax.axis("off"				)
    ax.set_xlim(0.0				, __lowerCAmelCase				)
    ax.set_ylim(__lowerCAmelCase				)

    a							=		saveas
    a							=		rgb
    a							=		pynb
    a							=		img
    a							=		edgecolor
    a							=		0.5
    a							=		linestyle
    a							=		int(np.sqrt(min(__lowerCAmelCase				, __lowerCAmelCase				)				) * scale // 3				)
    a							=		width
    a							=		height
    a							=		scale
    a							=		fig
    a							=		ax
    a							=		pad
    a							=		idaobj
    a							=		idaattr
    a							=		FigureCanvasAgg(__lowerCAmelCase				)

   def 		A				(   self      :							str				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							List[str]=None				)							->					Tuple:


    """simple docstring"""






    if color is None:
     a							=		self.edgecolor
    ((a)		,	(a)		,	(a)		,	(a))							=		box
    a							=		xa - xa
    a							=		ya - ya
    self.ax.add_patch(
        mpl.patches.Rectangle(
            (xa, ya)				, __lowerCAmelCase				, __lowerCAmelCase				, fill=__lowerCAmelCase				, edgecolor=__lowerCAmelCase				, linewidth=self.font_size // 3				, alpha=self.alpha				, linestyle=self.linestyle				, )				)

   def 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							str=None				, __lowerCAmelCase      :							List[Any]=None				, __lowerCAmelCase      :							Any=None				, __lowerCAmelCase      :							List[Any]=None				)							->					Dict:


    """simple docstring"""






    if len(boxes.shape				) > 2:
     a							=		boxes[0]
    if len(obj_ids.shape				) > 1:
     a							=		obj_ids[0]
    if len(obj_scores.shape				) > 1:
     a							=		obj_scores[0]
    if len(attr_ids.shape				) > 1:
     a							=		attr_ids[0]
    if len(attr_scores.shape				) > 1:
     a							=		attr_scores[0]
    if isinstance(__lowerCAmelCase				, torch.Tensor				):
     a							=		boxes.numpy()
    if isinstance(__lowerCAmelCase				, __lowerCAmelCase				):
     a							=		np.array(__lowerCAmelCase				)
    assert isinstance(__lowerCAmelCase				, np.ndarray				)
    a							=		np.prod(boxes[:, 2:] - boxes[:, :2]				, axis=1				)
    a							=		np.argsort(-areas				).tolist()
    a							=		boxes[sorted_idxs] if boxes is not None else None
    a							=		obj_ids[sorted_idxs] if obj_ids is not None else None
    a							=		obj_scores[sorted_idxs] if obj_scores is not None else None
    a							=		attr_ids[sorted_idxs] if attr_ids is not None else None
    a							=		attr_scores[sorted_idxs] if attr_scores is not None else None

    a							=		[self._random_color(maximum=1				) for _ in range(len(__lowerCAmelCase				)				)]
    a							=		[assigned_colors[idx] for idx in sorted_idxs]
    if obj_ids is not None:
     a							=		self._create_text_labels_attr(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)
     for i in range(len(__lowerCAmelCase				)				):
      a							=		assigned_colors[i]
      self.add_box(boxes[i]				, __lowerCAmelCase				)
      self.draw_labels(labels[i]				, boxes[i]				, __lowerCAmelCase				)

   def 		A				(   self      :							List[str]				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Optional[int]				)							->					Optional[int]:


    """simple docstring"""






    a		,	a		,	a		,	a							=		box
    a							=		(xa, ya)
    a							=		(ya - ya) * (xa - xa)
    a							=		_SMALL_OBJ * self.scale
    if instance_area < small or ya - ya < 40 * self.scale:
     if ya >= self.height - 5:
      a							=		(xa, ya)
     else:
      a							=		(xa, ya)

    a							=		(ya - ya) / np.sqrt(self.height * self.width				)
    a							=		self._change_color_brightness(__lowerCAmelCase				, brightness_factor=0.7				)
    a							=		np.clip((height_ratio - 0.0_2) / 0.0_8 + 1				, 1.2				, 2				)
    font_size *= 0.7_5 * self.font_size

    self.draw_text(
        text=__lowerCAmelCase				, position=__lowerCAmelCase				, color=__lowerCAmelCase				, )

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Union[str, Any]="g"				, __lowerCAmelCase      :							List[Any]="left"				, )							->					Any:


    """simple docstring"""






    a							=		0
    a							=		self.font_size
    a							=		np.maximum(list(mplc.to_rgb(__lowerCAmelCase				)				)				, 0.2				)
    a							=		max(0.8				, np.max(__lowerCAmelCase				)				)
    a							=		{
        "facecolor": "black",
        "alpha": self.alpha,
        "pad": self.pad,
        "edgecolor": "none",
    }
    a		,	a							=		position
    self.ax.text(
        __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, size=font_size * self.scale				, family="sans-serif"				, bbox=__lowerCAmelCase				, verticalalignment="top"				, horizontalalignment=__lowerCAmelCase				, color=__lowerCAmelCase				, zorder=10				, rotation=__lowerCAmelCase				, )

   def 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							int=None				)							->					Any:


    """simple docstring"""






    if saveas is None:
     a							=		self.saveas
    if saveas.lower().endswith(".jpg"				) or saveas.lower().endswith(".png"				):
     cva.imwrite(
         __lowerCAmelCase				, self._get_buffer()[:, :, ::-1]				, )
    else:
     self.fig.savefig(__lowerCAmelCase				)

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


    """simple docstring"""






    a							=		[self.idaobj[i] for i in classes]
    a							=		[self.idaattr[i] for i in attr_classes]
    a							=		[
        f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
        for label, score, attr, attr_score in zip(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)
    ]
    return labels

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


    """simple docstring"""






    a							=		[self.idaobj[i] for i in classes]
    if scores is not None:
     if labels is None:
      a							=		["{:.0f}%".format(s * 100				) for s in scores]
     else:
      a							=		["{} {:.0f}%".format(__lowerCAmelCase				, s * 100				) for li, s in zip(__lowerCAmelCase				, __lowerCAmelCase				)]
    return labels

   def 		A				(   self      :							List[str]				, __lowerCAmelCase      :							str=255				)							->					List[Any]:


    """simple docstring"""






    a							=		np.random.randint(0				, len(_COLORS				)				)
    a							=		_COLORS[idx] * maximum
    if not self.rgb:
     a							=		ret[::-1]
    return ret

   def 		A				(   self      :							Any				)							->					Any:


    """simple docstring"""






    if not self.pynb:
     a		,	a							=		self.canvas.print_to_buffer()
     if (width, height) != (self.width, self.height):
      a							=		cva.resize(self.img				, (width, height)				)
     else:
      a							=		self.img
    else:
     a							=		io.BytesIO()  # works for cairo backend
     self.canvas.print_rgba(__lowerCAmelCase				)
     a		,	a							=		self.width, self.height
     a							=		buf.getvalue()
     a							=		self.img

    a							=		np.frombuffer(__lowerCAmelCase				, dtype="uint8"				)
    a							=		buffer.reshape(__lowerCAmelCase				, __lowerCAmelCase				, 4				)
    a		,	a							=		np.split(__lowerCAmelCase				, [3]				, axis=2				)

    try:
     import numexpr as ne  # fuse them with numexpr

     a							=		ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"				)
    except ImportError:
     a							=		alpha.astype("float32"				) / 2_5_5.0
     a							=		img * (1 - alpha) + rgb * alpha

    return visualized_image.astype("uint8"				)




   def 		A				(   self      :							str				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Optional[int]				)							->					Dict:


    """simple docstring"""






    assert brightness_factor >= -1.0 and brightness_factor <= 1.0
    a							=		mplc.to_rgb(__lowerCAmelCase				)
    a							=		colorsys.rgb_to_hls(*mplc.to_rgb(__lowerCAmelCase				)				)
    a							=		polygon_color[1] + (brightness_factor * polygon_color[1])
    a							=		0.0 if modified_lightness < 0.0 else modified_lightness
    a							=		1.0 if modified_lightness > 1.0 else modified_lightness
    a							=		colorsys.hls_to_rgb(polygon_color[0]				, __lowerCAmelCase				, polygon_color[2]				)
    return modified_color

A_		:  int										=  (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)








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
snake_case_				=      '''src/diffusers'''
snake_case_				=      '''docs/source/en'''
snake_case_				=      '''.'''


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		with open(SCREAMING_SNAKE_CASE_				,							'''r'''				,							encoding='''utf-8'''				,							newline='''\n'''		) as f:
				lowerCamelCase :			List[str]    		=f.readlines()
		# Find the start prompt.
		lowerCamelCase :			List[Any]    		=0
		while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_		):
				start_index += 1
		start_index += 1

		lowerCamelCase :			Dict    		=start_index
		while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_		):
				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 |
snake_case_				=      '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
snake_case_				=      re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
snake_case_				=      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.
snake_case_				=      re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Tuple:
		lowerCamelCase :			Optional[Any]    		=re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''				,							SCREAMING_SNAKE_CASE_		)
		return [m.group(0		) for m in matches]


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		lowerCamelCase :			Dict    		=2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Union[str, Any]    		=(width - text_length) // 2
		lowerCamelCase :			List[str]    		=width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent


def        A__						(							) ->  Union[str, Any]:
		lowerCamelCase :			List[str]    		=diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		lowerCamelCase :			List[Any]    		={
		    name: config_mapping_names[code]
		    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_mapping_names
		}
		lowerCamelCase :			Optional[int]    		={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.
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			str    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Any    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)

		# Let's lookup through all diffusers object (once).
		for attr_name in dir(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Union[str, Any]    		=None
				if attr_name.endswith('''Tokenizer'''		):
						lowerCamelCase :			List[str]    		=slow_tokenizers
						lowerCamelCase :			List[Any]    		=attr_name[:-9]
				elif attr_name.endswith('''TokenizerFast'''		):
						lowerCamelCase :			Any    		=fast_tokenizers
						lowerCamelCase :			Optional[Any]    		=attr_name[:-1_3]
				elif _re_tf_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			Optional[Any]    		=tf_models
						lowerCamelCase :			Any    		=_re_tf_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_flax_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[str]    		=flax_models
						lowerCamelCase :			int    		=_re_flax_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_pt_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[Any]    		=pt_models
						lowerCamelCase :			List[Any]    		=_re_pt_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]

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

    # Let's build that table!
		lowerCamelCase :			Optional[int]    		=list(model_name_to_config.keys()		)
		model_names.sort(key=str.lower		)
		lowerCamelCase :			Union[str, Any]    		=['''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).
		lowerCamelCase :			Tuple    		=[len(SCREAMING_SNAKE_CASE_		) + 2 for c in columns]
		lowerCamelCase :			Dict    		=max([len(SCREAMING_SNAKE_CASE_		) for name in model_names]		) + 2

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

		lowerCamelCase :			Optional[Any]    		={True: '''✅''', False: '''❌'''}
		for name in model_names:
				lowerCamelCase :			str    		=model_name_to_prefix[name]
				lowerCamelCase :			Dict    		=[
				    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(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) for l, w in zip(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)]		) + "|\n"
		return table


def        A__						(							SCREAMING_SNAKE_CASE_=False		) ->  int:
		lowerCamelCase		, lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Tuple    		=_find_text_in_file(
		    filename=os.path.join(SCREAMING_SNAKE_CASE_				,							'''index.mdx'''		)				,							start_prompt='''<!--This table is updated automatically from the auto modules'''				,							end_prompt='''<!-- End table-->'''				,							)
		lowerCamelCase :			List[Any]    		=get_model_table_from_auto_modules()

		if current_table != new_table:
				if overwrite:
						with open(os.path.join(SCREAMING_SNAKE_CASE_				,							'''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__":
				snake_case_				=      argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				snake_case_				=      parser.parse_args()

				check_model_table(args.fix_and_overwrite)

