


'''simple docstring'''


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


UpperCAmelCase_	:     Tuple							     =	1000





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case=1.2					,      __snake_case="g"					,      __snake_case=0.5					,      __snake_case="-"					,      __snake_case="test_out.jpg"					,      __snake_case=True					,      __snake_case=False					,      __snake_case=None					,      __snake_case=None					,      __snake_case=0.7					,      ):
											if isinstance(__snake_case					,      torch.Tensor						):
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   img.numpy().astype("""np.uint8"""						)
											if isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: str  	=   img_tensorize(__snake_case						)
											assert isinstance(__snake_case					,      np.ndarray						)

											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   img.shape[1], img.shape[0]
											_SCREAMING_SNAKE_CASE				: List[str]  	=   mplfigure.Figure(frameon=__snake_case						)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   fig.get_dpi()
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   (width * scale + 1e-2) / dpi
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   (height * scale + 1e-2) / dpi
											fig.set_size_inches(__snake_case					,      __snake_case						)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   fig.add_axes([0.0, 0.0, 1.0, 1.0]						)
											ax.axis("""off"""						)
											ax.set_xlim(0.0					,      __snake_case						)
											ax.set_ylim(__snake_case						)

											_SCREAMING_SNAKE_CASE				: Tuple  	=   saveas
											_SCREAMING_SNAKE_CASE				: List[str]  	=   rgb
											_SCREAMING_SNAKE_CASE				: int  	=   pynb
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   img
											_SCREAMING_SNAKE_CASE				: List[str]  	=   edgecolor
											_SCREAMING_SNAKE_CASE				: List[str]  	=   0.5
											_SCREAMING_SNAKE_CASE				: Any  	=   linestyle
											_SCREAMING_SNAKE_CASE				: Any  	=   int(np.sqrt(min(__snake_case					,      __snake_case						)						) * scale // 3						)
											_SCREAMING_SNAKE_CASE				: Any  	=   width
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   height
											_SCREAMING_SNAKE_CASE				: Dict  	=   scale
											_SCREAMING_SNAKE_CASE				: Tuple  	=   fig
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   ax
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   pad
											_SCREAMING_SNAKE_CASE				: Tuple  	=   idaobj
											_SCREAMING_SNAKE_CASE				: int  	=   idaattr
											_SCREAMING_SNAKE_CASE				: List[str]  	=   FigureCanvasAgg(__snake_case						)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case=None						):
											if color is None:
															_SCREAMING_SNAKE_CASE				: Dict  	=   self.edgecolor
											((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: int  	=   box
											_SCREAMING_SNAKE_CASE				: Tuple  	=   xa - xa
											_SCREAMING_SNAKE_CASE				: Tuple  	=   ya - ya
											self.ax.add_patch(
											    mpl.patches.Rectangle(
											        (xa, ya)					,      __snake_case					,      __snake_case					,      fill=__snake_case					,      edgecolor=__snake_case					,      linewidth=self.font_size // 3					,      alpha=self.alpha					,      linestyle=self.linestyle					,      )						)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case=None					,      __snake_case=None					,      __snake_case=None					,      __snake_case=None						):
											if len(boxes.shape						) > 2:
															_SCREAMING_SNAKE_CASE				: Dict  	=   boxes[0]
											if len(obj_ids.shape						) > 1:
															_SCREAMING_SNAKE_CASE				: Any  	=   obj_ids[0]
											if len(obj_scores.shape						) > 1:
															_SCREAMING_SNAKE_CASE				: str  	=   obj_scores[0]
											if len(attr_ids.shape						) > 1:
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   attr_ids[0]
											if len(attr_scores.shape						) > 1:
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   attr_scores[0]
											if isinstance(__snake_case					,      torch.Tensor						):
															_SCREAMING_SNAKE_CASE				: Any  	=   boxes.numpy()
											if isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   np.array(__snake_case						)
											assert isinstance(__snake_case					,      np.ndarray						)
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   np.prod(boxes[:, 2:] - boxes[:, :2]					,      axis=1						)
											_SCREAMING_SNAKE_CASE				: str  	=   np.argsort(-areas						).tolist()
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   boxes[sorted_idxs] if boxes is not None else None
											_SCREAMING_SNAKE_CASE				: int  	=   obj_ids[sorted_idxs] if obj_ids is not None else None
											_SCREAMING_SNAKE_CASE				: str  	=   obj_scores[sorted_idxs] if obj_scores is not None else None
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   attr_ids[sorted_idxs] if attr_ids is not None else None
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   attr_scores[sorted_idxs] if attr_scores is not None else None

											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   [self._random_color(maximum=1						) for _ in range(len(__snake_case						)						)]
											_SCREAMING_SNAKE_CASE				: Dict  	=   [assigned_colors[idx] for idx in sorted_idxs]
											if obj_ids is not None:
															_SCREAMING_SNAKE_CASE				: Dict  	=   self._create_text_labels_attr(__snake_case					,      __snake_case					,      __snake_case					,      __snake_case						)
															for i in range(len(__snake_case						)						):
																			_SCREAMING_SNAKE_CASE				: str  	=   assigned_colors[i]
																			self.add_box(boxes[i]					,      __snake_case						)
																			self.draw_labels(labels[i]					,      boxes[i]					,      __snake_case						)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Optional[Any]  	=   box
											_SCREAMING_SNAKE_CASE				: str  	=   (xa, ya)
											_SCREAMING_SNAKE_CASE				: str  	=   (ya - ya) * (xa - xa)
											_SCREAMING_SNAKE_CASE				: Tuple  	=   _SMALL_OBJ * self.scale
											if instance_area < small or ya - ya < 40 * self.scale:
															if ya >= self.height - 5:
																			_SCREAMING_SNAKE_CASE				: List[str]  	=   (xa, ya)
															else:
																			_SCREAMING_SNAKE_CASE				: str  	=   (xa, ya)

											_SCREAMING_SNAKE_CASE				: Tuple  	=   (ya - ya) / np.sqrt(self.height * self.width						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   self._change_color_brightness(__snake_case					,      brightness_factor=0.7						)
											_SCREAMING_SNAKE_CASE				: List[str]  	=   np.clip((height_ratio - 0.02) / 0.08 + 1					,      1.2					,      2						)
											font_size *= 0.75 * self.font_size

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






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case="g"					,      __snake_case="left"					,      ):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   0
											_SCREAMING_SNAKE_CASE				: Dict  	=   self.font_size
											_SCREAMING_SNAKE_CASE				: Tuple  	=   np.maximum(list(mplc.to_rgb(__snake_case						)						)					,      0.2						)
											_SCREAMING_SNAKE_CASE				: Any  	=   max(0.8					,      np.max(__snake_case						)						)
											_SCREAMING_SNAKE_CASE				: Dict  	=   {
											    """facecolor""": """black""",
											    """alpha""": self.alpha,
											    """pad""": self.pad,
											    """edgecolor""": """none""",
											}
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Any  	=   position
											self.ax.text(
											    __snake_case					,      __snake_case					,      __snake_case					,      size=font_size * self.scale					,      family="""sans-serif"""					,      bbox=__snake_case					,      verticalalignment="""top"""					,      horizontalalignment=__snake_case					,      color=__snake_case					,      zorder=10					,      rotation=__snake_case					,      )






							def UpperCAmelCase_			(					self					,      __snake_case=None						):
											if saveas is None:
															_SCREAMING_SNAKE_CASE				: Tuple  	=   self.saveas
											if saveas.lower().endswith(""".jpg"""						) or saveas.lower().endswith(""".png"""						):
															cva.imwrite(
															    __snake_case					,      self._get_buffer()[:, :, ::-1]					,      )
											else:
															self.fig.savefig(__snake_case						)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: str  	=   [self.idaobj[i] for i in classes]
											_SCREAMING_SNAKE_CASE				: Tuple  	=   [self.idaattr[i] for i in attr_classes]
											_SCREAMING_SNAKE_CASE				: Tuple  	=   [
											    f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
											    for label, score, attr, attr_score in zip(__snake_case					,      __snake_case					,      __snake_case					,      __snake_case						)
											]
											return labels






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: str  	=   [self.idaobj[i] for i in classes]
											if scores is not None:
															if labels is None:
																			_SCREAMING_SNAKE_CASE				: Dict  	=   ["""{:.0f}%""".format(s * 100						) for s in scores]
															else:
																			_SCREAMING_SNAKE_CASE				: int  	=   ["""{} {:.0f}%""".format(__snake_case					,      s * 100						) for li, s in zip(__snake_case					,      __snake_case						)]
											return labels






							def UpperCAmelCase_			(					self					,      __snake_case=255						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   np.random.randint(0					,      len(_COLORS						)						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   _COLORS[idx] * maximum
											if not self.rgb:
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   ret[::-1]
											return ret






							def UpperCAmelCase_			(					self						):
											if not self.pynb:
															_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: str  	=   self.canvas.print_to_buffer()
															if (width, height) != (self.width, self.height):
																			_SCREAMING_SNAKE_CASE				: str  	=   cva.resize(self.img					,      (width, height)						)
															else:
																			_SCREAMING_SNAKE_CASE				: List[str]  	=   self.img
											else:
															_SCREAMING_SNAKE_CASE				: int  	=   io.BytesIO()  # works for cairo backend
															self.canvas.print_rgba(__snake_case						)
															_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: str  	=   self.width, self.height
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   buf.getvalue()
															_SCREAMING_SNAKE_CASE				: Dict  	=   self.img

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   np.frombuffer(__snake_case					,      dtype="""uint8"""						)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   buffer.reshape(__snake_case					,      __snake_case					,      4						)
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Optional[Any]  	=   np.split(__snake_case					,      [3]					,      axis=2						)

											try:
															import numexpr as ne  # fuse them with numexpr

															_SCREAMING_SNAKE_CASE				: Dict  	=   ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""						)
											except ImportError:
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   alpha.astype("""float32"""						) / 255.0
															_SCREAMING_SNAKE_CASE				: Tuple  	=   img * (1 - alpha) + rgb * alpha

											return visualized_image.astype("""uint8"""						)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case						):
											assert brightness_factor >= -1.0 and brightness_factor <= 1.0
											_SCREAMING_SNAKE_CASE				: Dict  	=   mplc.to_rgb(__snake_case						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   colorsys.rgb_to_hls(*mplc.to_rgb(__snake_case						)						)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   polygon_color[1] + (brightness_factor * polygon_color[1])
											_SCREAMING_SNAKE_CASE				: Dict  	=   0.0 if modified_lightness < 0.0 else modified_lightness
											_SCREAMING_SNAKE_CASE				: str  	=   1.0 if modified_lightness > 1.0 else modified_lightness
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   colorsys.hls_to_rgb(polygon_color[0]					,      __snake_case					,      polygon_color[2]						)
											return modified_color



UpperCAmelCase_	:     Any							     =	(
    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)
)








'''simple docstring'''


import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   set()
				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
								_SCREAMING_SNAKE_CASE				: Any  	=   getattr(transformers.models.auto							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
								if module is None:
												continue
								# all mappings in a single auto modeling file
								_SCREAMING_SNAKE_CASE				: str  	=   [
								    x
								    for x in dir(SCREAMING_SNAKE_CASE__  )
								    if x.endswith("""_MAPPING_NAMES"""  )
								    and (x.startswith("""MODEL_"""  ) or x.startswith("""TF_MODEL_"""  ) or x.startswith("""FLAX_MODEL_"""  ))
								]
								for name in mapping_names:
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   getattr(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(SCREAMING_SNAKE_CASE__							,   (list, tuple)  ):
																								model_names.update(SCREAMING_SNAKE_CASE__  )
																				elif isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
																								model_names.add(SCREAMING_SNAKE_CASE__  )

				return sorted(SCREAMING_SNAKE_CASE__  )







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   set(get_all_model_names()  )

				with open("""tests/utils/tiny_model_summary.json"""  ) as fp:
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   json.load(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   set()
				for model_base_name in tiny_model_info:
								tiny_models_names.update(tiny_model_info[model_base_name]["""model_classes"""]  )

				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
				_SCREAMING_SNAKE_CASE				: str  	=   model_names.difference(SCREAMING_SNAKE_CASE__  )
				for model_name in copy.copy(SCREAMING_SNAKE_CASE__  ):
								if not model_name.startswith("""TF"""  ) and f"""TF{model_name}""" in not_on_hub:
												tiny_models_names.remove(SCREAMING_SNAKE_CASE__  )
								elif model_name.startswith("""TF"""  ) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(SCREAMING_SNAKE_CASE__  )

				return sorted(SCREAMING_SNAKE_CASE__  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   get_all_model_names()
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   hf_api.list_models(
				    filter=ModelFilter(
				        author="""hf-internal-testing"""							,   )  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   set()
				for x in models:
								_SCREAMING_SNAKE_CASE				: Tuple  	=   x.modelId
								_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model.split("""/"""  )
								if not model.startswith("""tiny-random-"""  ):
												continue
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   model.replace("""tiny-random-"""							,   """"""  )
								if not model[0].isupper():
												continue
								if model not in model_names and model not in special_models:
												continue
								_models.add(SCREAMING_SNAKE_CASE__  )

				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   sorted(_models  )
				# All tiny model names on Hub
				_SCREAMING_SNAKE_CASE				: Dict  	=   {}
				for model in models:
								_SCREAMING_SNAKE_CASE				: str  	=   f"""hf-internal-testing/tiny-random-{model}"""
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   model.split("""-"""  )[0]
								try:
												_SCREAMING_SNAKE_CASE				: Tuple  	=   hf_api.repo_info(SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   {
												    """tokenizer_classes""": set(),
												    """processor_classes""": set(),
												    """model_classes""": set(),
												    """sha""": repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: int  	=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							,   use_fast=SCREAMING_SNAKE_CASE__  )
												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: str  	=   AutoImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["processor_classes"].add(img_p.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Any  	=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__  )
												if not isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
																content["processor_classes"].add(feat_p.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: int  	=   getattr(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Tuple  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   getattr(SCREAMING_SNAKE_CASE__							,   f"""TF{model}"""  )
												_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass

								_SCREAMING_SNAKE_CASE				: List[Any]  	=   sorted(content["""tokenizer_classes"""]  )
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   sorted(content["""processor_classes"""]  )
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   sorted(content["""model_classes"""]  )

								_SCREAMING_SNAKE_CASE				: List[Any]  	=   content
								with open(os.path.join(SCREAMING_SNAKE_CASE__							,   """hub_tiny_model_summary.json"""  )							,   """w"""  ) as fp:
												json.dump(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   ensure_ascii=SCREAMING_SNAKE_CASE__							,   indent=4  )


if __name__ == "__main__":
				UpperCAmelCase_	:     Any							     =	argparse.ArgumentParser()
				parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
				UpperCAmelCase_	:     Optional[int]							     =	parser.parse_args()

				# This has to be `spawn` to avoid hanging forever!
				multiprocessing.set_start_method('spawn')

				UpperCAmelCase_	:     Any							     =	'tiny_models'
				UpperCAmelCase_	:     Tuple							     =	True
				UpperCAmelCase_	:     Tuple							     =	None
				UpperCAmelCase_	:     Optional[int]							     =	get_tiny_model_names_from_repo()
				UpperCAmelCase_	:     Dict							     =	True
				UpperCAmelCase_	:     str							     =	True
				UpperCAmelCase_	:     Optional[int]							     =	'hf-internal-testing'

				create_tiny_models(
				    output_path,
				    all,
				    model_types,
				    models_to_skip,
				    no_check,
				    upload,
				    organization,
				    token=os.environ.get('TOKEN', None),
				    num_workers=args.num_workers,
				)




