

'''simple docstring'''







from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import (
							    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
							    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
							    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
							    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
							)


_lowerCamelCase    =  logging.get_logger(__name__)


_lowerCamelCase    =  Dict[str, Any]
_lowerCamelCase    =  List[Prediction]
@add_end_docstrings(_snake_case  )
class 					__a   (		_snake_case  ):

						def __init__(		self	:						Dict    ,			*lowercase__	:						Optional[int]    ,			**lowercase__	:						Union[str, Any])						->List[str]:

							"""simple docstring"""



							super().__init__(*lowercase__    ,			**lowercase__)

							if self.framework == "tf":
								raise ValueError(f"""The {self.__class__} is only available in PyTorch.""")

							requires_backends(self    ,			"""vision""")
							self.check_model_type(
							    dict(
							        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()))

						def 	_UpperCAmelCase							(		self	:						int    ,			**lowercase__	:						int)						->List[str]:

							"""simple docstring"""



							_lowercase							=     {}
							_lowercase							=     {}
							if "subtask" in kwargs:
								_lowercase							=     kwargs["""subtask"""]
								_lowercase							=     kwargs["""subtask"""]
							if "threshold" in kwargs:
								_lowercase							=     kwargs["""threshold"""]
							if "mask_threshold" in kwargs:
								_lowercase							=     kwargs["""mask_threshold"""]
							if "overlap_mask_area_threshold" in kwargs:
								_lowercase							=     kwargs["""overlap_mask_area_threshold"""]

							return preprocess_kwargs, {}, postprocess_kwargs

						def __call__(		self	:						str    ,			lowercase__	:						Optional[int]    ,			**lowercase__	:						Optional[Any])						->Union[Predictions, List[Prediction]]:

							"""simple docstring"""



							return super().__call__(lowercase__    ,			**lowercase__)

						def 	_UpperCAmelCase							(		self	:						List[Any]    ,			lowercase__	:						Optional[int]    ,			lowercase__	:						List[Any]=None)						->List[str]:

							"""simple docstring"""



							_lowercase							=     load_image(lowercase__)
							_lowercase							=     [(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
								if subtask is None:
									_lowercase							=     {}
								else:
									_lowercase							=     {"""task_inputs""": [subtask]}
								_lowercase							=     self.image_processor(images=[image]    ,			return_tensors="""pt"""    ,			**lowercase__)
								_lowercase							=     self.tokenizer(
								    inputs["""task_inputs"""]    ,			padding="""max_length"""    ,			max_length=self.model.config.task_seq_len    ,			return_tensors=self.framework    ,			)["""input_ids"""]
							else:
								_lowercase							=     self.image_processor(images=[image]    ,			return_tensors="""pt""")
							_lowercase							=     target_size
							return inputs

						def 	_UpperCAmelCase							(		self	:						str    ,			lowercase__	:						Optional[int])						->Tuple:

							"""simple docstring"""



							_lowercase							=     model_inputs.pop("""target_size""")
							_lowercase							=     self.model(**lowercase__)
							_lowercase							=     target_size
							return model_outputs


						def 	_UpperCAmelCase							(		self	:						Optional[int]    ,			lowercase__	:						List[str]    ,			lowercase__	:						Tuple=None    ,			lowercase__	:						Optional[Any]=0.9    ,			lowercase__	:						List[str]=0.5    ,			lowercase__	:						Dict=0.5)						->int:

							"""simple docstring"""



							_lowercase							=     None
							if subtask in {"panoptic", None} and hasattr(self.image_processor    ,			"""post_process_panoptic_segmentation"""):
								_lowercase							=     self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor    ,			"""post_process_instance_segmentation"""):
								_lowercase							=     self.image_processor.post_process_instance_segmentation

							if fn is not None:
								_lowercase							=     fn(
								    lowercase__    ,			threshold=lowercase__    ,			mask_threshold=lowercase__    ,			overlap_mask_area_threshold=lowercase__    ,			target_sizes=model_outputs["""target_size"""]    ,			)[0]

								_lowercase							=     []
								_lowercase							=     outputs["""segmentation"""]

								for segment in outputs["segments_info"]:
									_lowercase							=     (segmentation == segment["""id"""]) * 2_55
									_lowercase							=     Image.fromarray(mask.numpy().astype(np.uinta)    ,			mode="""L""")
									_lowercase							=     self.model.config.idalabel[segment["""label_id"""]]
									_lowercase							=     segment["""score"""]
									annotation.append({"""score""": score, """label""": label, """mask""": mask})

							elif subtask in {"semantic", None} and hasattr(self.image_processor    ,			"""post_process_semantic_segmentation"""):
								_lowercase							=     self.image_processor.post_process_semantic_segmentation(
								    lowercase__    ,			target_sizes=model_outputs["""target_size"""])[0]

								_lowercase							=     []
								_lowercase							=     outputs.numpy()
								_lowercase							=     np.unique(lowercase__)

								for label in labels:
									_lowercase							=     (segmentation == label) * 2_55
									_lowercase							=     Image.fromarray(mask.astype(np.uinta)    ,			mode="""L""")
									_lowercase							=     self.model.config.idalabel[label]
									annotation.append({"""score""": None, """label""": label, """mask""": mask})
							else:
								raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model)}""")
							return annotation







'''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 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     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"]:
		_lowercase							=     getattr(transformers.models.auto							,       snake_case_							,       snake_case_	)
		if module is None:
			continue
		# all mappings in a single auto modeling file
		_lowercase							=     [
		    x
		    for x in dir(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:
			_lowercase							=     getattr(snake_case_							,       snake_case_	)
			if mapping is not None:
				for v in mapping.values():
					if isinstance(snake_case_							,       (list, tuple)	):
						model_names.update(snake_case_	)
					elif isinstance(snake_case_							,       snake_case_	):
						model_names.add(snake_case_	)

	return sorted(snake_case_	)






def 					_SCREAMING_SNAKE_CASE		(						):
	# All model names defined in auto mappings
	_lowercase							=     set(get_all_model_names()	)

	with open("""tests/utils/tiny_model_summary.json"""	) as fp:
		_lowercase							=     json.load(snake_case_	)
	_lowercase							=     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.
	_lowercase							=     model_names.difference(snake_case_	)
	for model_name in copy.copy(snake_case_	):
		if not model_name.startswith("""TF"""	) and F"""TF{model_name}""" in not_on_hub:
			tiny_models_names.remove(snake_case_	)
		elif model_name.startswith("""TF"""	) and model_name[2:] in not_on_hub:
			tiny_models_names.remove(snake_case_	)

	return sorted(snake_case_	)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	_lowercase							=     COMPOSITE_MODELS.values()

	# All tiny model base names on Hub
	_lowercase							=     get_all_model_names()
	_lowercase							=     hf_api.list_models(
	    filter=ModelFilter(
	        author="""hf-internal-testing"""							,       )	)
	_lowercase							=     set()
	for x in models:
		_lowercase							=     x.modelId
		_lowercase		,		_lowercase							=     model.split("""/"""	)
		if not model.startswith("""tiny-random-"""	):
			continue
		_lowercase							=     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(snake_case_	)

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

		_lowercase							=     sorted(content["""tokenizer_classes"""]	)
		_lowercase							=     sorted(content["""processor_classes"""]	)
		_lowercase							=     sorted(content["""model_classes"""]	)

		_lowercase							=     content
		with open(os.path.join(snake_case_							,       """hub_tiny_model_summary.json"""	)							,       """w"""	) as fp:
			json.dump(snake_case_							,       snake_case_							,       ensure_ascii=snake_case_							,       indent=4	)


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

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

							_lowerCamelCase    =  'tiny_models'
							_lowerCamelCase    =  True
							_lowerCamelCase    =  None
							_lowerCamelCase    =  get_tiny_model_names_from_repo()
							_lowerCamelCase    =  True
							_lowerCamelCase    =  True
							_lowerCamelCase    =  '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,
							)




