




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__A						:		Dict          =						logging.get_logger(__name__)

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= ["pixel_values"]




					def __init__(   self:		str,     _lowercase:		bool = True,     _lowercase:		Dict[str, int] = None,     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		bool = False,     _lowercase:		Dict[str, int] = None,     _lowercase:		Union[int, float] = 1 / 255,     _lowercase:		bool = False,     _lowercase:		bool = True,     _lowercase:		bool = True,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = True,     **_lowercase:		Union[str, Any],     ):


									'''simple docstring'''


									super().__init__(**_lowercase)
									__lowerCAmelCase					=  size if size is not None else {"""height""": 346, """width""": 346}
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  do_resize
									__lowerCAmelCase					=  size
									__lowerCAmelCase					=  resample
									__lowerCAmelCase					=  do_center_crop
									__lowerCAmelCase					=  crop_size
									__lowerCAmelCase					=  do_rescale
									__lowerCAmelCase					=  rescale_factor
									__lowerCAmelCase					=  rescale_offset
									__lowerCAmelCase					=  do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__lowerCAmelCase					=  image_std if image_std is not None else IMAGENET_STANDARD_STD
									__lowerCAmelCase					=  include_top




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		np.ndarray,     _lowercase:		Dict[str, int],     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return resize(
									    _lowercase,     size=(size["""height"""], size["""width"""]),     resample=_lowercase,     data_format=_lowercase,     **_lowercase)




					def     _lowercase						(   self:		List[str],     _lowercase:		np.ndarray,     _lowercase:		Dict[str, int],     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Tuple,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return center_crop(_lowercase,     size=(size["""height"""], size["""width"""]),     data_format=_lowercase,     **_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		np.ndarray,     _lowercase:		Union[int, float],     _lowercase:		bool = True,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									if offset:
													__lowerCAmelCase					=  (image - 127.5) * scale
													if data_format is not None:
																	__lowerCAmelCase					=  to_channel_dimension_format(_lowercase,     _lowercase)
													__lowerCAmelCase					=  rescaled_image.astype(np.floataa)
									else:
													__lowerCAmelCase					=  rescale(_lowercase,     scale=_lowercase,     data_format=_lowercase,     **_lowercase)
									return rescaled_image




					def     _lowercase						(   self:		Dict,     _lowercase:		np.ndarray,     _lowercase:		Union[float, List[float]],     _lowercase:		Union[float, List[float]],     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Any,     ):


									'''simple docstring'''


									return normalize(_lowercase,     mean=_lowercase,     std=_lowercase,     data_format=_lowercase,     **_lowercase)







					def     _lowercase						(   self:		Dict,     _lowercase:		ImageInput,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		str=None,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		bool = None,     _lowercase:		float = None,     _lowercase:		bool = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[str, TensorType]] = None,     _lowercase:		ChannelDimension = ChannelDimension.FIRST,     **_lowercase:		Tuple,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  do_resize if do_resize is not None else self.do_resize
									__lowerCAmelCase					=  resample if resample is not None else self.resample
									__lowerCAmelCase					=  do_center_crop if do_center_crop is not None else self.do_center_crop
									__lowerCAmelCase					=  do_rescale if do_rescale is not None else self.do_rescale
									__lowerCAmelCase					=  rescale_factor if rescale_factor is not None else self.rescale_factor
									__lowerCAmelCase					=  rescale_offset if rescale_offset is not None else self.rescale_offset
									__lowerCAmelCase					=  do_normalize if do_normalize is not None else self.do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else self.image_mean
									__lowerCAmelCase					=  image_std if image_std is not None else self.image_std
									__lowerCAmelCase					=  include_top if include_top is not None else self.include_top

									__lowerCAmelCase					=  size if size is not None else self.size
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else self.crop_size
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  make_list_of_images(_lowercase)

									if not valid_images(_lowercase):
													raise ValueError(
													    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
													    """torch.Tensor, tf.Tensor or jax.ndarray.""")

									if do_resize and size is None or resample is None:
													raise ValueError("""Size and resample must be specified if do_resize is True.""")

									if do_center_crop and crop_size is None:
													raise ValueError("""Crop size must be specified if do_center_crop is True.""")

									if do_rescale and rescale_factor is None:
													raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

									# All transformations expect numpy arrays.
									__lowerCAmelCase					=  [to_numpy_array(_lowercase) for image in images]

									if do_resize:
													__lowerCAmelCase					=  [self.resize(image=_lowercase,     size=_lowercase,     resample=_lowercase) for image in images]

									if do_center_crop:
													__lowerCAmelCase					=  [self.center_crop(image=_lowercase,     size=_lowercase) for image in images]

									if do_rescale:
													__lowerCAmelCase					=  [self.rescale(image=_lowercase,     scale=_lowercase,     offset=_lowercase) for image in images]

									if do_normalize:
													__lowerCAmelCase					=  [self.normalize(image=_lowercase,     mean=_lowercase,     std=_lowercase) for image in images]

									if include_top:
													__lowerCAmelCase					=  [self.normalize(image=_lowercase,     mean=[0, 0, 0],     std=_lowercase) for image in images]

									__lowerCAmelCase					=  [to_channel_dimension_format(_lowercase,     _lowercase) for image in images]

									__lowerCAmelCase					=  {"""pixel_values""": images}
									return BatchFeature(data=_lowercase,     tensor_type=_lowercase)








'''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,
							)




