

'''simple docstring'''







import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
class 					__a   (		_snake_case  ):
						__SCREAMING_SNAKE_CASE     :					Optional[int]  =      CONFIG_NAME

						@register_to_config
						def __init__(		self	:						Optional[Any]    ,			lowercase__	:						bool = True    ,			lowercase__	:						int = 8    ,			lowercase__	:						str = "lanczos"    ,			lowercase__	:						bool = True    ,			lowercase__	:						bool = False    ,			)						->Dict:

							"""simple docstring"""



							super().__init__()

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						np.ndarray)						->PIL.Image.Image:

							"""simple docstring"""



							if images.ndim == 3:
								_lowercase							=     images[None, ...]
							_lowercase							=     (images * 2_55).round().astype("""uint8""")
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								_lowercase							=     [Image.fromarray(image.squeeze()    ,			mode="""L""") for image in images]
							else:
								_lowercase							=     [Image.fromarray(lowercase__) for image in images]

							return pil_images

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						Union[List[PIL.Image.Image], PIL.Image.Image])						->np.ndarray:

							"""simple docstring"""



							if not isinstance(lowercase__    ,			lowercase__):
								_lowercase							=     [images]
							_lowercase							=     [np.array(lowercase__).astype(np.floataa) / 255.0 for image in images]
							_lowercase							=     np.stack(lowercase__    ,			axis=0)

							return images

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						np.ndarray)						->torch.FloatTensor:

							"""simple docstring"""



							if images.ndim == 3:
								_lowercase							=     images[..., None]

							_lowercase							=     torch.from_numpy(images.transpose(0    ,			3    ,			1    ,			2))
							return images

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						torch.FloatTensor)						->np.ndarray:

							"""simple docstring"""



							_lowercase							=     images.cpu().permute(0    ,			2    ,			3    ,			1).float().numpy()
							return images

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						Optional[Any])						->Optional[int]:

							"""simple docstring"""



							return 2.0 * images - 1.0

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						List[Any])						->Optional[int]:

							"""simple docstring"""



							return (images / 2 + 0.5).clamp(0    ,			1)

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						PIL.Image.Image)						->PIL.Image.Image:

							"""simple docstring"""



							_lowercase							=     image.convert("""RGB""")
							return image

						def 	_UpperCAmelCase							(		self	:						Tuple    ,			lowercase__	:						PIL.Image.Image    ,			lowercase__	:						Optional[int] = None    ,			lowercase__	:						Optional[int] = None    ,			)						->PIL.Image.Image:

							"""simple docstring"""



							if height is None:
								_lowercase							=     image.height
							if width is None:
								_lowercase							=     image.width

							_lowercase		,		_lowercase							=     (
							    x - x % self.config.vae_scale_factor for x in (width, height)
							)  # resize to integer multiple of vae_scale_factor
							_lowercase							=     image.resize((width, height)    ,			resample=PIL_INTERPOLATION[self.config.resample])
							return image

						def 	_UpperCAmelCase							(		self	:						Dict    ,			lowercase__	:						Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]    ,			lowercase__	:						Optional[int] = None    ,			lowercase__	:						Optional[int] = None    ,			)						->torch.Tensor:

							"""simple docstring"""



							_lowercase							=     (PIL.Image.Image, np.ndarray, torch.Tensor)
							if isinstance(lowercase__    ,			lowercase__):
								_lowercase							=     [image]
							elif not (isinstance(lowercase__    ,			lowercase__) and all(isinstance(lowercase__    ,			lowercase__) for i in image)):
								raise ValueError(
								    f"""Input is in incorrect format: {[type(lowercase__) for i in image]}. Currently, we only support {", ".join(lowercase__)}""")

							if isinstance(image[0]    ,			PIL.Image.Image):
								if self.config.do_convert_rgb:
									_lowercase							=     [self.convert_to_rgb(lowercase__) for i in image]
								if self.config.do_resize:
									_lowercase							=     [self.resize(lowercase__    ,			lowercase__    ,			lowercase__) for i in image]
								_lowercase							=     self.pil_to_numpy(lowercase__)  # to np
								_lowercase							=     self.numpy_to_pt(lowercase__)  # to pt

							elif isinstance(image[0]    ,			np.ndarray):
								_lowercase							=     np.concatenate(lowercase__    ,			axis=0) if image[0].ndim == 4 else np.stack(lowercase__    ,			axis=0)
								_lowercase							=     self.numpy_to_pt(lowercase__)
								_lowercase		,		_lowercase		,		_lowercase		,		_lowercase							=     image.shape
								if self.config.do_resize and (
								    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
								):
									raise ValueError(
									    f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
									    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""")

							elif isinstance(image[0]    ,			torch.Tensor):
								_lowercase							=     torch.cat(lowercase__    ,			axis=0) if image[0].ndim == 4 else torch.stack(lowercase__    ,			axis=0)
								_lowercase		,		_lowercase		,		_lowercase		,		_lowercase							=     image.shape

								# don't need any preprocess if the image is latents
								if channel == 4:
									return image

								if self.config.do_resize and (
								    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
								):
									raise ValueError(
									    f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
									    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""")

        # expected range [0,1], normalize to [-1,1]
							_lowercase							=     self.config.do_normalize
							if image.min() < 0:
								warnings.warn(
								    """Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] """
								    f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""    ,			lowercase__    ,			)
								_lowercase							=     False

							if do_normalize:
								_lowercase							=     self.normalize(lowercase__)

							return image


						def 	_UpperCAmelCase							(		self	:						List[str]    ,			lowercase__	:						torch.FloatTensor    ,			lowercase__	:						str = "pil"    ,			lowercase__	:						Optional[List[bool]] = None    ,			)						->List[Any]:

							"""simple docstring"""



							if not isinstance(lowercase__    ,			torch.Tensor):
								raise ValueError(
								    f"""Input for postprocessing is in incorrect format: {type(lowercase__)}. We only support pytorch tensor""")
							if output_type not in ["latent", "pt", "np", "pil"]:
								_lowercase							=     (
								    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
								    """`pil`, `np`, `pt`, `latent`"""
								)
								deprecate("""Unsupported output_type"""    ,			"""1.0.0"""    ,			lowercase__    ,			standard_warn=lowercase__)
								_lowercase							=     """np"""

							if output_type == "latent":
								return image

							if do_denormalize is None:
								_lowercase							=     [self.config.do_normalize] * image.shape[0]

							_lowercase							=     torch.stack(
							    [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])])

							if output_type == "pt":
								return image

							_lowercase							=     self.pt_to_numpy(lowercase__)

							if output_type == "np":
								return image

							if output_type == "pil":
								return self.numpy_to_pil(lowercase__)






class 					__a   (		_snake_case  ):
						__SCREAMING_SNAKE_CASE     :					int  =      CONFIG_NAME

						@register_to_config
						def __init__(		self	:						Any    ,			lowercase__	:						bool = True    ,			lowercase__	:						int = 8    ,			lowercase__	:						str = "lanczos"    ,			lowercase__	:						bool = True    ,			)						->Any:

							"""simple docstring"""



							super().__init__()

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						List[str])						->List[Any]:

							"""simple docstring"""



							if images.ndim == 3:
								_lowercase							=     images[None, ...]
							_lowercase							=     (images * 2_55).round().astype("""uint8""")
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								_lowercase							=     [Image.fromarray(image.squeeze()    ,			mode="""L""") for image in images]
							else:
								_lowercase							=     [Image.fromarray(image[:, :, :3]) for image in images]

							return pil_images

						@staticmethod
						def 	_UpperCAmelCase							(		lowercase__	:						Any)						->List[str]:

							"""simple docstring"""



							return image[:, :, 1] * 2**8 + image[:, :, 2]

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

							"""simple docstring"""



							if images.ndim == 3:
								_lowercase							=     images[None, ...]
							_lowercase							=     images[:, :, :, 3:]
							if images.shape[-1] == 6:
								_lowercase							=     (images_depth * 2_55).round().astype("""uint8""")
								_lowercase							=     [
								    Image.fromarray(self.rgblike_to_depthmap(lowercase__)    ,			mode="""I;16""") for image_depth in images_depth
								]
							elif images.shape[-1] == 4:
								_lowercase							=     (images_depth * 65535.0).astype(np.uintaa)
								_lowercase							=     [Image.fromarray(lowercase__    ,			mode="""I;16""") for image_depth in images_depth]
							else:
								raise Exception("""Not supported""")

							return pil_images


						def 	_UpperCAmelCase							(		self	:						Optional[Any]    ,			lowercase__	:						torch.FloatTensor    ,			lowercase__	:						str = "pil"    ,			lowercase__	:						Optional[List[bool]] = None    ,			)						->List[Any]:

							"""simple docstring"""



							if not isinstance(lowercase__    ,			torch.Tensor):
								raise ValueError(
								    f"""Input for postprocessing is in incorrect format: {type(lowercase__)}. We only support pytorch tensor""")
							if output_type not in ["latent", "pt", "np", "pil"]:
								_lowercase							=     (
								    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
								    """`pil`, `np`, `pt`, `latent`"""
								)
								deprecate("""Unsupported output_type"""    ,			"""1.0.0"""    ,			lowercase__    ,			standard_warn=lowercase__)
								_lowercase							=     """np"""

							if do_denormalize is None:
								_lowercase							=     [self.config.do_normalize] * image.shape[0]

							_lowercase							=     torch.stack(
							    [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])])

							_lowercase							=     self.pt_to_numpy(lowercase__)

							if output_type == "np":
								if image.shape[-1] == 6:
									_lowercase							=     np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image]    ,			axis=0)
								else:
									_lowercase							=     image[:, :, :, 3:]
								return image[:, :, :, :3], image_depth

							if output_type == "pil":
								return self.numpy_to_pil(lowercase__), self.numpy_to_depth(lowercase__)
							else:
								raise Exception(f"""This type {output_type} is not supported""")







'''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,
							)




