

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     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  CONFIG_NAME




 @register_to_config
 def __init__( self     ,     _UpperCAmelCase = True     ,     _UpperCAmelCase = 8     ,     _UpperCAmelCase = "lanczos"     ,     _UpperCAmelCase = True     ,     _UpperCAmelCase = False     ,     ):
   super().__init__()




 @staticmethod
 def 						a					( _UpperCAmelCase       ):
   if images.ndim == 3:
     __UpperCAmelCase  :     Union[str, Any]		=    images[None, ...]
   __UpperCAmelCase  :     Optional[int]		=    (images * 255).round().astype('uint8'       )
   if images.shape[-1] == 1:
     # special case for grayscale (single channel) images
     __UpperCAmelCase  :     Optional[int]		=    [Image.fromarray(image.squeeze()     ,     mode='L'       ) for image in images]
   else:
     __UpperCAmelCase  :     Any		=    [Image.fromarray(_UpperCAmelCase       ) for image in images]

   return pil_images




 @staticmethod
 def 						a					( _UpperCAmelCase       ):
   if not isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ):
     __UpperCAmelCase  :     List[str]		=    [images]
   __UpperCAmelCase  :     Optional[Any]		=    [np.array(_UpperCAmelCase       ).astype(np.floataa       ) / 2_5_5.0 for image in images]
   __UpperCAmelCase  :     List[str]		=    np.stack(_UpperCAmelCase     ,     axis=0       )

   return images




 @staticmethod
 def 						a					( _UpperCAmelCase       ):
   if images.ndim == 3:
     __UpperCAmelCase  :     List[Any]		=    images[..., None]

   __UpperCAmelCase  :     int		=    torch.from_numpy(images.transpose(0     ,     3     ,     1     ,     2       )       )
   return images




 @staticmethod
 def 						a					( _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[int]		=    images.cpu().permute(0     ,     2     ,     3     ,     1       ).float().numpy()
   return images




 @staticmethod
 def 						a					( _UpperCAmelCase       ):
   return 2.0 * images - 1.0




 @staticmethod
 def 						a					( _UpperCAmelCase       ):
   return (images / 2 + 0.5).clamp(0     ,     1       )




 @staticmethod
 def 						a					( _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[int]		=    image.convert('RGB'       )
   return image




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = None     ,     ):
   if height is None:
     __UpperCAmelCase  :     int		=    image.height
   if width is None:
     __UpperCAmelCase  :     Dict		=    image.width

   __UpperCAmelCase, __UpperCAmelCase  :     List[str]		=    (
       x - x % self.config.vae_scale_factor for x in (width, height)
   )  # resize to integer multiple of vae_scale_factor
   __UpperCAmelCase  :     Optional[Any]		=    image.resize((width, height)     ,     resample=PIL_INTERPOLATION[self.config.resample]       )
   return image




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = None     ,     ):
   __UpperCAmelCase  :     List[str]		=    (PIL.Image.Image, np.ndarray, torch.Tensor)
   if isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ):
     __UpperCAmelCase  :     Dict		=    [image]
   elif not (isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ) and all(isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ) for i in image       )):
     raise ValueError(
         F"Input is in incorrect format: {[type(_UpperCAmelCase       ) for i in image]}. Currently, we only support {', '.join(_UpperCAmelCase       )}"       )

   if isinstance(image[0]     ,     PIL.Image.Image       ):
     if self.config.do_convert_rgb:
       __UpperCAmelCase  :     str		=    [self.convert_to_rgb(_UpperCAmelCase       ) for i in image]
     if self.config.do_resize:
       __UpperCAmelCase  :     Tuple		=    [self.resize(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ) for i in image]
     __UpperCAmelCase  :     int		=    self.pil_to_numpy(_UpperCAmelCase       )  # to np
     __UpperCAmelCase  :     Optional[int]		=    self.numpy_to_pt(_UpperCAmelCase       )  # to pt

   elif isinstance(image[0]     ,     np.ndarray       ):
     __UpperCAmelCase  :     Any		=    np.concatenate(_UpperCAmelCase     ,     axis=0       ) if image[0].ndim == 4 else np.stack(_UpperCAmelCase     ,     axis=0       )
     __UpperCAmelCase  :     str		=    self.numpy_to_pt(_UpperCAmelCase       )
     __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     Optional[int]		=    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       ):
     __UpperCAmelCase  :     Any		=    torch.cat(_UpperCAmelCase     ,     axis=0       ) if image[0].ndim == 4 else torch.stack(_UpperCAmelCase     ,     axis=0       )
     __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     int		=    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]
   __UpperCAmelCase  :     int		=    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()}]"     ,     _UpperCAmelCase     ,     )
     __UpperCAmelCase  :     Union[str, Any]		=    False

   if do_normalize:
     __UpperCAmelCase  :     Tuple		=    self.normalize(_UpperCAmelCase       )

   return image




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase = "pil"     ,     _UpperCAmelCase = None     ,     ):
   if not isinstance(_UpperCAmelCase     ,     torch.Tensor       ):
     raise ValueError(
         F"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase       )}. We only support pytorch tensor"       )
   if output_type not in ["latent", "pt", "np", "pil"]:
     __UpperCAmelCase  :     Optional[Any]		=    (
         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'     ,     _UpperCAmelCase     ,     standard_warn=_UpperCAmelCase       )
     __UpperCAmelCase  :     List[str]		=    'np'

   if output_type == "latent":
     return image

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

   __UpperCAmelCase  :     Dict		=    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

   __UpperCAmelCase  :     Tuple		=    self.pt_to_numpy(_UpperCAmelCase       )

   if output_type == "np":
     return image

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



class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  CONFIG_NAME




 @register_to_config
 def __init__( self     ,     _UpperCAmelCase = True     ,     _UpperCAmelCase = 8     ,     _UpperCAmelCase = "lanczos"     ,     _UpperCAmelCase = True     ,     ):
   super().__init__()




 @staticmethod
 def 						a					( _UpperCAmelCase       ):
   if images.ndim == 3:
     __UpperCAmelCase  :     Union[str, Any]		=    images[None, ...]
   __UpperCAmelCase  :     Optional[int]		=    (images * 255).round().astype('uint8'       )
   if images.shape[-1] == 1:
     # special case for grayscale (single channel) images
     __UpperCAmelCase  :     Optional[int]		=    [Image.fromarray(image.squeeze()     ,     mode='L'       ) for image in images]
   else:
     __UpperCAmelCase  :     Union[str, Any]		=    [Image.fromarray(image[:, :, :3]       ) for image in images]

   return pil_images




 @staticmethod
 def 						a					( _UpperCAmelCase       ):
   return image[:, :, 1] * 2**8 + image[:, :, 2]




 def 						a					( self     ,     _UpperCAmelCase       ):
   if images.ndim == 3:
     __UpperCAmelCase  :     int		=    images[None, ...]
   __UpperCAmelCase  :     Optional[Any]		=    images[:, :, :, 3:]
   if images.shape[-1] == 6:
     __UpperCAmelCase  :     Dict		=    (images_depth * 255).round().astype('uint8'       )
     __UpperCAmelCase  :     List[str]		=    [
         Image.fromarray(self.rgblike_to_depthmap(_UpperCAmelCase       )     ,     mode='I;16'       ) for image_depth in images_depth
     ]
   elif images.shape[-1] == 4:
     __UpperCAmelCase  :     Dict		=    (images_depth * 6_5_5_3_5.0).astype(np.uintaa       )
     __UpperCAmelCase  :     Any		=    [Image.fromarray(_UpperCAmelCase     ,     mode='I;16'       ) for image_depth in images_depth]
   else:
     raise Exception('Not supported'       )

   return pil_images




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase = "pil"     ,     _UpperCAmelCase = None     ,     ):
   if not isinstance(_UpperCAmelCase     ,     torch.Tensor       ):
     raise ValueError(
         F"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase       )}. We only support pytorch tensor"       )
   if output_type not in ["latent", "pt", "np", "pil"]:
     __UpperCAmelCase  :     int		=    (
         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'     ,     _UpperCAmelCase     ,     standard_warn=_UpperCAmelCase       )
     __UpperCAmelCase  :     Tuple		=    'np'

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

   __UpperCAmelCase  :     Union[str, Any]		=    torch.stack(
       [self.denormalize(image[i]       ) if do_denormalize[i] else image[i] for i in range(image.shape[0]       )]       )

   __UpperCAmelCase  :     Optional[int]		=    self.pt_to_numpy(_UpperCAmelCase       )

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

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








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		()       ->		Optional[int]:
  __UpperCAmelCase  :     Any		=    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"]:
    __UpperCAmelCase  :     str		=    getattr(transformers.models.auto						,					snake_case_						,					snake_case_     )
    if module is None:
      continue
    # all mappings in a single auto modeling file
    __UpperCAmelCase  :     Dict		=    [
        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:
      __UpperCAmelCase  :     Optional[int]		=    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    _snake_case		()       ->		Any:
  # All model names defined in auto mappings
  __UpperCAmelCase  :     int		=    set(get_all_model_names()     )

  with open('tests/utils/tiny_model_summary.json'     ) as fp:
    __UpperCAmelCase  :     Tuple		=    json.load(snake_case_     )
  __UpperCAmelCase  :     int		=    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.
  __UpperCAmelCase  :     Tuple		=    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    _snake_case		(snake_case_				:    List[str]     )       ->		Tuple:
  __UpperCAmelCase  :     Dict		=    COMPOSITE_MODELS.values()

  # All tiny model base names on Hub
  __UpperCAmelCase  :     Optional[int]		=    get_all_model_names()
  __UpperCAmelCase  :     List[Any]		=    hf_api.list_models(
      filter=ModelFilter(
          author='hf-internal-testing'						,					)     )
  __UpperCAmelCase  :     Dict		=    set()
  for x in models:
    __UpperCAmelCase  :     Union[str, Any]		=    x.modelId
    __UpperCAmelCase, __UpperCAmelCase  :     List[str]		=    model.split('/'     )
    if not model.startswith('tiny-random-'     ):
      continue
    __UpperCAmelCase  :     Optional[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(snake_case_     )

  __UpperCAmelCase  :     Optional[Any]		=    sorted(_models     )
  # All tiny model names on Hub
  __UpperCAmelCase  :     List[Any]		=    {}
  for model in models:
    __UpperCAmelCase  :     Any		=    F"hf-internal-testing/tiny-random-{model}"
    __UpperCAmelCase  :     Optional[Any]		=    model.split('-'     )[0]
    try:
      __UpperCAmelCase  :     Tuple		=    hf_api.repo_info(snake_case_     )
      __UpperCAmelCase  :     Optional[int]		=    {
          'tokenizer_classes': set(),
          'processor_classes': set(),
          'model_classes': set(),
          'sha': repo_info.sha,
      }
    except Exception:
      continue
    try:
      time.sleep(1     )
      __UpperCAmelCase  :     List[Any]		=    AutoTokenizer.from_pretrained(snake_case_     )
      content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
    except Exception:
      pass
    try:
      time.sleep(1     )
      __UpperCAmelCase  :     Optional[int]		=    AutoTokenizer.from_pretrained(snake_case_						,					use_fast=snake_case_     )
      content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
    except Exception:
      pass
    try:
      time.sleep(1     )
      __UpperCAmelCase  :     Optional[int]		=    AutoImageProcessor.from_pretrained(snake_case_     )
      content["processor_classes"].add(img_p.__class__.__name__     )
    except Exception:
      pass
    try:
      time.sleep(1     )
      __UpperCAmelCase  :     Optional[int]		=    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     )
      __UpperCAmelCase  :     Tuple		=    getattr(snake_case_						,					snake_case_     )
      __UpperCAmelCase  :     str		=    model_class.from_pretrained(snake_case_     )
      content["model_classes"].add(m.__class__.__name__     )
    except Exception:
      pass
    try:
      time.sleep(1     )
      __UpperCAmelCase  :     Any		=    getattr(snake_case_						,					F"TF{model}"     )
      __UpperCAmelCase  :     List[str]		=    model_class.from_pretrained(snake_case_     )
      content["model_classes"].add(m.__class__.__name__     )
    except Exception:
      pass

    __UpperCAmelCase  :     Optional[int]		=    sorted(content['tokenizer_classes']     )
    __UpperCAmelCase  :     List[str]		=    sorted(content['processor_classes']     )
    __UpperCAmelCase  :     Tuple		=    sorted(content['model_classes']     )

    __UpperCAmelCase  :     str		=    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__":
      _SCREAMING_SNAKE_CASE          = argparse.ArgumentParser()
      parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
      _SCREAMING_SNAKE_CASE          = parser.parse_args()

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

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





