





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Optional[Any]      ,				snake_case__	:Optional[Any]=0.999      ,				snake_case__	:Tuple="cosine"      ,				)   ->    Optional[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(snake_case__	:List[Any]  ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(snake_case__	:Optional[int]  ):
      return math.exp(t * -12.0  )

  else:
    raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""  )

  _lowercase						    =			[]
  for i in range(snake_case__  ):
    _lowercase						    =			i / num_diffusion_timesteps
    _lowercase						    =			(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(snake_case__  ) / alpha_bar_fn(snake_case__  )      ,				snake_case__  )  )
  return torch.tensor(snake_case__      ,				dtype=torch.floataa  )







class 					A_					(   UpperCAmelCase       ,						UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Optional[Any]  			=	[e.name for e in KarrasDiffusionSchedulers]
       SCREAMING_SNAKE_CASE_   :			Dict  			=	2







       @register_to_config
       def __init__(     self						:						Union[str, Any]    ,__A						:						int = 1000    ,__A						:						float = 0.00085    ,__A						:						float = 0.012    ,__A						:						str = "linear"    ,__A						:						Optional[Union[np.ndarray, List[float]]] = None    ,__A						:						str = "epsilon"    ,__A						:						str = "linspace"    ,__A						:						int = 0    ,)    ->       Union[str, Any]:
         if trained_betas is not None:
           _lowercase						    =			torch.tensor(__A    ,dtype=torch.floataa	)
         elif beta_schedule == "linear":
           _lowercase						    =			torch.linspace(__A    ,__A    ,__A    ,dtype=torch.floataa	)
         elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           _lowercase						    =			(
               torch.linspace(beta_start**0.5    ,beta_end**0.5    ,__A    ,dtype=torch.floataa	) ** 2
           )
         elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           _lowercase						    =			betas_for_alpha_bar(__A	)
         else:
           raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""	)

         _lowercase						    =			1.0 - self.betas
         _lowercase						    =			torch.cumprod(self.alphas    ,dim=0	)

         #  set all values
         self.set_timesteps(__A    ,__A    ,__A	)







       def  __UpperCAmelCase			(     self						:						str    ,__A						:						List[str]    ,__A						:						Any=None	)    ->       str:
         if schedule_timesteps is None:
           _lowercase						    =			self.timesteps

         _lowercase						    =			(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter	) == 0:
           _lowercase						    =			1 if len(__A	) > 1 else 0
         else:
           _lowercase						    =			timestep.cpu().item() if torch.is_tensor(__A	) else timestep
           _lowercase						    =			self._index_counter[timestep_int]

         return indices[pos].item()







       @property
       def  __UpperCAmelCase			(     self						:						int	)    ->       Tuple:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5







       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						torch.FloatTensor    ,__A						:						Union[float, torch.FloatTensor]    ,)    ->       torch.FloatTensor:
         _lowercase						    =			self.index_for_timestep(__A	)

         if self.state_in_first_order:
           _lowercase						    =			self.sigmas[step_index]
         else:
           _lowercase						    =			self.sigmas_interpol[step_index]

         _lowercase						    =			sample / ((sigma**2 + 1) ** 0.5)
         return sample







       def  __UpperCAmelCase			(     self						:						Tuple    ,__A						:						int    ,__A						:						Union[str, torch.device] = None    ,__A						:						Optional[int] = None    ,)    ->       str:
         _lowercase						    =			num_inference_steps

         _lowercase						    =			num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
           _lowercase						    =			np.linspace(0    ,num_train_timesteps - 1    ,__A    ,dtype=__A	)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
           _lowercase						    =			num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           _lowercase						    =			(np.arange(0    ,__A	) * step_ratio).round()[::-1].copy().astype(__A	)
           timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
           _lowercase						    =			num_train_timesteps / self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           _lowercase						    =			(np.arange(__A    ,0    ,-step_ratio	)).round().copy().astype(__A	)
           timesteps -= 1
         else:
           raise ValueError(
               F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""	)

         _lowercase						    =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
         _lowercase						    =			torch.from_numpy(np.log(__A	)	).to(__A	)

         _lowercase						    =			np.interp(__A    ,np.arange(0    ,len(__A	)	)    ,__A	)
         _lowercase						    =			np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
         _lowercase						    =			torch.from_numpy(__A	).to(device=__A	)

         # interpolate sigmas
         _lowercase						    =			sigmas.log().lerp(sigmas.roll(1	).log()    ,0.5	).exp()

         _lowercase						    =			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
         _lowercase						    =			torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)

         if str(__A	).startswith('mps'	):
           # mps does not support float64
           _lowercase						    =			torch.from_numpy(__A	).to(__A    ,dtype=torch.floataa	)
         else:
           _lowercase						    =			torch.from_numpy(__A	).to(__A	)

         # interpolate timesteps
         _lowercase						    =			self.sigma_to_t(__A	).to(__A    ,dtype=timesteps.dtype	)
         _lowercase						    =			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])    ,dim=-1	).flatten()

         _lowercase						    =			torch.cat([timesteps[:1], interleaved_timesteps]	)

         _lowercase						    =			None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         _lowercase						    =			defaultdict(__A	)







       def  __UpperCAmelCase			(     self						:						Optional[Any]    ,__A						:						str	)    ->       List[str]:
         # get log sigma
         _lowercase						    =			sigma.log()

         # get distribution
         _lowercase						    =			log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         _lowercase						    =			dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
         _lowercase						    =			low_idx + 1

         _lowercase						    =			self.log_sigmas[low_idx]
         _lowercase						    =			self.log_sigmas[high_idx]

         # interpolate sigmas
         _lowercase						    =			(low - log_sigma) / (low - high)
         _lowercase						    =			w.clamp(0    ,1	)

         # transform interpolation to time range
         _lowercase						    =			(1 - w) * low_idx + w * high_idx
         _lowercase						    =			t.view(sigma.shape	)
         return t







       @property
       def  __UpperCAmelCase			(     self						:						Any	)    ->       Union[str, Any]:
         return self.sample is None







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						Union[torch.FloatTensor, np.ndarray]    ,__A						:						Union[float, torch.FloatTensor]    ,__A						:						Union[torch.FloatTensor, np.ndarray]    ,__A						:						bool = True    ,)    ->       Union[SchedulerOutput, Tuple]:
         _lowercase						    =			self.index_for_timestep(__A	)

         # advance index counter by 1
         _lowercase						    =			timestep.cpu().item() if torch.is_tensor(__A	) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
           _lowercase						    =			self.sigmas[step_index]
           _lowercase						    =			self.sigmas_interpol[step_index + 1]
           _lowercase						    =			self.sigmas[step_index + 1]
         else:
           # 2nd order / KDPM2's method
           _lowercase						    =			self.sigmas[step_index - 1]
           _lowercase						    =			self.sigmas_interpol[step_index]
           _lowercase						    =			self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         _lowercase						    =			0
         _lowercase						    =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
           _lowercase						    =			sigma_hat if self.state_in_first_order else sigma_interpol
           _lowercase						    =			sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
           _lowercase						    =			sigma_hat if self.state_in_first_order else sigma_interpol
           _lowercase						    =			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
         elif self.config.prediction_type == "sample":
           raise NotImplementedError('prediction_type not implemented yet: sample'	)
         else:
           raise ValueError(
               F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

         if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           _lowercase						    =			(sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           _lowercase						    =			sigma_interpol - sigma_hat

           # store for 2nd order step
           _lowercase						    =			sample
         else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           _lowercase						    =			(sample - pred_original_sample) / sigma_interpol

           # 3. delta timestep
           _lowercase						    =			sigma_next - sigma_hat

           _lowercase						    =			self.sample
           _lowercase						    =			None

         _lowercase						    =			sample + derivative * dt

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=__A	)







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						torch.FloatTensor    ,__A						:						torch.FloatTensor    ,__A						:						torch.FloatTensor    ,)    ->       torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         _lowercase						    =			self.sigmas.to(device=original_samples.device    ,dtype=original_samples.dtype	)
         if original_samples.device.type == "mps" and torch.is_floating_point(__A	):
           # mps does not support float64
           _lowercase						    =			self.timesteps.to(original_samples.device    ,dtype=torch.floataa	)
           _lowercase						    =			timesteps.to(original_samples.device    ,dtype=torch.floataa	)
         else:
           _lowercase						    =			self.timesteps.to(original_samples.device	)
           _lowercase						    =			timesteps.to(original_samples.device	)

         _lowercase						    =			[self.index_for_timestep(__A    ,__A	) for t in timesteps]

         _lowercase						    =			sigmas[step_indices].flatten()
         while len(sigma.shape	) < len(original_samples.shape	):
           _lowercase						    =			sigma.unsqueeze(-1	)

         _lowercase						    =			original_samples + noise * sigma
         return noisy_samples







       def __len__(     self						:						str	)    ->       Tuple:
         return self.config.num_train_timesteps
'''simple docstring'''




import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__lowercase        =    {
    '''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
    ''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
}






def 							snake_case__				(     _A:   str = "dhaka" ,  _A:   int = 5					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    min(_A ,  50					)  # Prevent abuse!
		lowerCAmelCase     =    {
		    """q""": query,
		    """tbm""": """isch""",
		    """hl""": """en""",
		    """ijn""": """0""",
		}

		lowerCAmelCase     =    requests.get("""https://www.google.com/search""" ,  params=_A ,  headers=_A					)
		lowerCAmelCase     =    BeautifulSoup(html.text ,  """html.parser"""					)
		lowerCAmelCase     =    """""".join(
		    re.findall(r"""AF_initDataCallback\(([^<]+)\);""" ,  str(soup.select("""script"""					)					)					)					)

		lowerCAmelCase     =    json.dumps(_A					)
		lowerCAmelCase     =    json.loads(_A					)

		lowerCAmelCase     =    re.findall(
		    r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",""" ,  _A ,  )
		if not matched_google_image_data:
				return 0

		lowerCAmelCase     =    re.sub(
		    r"""\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]""" ,  """""" ,  str(_A					) ,  )

		lowerCAmelCase     =    re.findall(
		    r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]""" ,  _A ,  )
		for index, fixed_full_res_image in enumerate(_A					):
				if index >= max_images:
						return index
				lowerCAmelCase     =    bytes(_A ,  """ascii"""					).decode(
				    """unicode-escape"""					)
				lowerCAmelCase     =    bytes(_A ,  """ascii"""					).decode(
				    """unicode-escape"""					)
				lowerCAmelCase     =    urllib.request.build_opener()
				lowerCAmelCase     =    [
				    (
				        """User-Agent""",
				        """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
				        """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582""",
				    )
				]
				urllib.request.install_opener(_A					)
				lowerCAmelCase     =    f"query_{query.replace(' ' ,  '_'					)}"
				if not os.path.exists(_A					):
						os.makedirs(_A					)
				urllib.request.urlretrieve(  # noqa: S310
				    _A ,  f"{path_name}/original_size_img_{index}.jpg"					)
		return index


if __name__ == "__main__":
						try:
												__lowercase        =    download_images_from_google_query(sys.argv[1])
												print(f'{image_count} images were downloaded to disk.')
						except IndexError:
												print('''Please provide a search term.''')
												raise




