


"""simple docstring"""


# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class 						_UpperCamelCase						:
							'''simple docstring'''







							__UpperCAmelCase		:					CommonSchedulerState

							# setable values
							__UpperCAmelCase		:					jnp.ndarray
							__UpperCAmelCase		:					jnp.ndarray
							__UpperCAmelCase		:					Optional[int]          =None

							@classmethod
							def     snake_case       (  cls					,     __a					,     __a					,     __a			):
										return cls(common=__a					,     init_noise_sigma=__a					,     timesteps=__a			)
@dataclass
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					DDPMSchedulerState
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					str          =[e.name for e in FlaxKarrasDiffusionSchedulers]

							__UpperCAmelCase		:					jnp.dtype

							@property
							def     snake_case       (  self			):
										return True

							@register_to_config
							def __init__(  self					,     __a = 10_00					,     __a = 0.0_0_0_1					,     __a = 0.0_2					,     __a = "linear"					,     __a = None					,     __a = "fixed_small"					,     __a = True					,     __a = "epsilon"					,     __a = jnp.floataa					,     ):
										__lowerCAmelCase       					=    dtype

							def     snake_case       (  self					,     __a = None			):
										if common is None:
													__lowerCAmelCase       					=    CommonSchedulerState.create(self			)

										# standard deviation of the initial noise distribution
										__lowerCAmelCase       					=    jnp.array(1.0					,     dtype=self.dtype			)

										__lowerCAmelCase       					=    jnp.arange(0					,     self.config.num_train_timesteps			).round()[::-1]

										return DDPMSchedulerState.create(
										    common=__a					,     init_noise_sigma=__a					,     timesteps=__a					,     )

							def     snake_case       (  self					,     __a					,     __a					,     __a = None			):
										return sample

							def     snake_case       (  self					,     __a					,     __a					,     __a = ()			):
										__lowerCAmelCase       					=    self.config.num_train_timesteps // num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# rounding to avoid issues when num_inference_step is power of 3
										__lowerCAmelCase       					=    (jnp.arange(0					,     __a			) * step_ratio).round()[::-1]

										return state.replace(
										    num_inference_steps=__a					,     timesteps=__a					,     )

							def     snake_case       (  self					,     __a					,     __a					,     __a=None					,     __a=None			):
										__lowerCAmelCase       					=    state.common.alphas_cumprod[t]
										__lowerCAmelCase       					=    jnp.where(t > 0					,     state.common.alphas_cumprod[t - 1]					,     jnp.array(1.0					,     dtype=self.dtype			)			)

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										__lowerCAmelCase       					=    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

										if variance_type is None:
													__lowerCAmelCase       					=    self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small":
													__lowerCAmelCase       					=    jnp.clip(__a					,     a_min=1e-2_0			)
										# for rl-diffuser https://arxiv.org/abs/2205.09991
										elif variance_type == "fixed_small_log":
													__lowerCAmelCase       					=    jnp.log(jnp.clip(__a					,     a_min=1e-2_0			)			)
										elif variance_type == "fixed_large":
													__lowerCAmelCase       					=    state.common.betas[t]
										elif variance_type == "fixed_large_log":
													# Glide max_log
													__lowerCAmelCase       					=    jnp.log(state.common.betas[t]			)
										elif variance_type == "learned":
													return predicted_variance
										elif variance_type == "learned_range":
													__lowerCAmelCase       					=    variance
													__lowerCAmelCase       					=    state.common.betas[t]
													__lowerCAmelCase       					=    (predicted_variance + 1) / 2
													__lowerCAmelCase       					=    frac * max_log + (1 - frac) * min_log

										return variance

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a = None					,     __a = True					,     ):
										__lowerCAmelCase       					=    timestep

										if key is None:
													__lowerCAmelCase       					=    jax.random.PRNGKey(0			)

										if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
													__lowerCAmelCase					,			__lowerCAmelCase       					=    jnp.split(__a					,     sample.shape[1]					,     axis=1			)
										else:
													__lowerCAmelCase       					=    None

										# 1. compute alphas, betas
										__lowerCAmelCase       					=    state.common.alphas_cumprod[t]
										__lowerCAmelCase       					=    jnp.where(t > 0					,     state.common.alphas_cumprod[t - 1]					,     jnp.array(1.0					,     dtype=self.dtype			)			)
										__lowerCAmelCase       					=    1 - alpha_prod_t
										__lowerCAmelCase       					=    1 - alpha_prod_t_prev

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
													__lowerCAmelCase       					=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
													__lowerCAmelCase       					=    model_output
										elif self.config.prediction_type == "v_prediction":
													__lowerCAmelCase       					=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
										else:
													raise ValueError(
													    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
													    " for the FlaxDDPMScheduler."			)

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
													__lowerCAmelCase       					=    jnp.clip(__a					,     -1					,     1			)

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										__lowerCAmelCase       					=    (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
										__lowerCAmelCase       					=    state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										__lowerCAmelCase       					=    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										def random_variance():
													__lowerCAmelCase       					=    jax.random.split(__a					,     num=1			)
													__lowerCAmelCase       					=    jax.random.normal(__a					,     shape=model_output.shape					,     dtype=self.dtype			)
													return (self._get_variance(__a					,     __a					,     predicted_variance=__a			) ** 0.5) * noise

										__lowerCAmelCase       					=    jnp.where(t > 0					,     random_variance()					,     jnp.zeros(model_output.shape					,     dtype=self.dtype			)			)

										__lowerCAmelCase       					=    pred_prev_sample + variance

										if not return_dict:
													return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=__a					,     state=__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     ):
										return add_noise_common(state.common					,     __a					,     __a					,     __a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     ):
										return get_velocity_common(state.common					,     __a					,     __a					,     __a			)



							def __len__(  self			):
										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

A  :     Union[str, Any]    					=    {
    "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 _lowerCamelCase			(	_UpperCamelCase = "dhaka"					,				_UpperCamelCase = 5   ):

			'''simple docstring'''




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

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

			__lowerCAmelCase       					=    json.dumps(_UpperCamelCase   )
			__lowerCAmelCase       					=    json.loads(_UpperCamelCase   )

			__lowerCAmelCase       					=    re.findall(
			    R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","					,				_UpperCamelCase					,				)
			if not matched_google_image_data:
						return 0

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

			__lowerCAmelCase       					=    re.findall(
			    R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"					,				_UpperCamelCase					,				)
			for index, fixed_full_res_image in enumerate(_UpperCamelCase   ):
						if index >= max_images:
									return index
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"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(_UpperCamelCase   )
						__lowerCAmelCase       					=    f"query_{query.replace(' '					,				'_'   )}"
						if not os.path.exists(_UpperCamelCase   ):
									os.makedirs(_UpperCamelCase   )
						urllib.request.urlretrieve(  # noqa: S310
						    _UpperCamelCase					,				f"{path_name}/original_size_img_{index}.jpg"   )
			return index


if __name__ == "__main__":
	try:
		A  :     Any    					=    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




