





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

import requests
from bsa import BeautifulSoup

lowercase    : int    	=  {
    """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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase = "dhaka"    ,       lowerCAmelCase = 5					):
       _UpperCamelCase              =						min(lowerCAmelCase    ,       50					)  # Prevent abuse!
       _UpperCamelCase              =						{
           '''q''': query,
           '''tbm''': '''isch''',
           '''hl''': '''en''',
           '''ijn''': '''0''',
       }

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

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

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

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

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


if __name__ == "__main__":
       try:
              lowercase    : str    	=  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













import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard

@slow
@require_flax
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            super().tearDown()
            gc.collect()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxStableDiffusionPipeline.from_pretrained(
                '''stabilityai/stable-diffusion-2''',       revision='''bf16''',       dtype=jnp.bfloataa,       )

            _UpperCamelCase              =						'''A painting of a squirrel eating a burger'''
            _UpperCamelCase              =						jax.device_count()
            _UpperCamelCase              =						num_samples * [prompt]
            _UpperCamelCase              =						sd_pipe.prepare_inputs(A      )

            _UpperCamelCase              =						replicate(A      )
            _UpperCamelCase              =						shard(A      )

            _UpperCamelCase              =						jax.random.PRNGKey(0      )
            _UpperCamelCase              =						jax.random.split(A,       jax.device_count()      )

            _UpperCamelCase              =						sd_pipe(A,       A,       A,       num_inference_steps=25,       jit=A      )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

            _UpperCamelCase              =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
            _UpperCamelCase              =						images[0, 253:256, 253:256, -1]

            _UpperCamelCase              =						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
            _UpperCamelCase              =						jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]      )
            print(F'''output_slice: {output_slice}'''      )
            assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''stabilityai/stable-diffusion-2'''
            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxDPMSolverMultistepScheduler.from_pretrained(A,       subfolder='''scheduler'''      )
            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxStableDiffusionPipeline.from_pretrained(
                A,       scheduler=A,       revision='''bf16''',       dtype=jnp.bfloataa,       )
            _UpperCamelCase              =						scheduler_params

            _UpperCamelCase              =						'''A painting of a squirrel eating a burger'''
            _UpperCamelCase              =						jax.device_count()
            _UpperCamelCase              =						num_samples * [prompt]
            _UpperCamelCase              =						sd_pipe.prepare_inputs(A      )

            _UpperCamelCase              =						replicate(A      )
            _UpperCamelCase              =						shard(A      )

            _UpperCamelCase              =						jax.random.PRNGKey(0      )
            _UpperCamelCase              =						jax.random.split(A,       jax.device_count()      )

            _UpperCamelCase              =						sd_pipe(A,       A,       A,       num_inference_steps=25,       jit=A      )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

            _UpperCamelCase              =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
            _UpperCamelCase              =						images[0, 253:256, 253:256, -1]

            _UpperCamelCase              =						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
            _UpperCamelCase              =						jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]      )
            print(F'''output_slice: {output_slice}'''      )
            assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2






