





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, TensorType

A    :					Any =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       str							=    '''imagegpt'''
			__lowerCamelCase	:       Union[str, Any]							=    ['''past_key_values''']
			__lowerCamelCase	:       Optional[int]							=    {
			    '''hidden_size''': '''n_embd''',
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}






			def __init__(			self   :    Any							,	__lowerCAmelCase   :    Optional[Any]=5_12 + 1							,	__lowerCAmelCase   :    Union[str, Any]=32 * 32							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    List[Any]=24							,	__lowerCAmelCase   :    Tuple=8							,	__lowerCAmelCase   :    Optional[Any]=None							,	__lowerCAmelCase   :    Optional[int]="quick_gelu"							,	__lowerCAmelCase   :    int=0.1							,	__lowerCAmelCase   :    Optional[int]=0.1							,	__lowerCAmelCase   :    Optional[int]=0.1							,	__lowerCAmelCase   :    Any=1e-5							,	__lowerCAmelCase   :    List[str]=0.0_2							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Any=False							,	__lowerCAmelCase   :    List[str]=False							,	__lowerCAmelCase   :    int=False							,	**__lowerCAmelCase   :    str							,	)	->	Tuple:







					"""simple docstring"""






					A__        =   vocab_size
					A__        =   n_positions
					A__        =   n_embd
					A__        =   n_layer
					A__        =   n_head
					A__        =   n_inner
					A__        =   activation_function
					A__        =   resid_pdrop
					A__        =   embd_pdrop
					A__        =   attn_pdrop
					A__        =   layer_norm_epsilon
					A__        =   initializer_range
					A__        =   scale_attn_weights
					A__        =   use_cache
					A__        =   scale_attn_by_inverse_layer_idx
					A__        =   reorder_and_upcast_attn
					A__        =   tie_word_embeddings

					super().__init__(tie_word_embeddings=__lowerCAmelCase							,	**__lowerCAmelCase   )


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			@property
			def    a_       (			self   :    Dict   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					return OrderedDict(
					    [
					        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
					    ]   )







			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    "FeatureExtractionMixin"							,	__lowerCAmelCase   :    int = 1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 32							,	__lowerCAmelCase   :    int = 32							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )
					A__        =   dict(preprocessor(images=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase   )   )

					return inputs










from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
					A    :					List[str] =      input('''Enter image url: ''').strip()
					print(F'''Downloading image from {url} ...''')
					A    :					Any =      BeautifulSoup(requests.get(url).content, '''html.parser''')
					# The image URL is in the content field of the first meta tag with property og:image
					A    :					List[Any] =      soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
					A    :					Dict =      requests.get(image_url).content
					A    :					Tuple =      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
					with open(file_name, '''wb''') as fp:
										fp.write(image_data)
					print(F'''Done. Image saved to disk as {file_name}.''')



