



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

from packaging import version

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


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      List[str]         =					"""layoutlmv3"""


							def __init__(							self  ,  a_=50_265  ,  a_=768  ,  a_=12  ,  a_=12  ,  a_=3_072  ,  a_="gelu"  ,  a_=0.1  ,  a_=0.1  ,  a_=512  ,  a_=2  ,  a_=0.02  ,  a_=1E-5  ,  a_=1  ,  a_=0  ,  a_=2  ,  a_=1_024  ,  a_=128  ,  a_=128  ,  a_=True  ,  a_=32  ,  a_=128  ,  a_=64  ,  a_=256  ,  a_=True  ,  a_=True  ,  a_=True  ,  a_=224  ,  a_=3  ,  a_=16  ,  a_=None  ,  **a_  ,  ):
											super().__init__(
											    vocab_size=a_  ,  hidden_size=a_  ,  num_hidden_layers=a_  ,  num_attention_heads=a_  ,  intermediate_size=a_  ,  hidden_act=a_  ,  hidden_dropout_prob=a_  ,  attention_probs_dropout_prob=a_  ,  max_position_embeddings=a_  ,  type_vocab_size=a_  ,  initializer_range=a_  ,  layer_norm_eps=a_  ,  pad_token_id=a_  ,  bos_token_id=a_  ,  eos_token_id=a_  ,  **a_  ,  )
											a__				      =		max_ad_position_embeddings
											a__				      =		coordinate_size
											a__				      =		shape_size
											a__				      =		has_relative_attention_bias
											a__				      =		rel_pos_bins
											a__				      =		max_rel_pos
											a__				      =		has_spatial_attention_bias
											a__				      =		rel_ad_pos_bins
											a__				      =		max_rel_ad_pos
											a__				      =		text_embed
											a__				      =		visual_embed
											a__				      =		input_size
											a__				      =		num_channels
											a__				      =		patch_size
											a__				      =		classifier_dropout




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Union[str, Any]         =					version.parse("""1.12""")


							@property
							def _a      (							self		):
											# The order of inputs is different for question answering and sequence classification
											if self.task in ["question-answering", "sequence-classification"]:
															return OrderedDict(
															    [
															        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
															        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
															        ("""bbox""", {0: """batch""", 1: """sequence"""}),
															        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
															    ]		)
											else:
															return OrderedDict(
															    [
															        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
															        ("""bbox""", {0: """batch""", 1: """sequence"""}),
															        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
															        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
															    ]		)


							@property
							def _a      (							self		):
											return 1E-5


							@property
							def _a      (							self		):
											return 12







							def _a      (							self  ,  a_  ,  a_ = -1  ,  a_ = -1  ,  a_ = False  ,  a_ = None  ,  a_ = 3  ,  a_ = 40  ,  a_ = 40  ,  ):
											setattr(processor.image_processor  ,  """apply_ocr"""  ,  a_		)

											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											a__				      =		compute_effective_axis_dimension(
											    a_  ,  fixed_dimension=OnnxConfig.default_fixed_batch  ,  num_token_to_add=0		)
											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											a__				      =		processor.tokenizer.num_special_tokens_to_add(a_		)
											a__				      =		compute_effective_axis_dimension(
											    a_  ,  fixed_dimension=OnnxConfig.default_fixed_sequence  ,  num_token_to_add=a_		)
											# Generate dummy inputs according to compute batch and sequence
											a__				      =		[[""" """.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

											# Generate dummy bounding boxes
											a__				      =		[[[48, 84, 73, 128]]] * batch_size

											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
											a__				      =		self._generate_dummy_images(a_  ,  a_  ,  a_  ,  a_		)

											a__				      =		dict(
											    processor(
											        a_  ,  text=a_  ,  boxes=a_  ,  return_tensors=a_  ,  )		)

											return inputs






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			A_      (	__a     :				str = "laptop"		):

				"""simple docstring"""
				a__				      =		F'''https://www.amazon.in/laptop/s?k={product}'''
				a__				      =		{
				    """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
				    """Accept-Language""": """en-US, en;q=0.5""",
				}
				a__				      =		BeautifulSoup(requests.get(__a ,		headers=__a		).text		)
				# Initialize a Pandas dataframe with the column titles
				a__				      =		DataFrame(
				    columns=[
				        """Product Title""",
				        """Product Link""",
				        """Current Price of the product""",
				        """Product Rating""",
				        """MRP of the product""",
				        """Discount""",
				    ]		)
				# Loop through each entry and store them in the dataframe
				for item, _ in zip_longest(
				    soup.find_all(
				        """div""" ,		attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""} ,		) ,		soup.find_all("""div""" ,		attrs={"""class""": """a-row a-size-base a-color-base"""}		) ,		):
								try:
												a__				      =		item.ha.text
												a__				      =		"""https://www.amazon.in/""" + item.ha.a["""href"""]
												a__				      =		item.find("""span""" ,		attrs={"""class""": """a-offscreen"""}		).text
												try:
																a__				      =		item.find("""span""" ,		attrs={"""class""": """a-icon-alt"""}		).text
												except AttributeError:
																a__				      =		"""Not available"""
												try:
																a__				      =		(
																    """₹"""
																    + item.find(
																        """span""" ,		attrs={"""class""": """a-price a-text-price"""}		).text.split("""₹"""		)[1]
																)
												except AttributeError:
																a__				      =		""""""
												try:
																a__				      =		float(
																    (
																        (
																            float(product_mrp.strip("""₹"""		).replace(""",""" ,		""""""		)		)
																            - float(product_price.strip("""₹"""		).replace(""",""" ,		""""""		)		)
																        )
																        / float(product_mrp.strip("""₹"""		).replace(""",""" ,		""""""		)		)
																    )
																    * 100		)
												except ValueError:
																a__				      =		float("""nan"""		)
								except AttributeError:
												pass
								a__				      =		[
								    product_title,
								    product_link,
								    product_price,
								    product_rating,
								    product_mrp,
								    discount,
								]
				a__				      =		""" """
				a__				      =		""" """
				data_frame.index += 1
				return data_frame


if __name__ == "__main__":
	UpperCAmelCase							 =					"""headphones"""
	get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")

