



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
	import os

	import jax.numpy as jnp
	from jax import jit

	from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
	from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

	UpperCAmelCase							 =					"""0.12"""  # assumed parallelism: 8


if is_torch_available():
	import torch

def 			A_      (	__a     :				Any ,		__a     :				Dict ,		__a     :				Optional[int]=None		):

				"""simple docstring"""
				if rng is None:
								a__				      =		random.Random()

				a__				      =		1
				for dim in shape:
								total_dims *= dim

				a__				      =		[]
				for _ in range(__a		):
								values.append(rng.randint(0 ,		vocab_size - 1		)		)

				a__				      =		np.array(__a ,		dtype=jnp.intaa		).reshape(__a		)

				return output

def 			A_      (	__a     :				Tuple ,		__a     :				List[str]=None		):

				"""simple docstring"""
				a__				      =		ids_tensor(__a ,		vocab_size=2 ,		rng=__a		)
				# make sure that at least one token is attended to for each batch
				a__				      =		1
				return attn_mask

@require_flax
class  __snake_case      :







							'''simple docstring'''






							UpperCamelCase__				:      Any         =					None
							UpperCamelCase__				:      int         =					()


							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()

											# cut to half length & take max batch_size 3
											a__				      =		2
											a__				      =		inputs["""input_ids"""].shape[-1] // 2
											a__				      =		inputs["""input_ids"""][:max_batch_size, :sequence_length]

											a__				      =		jnp.ones_like(a_		)
											a__				      =		attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											a__				      =		input_ids.shape[-1] + 5
											if config.eos_token_id is not None and config.pad_token_id is None:
															# hack to allow generate for models such as GPT2 as is done in `generate()`
															a__				      =		config.eos_token_id
											return config, input_ids, attention_mask, max_length


							@is_pt_flax_cross_test
							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		False
											a__				      =		max_length
											a__				      =		0

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a__				      =		getattr(a_  ,  a_		)
															a__				      =		pt_model_class(a_		).eval()
															a__				      =		load_flax_weights_in_pytorch_model(a_  ,  flax_model.params		)

															a__				      =		flax_model.generate(a_		).sequences
															a__				      =		pt_model.generate(torch.tensor(a_  ,  dtype=torch.long		)		)

															if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																			a__				      =		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

															self.assertListEqual(pt_generation_outputs.numpy().tolist()  ,  flax_generation_outputs.tolist()		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		False
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

															self.assertListEqual(generation_outputs.tolist()  ,  jit_generation_outputs.tolist()		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		True
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

															self.assertListEqual(generation_outputs.tolist()  ,  jit_generation_outputs.tolist()		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		False
											a__				      =		max_length
											a__				      =		2

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

															self.assertListEqual(generation_outputs.tolist()  ,  jit_generation_outputs.tolist()		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		False
											a__				      =		max_length
											a__				      =		2
											a__				      =		2

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[0]  ,  input_ids.shape[0] * config.num_return_sequences		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		True
											a__				      =		max_length
											a__				      =		0.8
											a__				      =		10
											a__				      =		0.3
											a__				      =		1
											a__				      =		8
											a__				      =		9

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

															self.assertListEqual(generation_outputs.tolist()  ,  jit_generation_outputs.tolist()		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		max_length
											a__				      =		1
											a__				      =		8
											a__				      =		9

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

															self.assertListEqual(generation_outputs.tolist()  ,  jit_generation_outputs.tolist()		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()
											a__				      =		max_length
											a__				      =		2
											a__				      =		1
											a__				      =		8
											a__				      =		9

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_		).sequences

															self.assertListEqual(generation_outputs.tolist()  ,  jit_generation_outputs.tolist()		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()

											# pad attention mask on the left
											a__				      =		attention_mask.at[(0, 0)].set(0		)

											a__				      =		False
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_  ,  attention_mask=a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_  ,  attention_mask=a_		).sequences

															self.assertListEqual(generation_outputs.tolist()  ,  jit_generation_outputs.tolist()		)


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()

											# pad attention mask on the left
											a__				      =		attention_mask.at[(0, 0)].set(0		)

											a__				      =		True
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_  ,  attention_mask=a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_  ,  attention_mask=a_		).sequences

															self.assertListEqual(generation_outputs.tolist()  ,  jit_generation_outputs.tolist()		)







							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__				      =		self._get_input_ids_and_config()

											# pad attention mask on the left
											a__				      =		attention_mask.at[(0, 0)].set(0		)

											a__				      =		2
											a__				      =		max_length

											for model_class in self.all_generative_model_classes:
															a__				      =		model_class(a_		)

															a__				      =		model.generate(a_  ,  attention_mask=a_		).sequences
															self.assertEqual(generation_outputs.shape[-1]  ,  a_		)

															a__				      =		jit(model.generate		)
															a__				      =		jit_generate(a_  ,  attention_mask=a_		).sequences

															self.assertListEqual(generation_outputs.tolist()  ,  jit_generation_outputs.tolist()		)




@require_flax
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											a__				      =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""		)
											a__				      =		FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""		)

											a__				      =		"""Hello world"""
											a__				      =		tokenizer(a_  ,  return_tensors="""np"""		).input_ids

											# typos are quickly detected (the correct argument is `do_sample`)
											with self.assertRaisesRegex(a_  ,  """do_samples"""		):
															model.generate(a_  ,  do_samples=a_		)

											# arbitrary arguments that will not be used anywhere are also not accepted
											with self.assertRaisesRegex(a_  ,  """foo"""		):
															a__				      =		{"""foo""": """bar"""}
															model.generate(a_  ,  **a_		)






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""")

