
"""simple docstring"""







import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


lowerCamelCase__   					=		get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
lowerCamelCase__   					=		get_tests_dir("""fixtures/vocab.json""")
lowerCamelCase__   					=		get_tests_dir("""fixtures""")




class  A__       (	unittest.TestCase):
		A_						:       Any				   =	['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[str]									=      0

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Any									=      AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )
			self.assertIsInstance(_a       ,		_a )

		def 							__lowerCamelCase							(      self ):
			with tempfile.TemporaryDirectory() as tmpdirname:
				__lowerCAmelCase			:						Tuple									=      WavaVecaConfig()
				__lowerCAmelCase			:						int									=      AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )

				# save in new folder
				model_config.save_pretrained(_a )
				processor.save_pretrained(_a )

				__lowerCAmelCase			:						int									=      AutoProcessor.from_pretrained(_a )

			self.assertIsInstance(_a       ,		_a )

		def 							__lowerCamelCase							(      self ):
			with tempfile.TemporaryDirectory() as tmpdirname:
				# copy relevant files
				copyfile(_a       ,		os.path.join(_a       ,		_a ) )
				copyfile(_a       ,		os.path.join(_a       ,		'vocab.json' ) )

				__lowerCAmelCase			:						List[Any]									=      AutoProcessor.from_pretrained(_a )

			self.assertIsInstance(_a       ,		_a )

		def 							__lowerCamelCase							(      self ):
			with tempfile.TemporaryDirectory() as tmpdirname:
				__lowerCAmelCase			:						Optional[int]									=      WavaVecaFeatureExtractor()
				__lowerCAmelCase			:						Dict									=      AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )

				__lowerCAmelCase			:						Dict									=      WavaVecaProcessor(_a       ,		_a )

				# save in new folder
				processor.save_pretrained(_a )

				# drop `processor_class` in tokenizer
				with open(os.path.join(_a       ,		_a )       ,		'r' ) as f:
					__lowerCAmelCase			:						Optional[Any]									=      json.load(_a )
					config_dict.pop('processor_class' )

				with open(os.path.join(_a       ,		_a )       ,		'w' ) as f:
					f.write(json.dumps(_a ) )

				__lowerCAmelCase			:						Any									=      AutoProcessor.from_pretrained(_a )

			self.assertIsInstance(_a       ,		_a )

		def 							__lowerCamelCase							(      self ):
			with tempfile.TemporaryDirectory() as tmpdirname:
				__lowerCAmelCase			:						Dict									=      WavaVecaFeatureExtractor()
				__lowerCAmelCase			:						Dict									=      AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )

				__lowerCAmelCase			:						List[str]									=      WavaVecaProcessor(_a       ,		_a )

				# save in new folder
				processor.save_pretrained(_a )

				# drop `processor_class` in feature extractor
				with open(os.path.join(_a       ,		_a )       ,		'r' ) as f:
					__lowerCAmelCase			:						List[str]									=      json.load(_a )
					config_dict.pop('processor_class' )

				with open(os.path.join(_a       ,		_a )       ,		'w' ) as f:
					f.write(json.dumps(_a ) )

				__lowerCAmelCase			:						int									=      AutoProcessor.from_pretrained(_a )

			self.assertIsInstance(_a       ,		_a )

		def 							__lowerCamelCase							(      self ):
			with tempfile.TemporaryDirectory() as tmpdirname:
				__lowerCAmelCase			:						int									=      WavaVecaConfig(processor_class='Wav2Vec2Processor' )
				model_config.save_pretrained(_a )
				# copy relevant files
				copyfile(_a       ,		os.path.join(_a       ,		'vocab.json' ) )
				# create emtpy sample processor
				with open(os.path.join(_a       ,		_a )       ,		'w' ) as f:
					f.write('{}' )

				__lowerCAmelCase			:						str									=      AutoProcessor.from_pretrained(_a )

			self.assertIsInstance(_a       ,		_a )

		def 							__lowerCamelCase							(      self ):
			# If remote code is not set, we will time out when asking whether to load the model.
			with self.assertRaises(_a ):
				__lowerCAmelCase			:						Tuple									=      AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
			# If remote code is disabled, we can't load this config.
			with self.assertRaises(_a ):
				__lowerCAmelCase			:						Any									=      AutoProcessor.from_pretrained(
				    'hf-internal-testing/test_dynamic_processor'       ,		trust_remote_code=_a )

			__lowerCAmelCase			:						Any									=      AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'       ,		trust_remote_code=_a )
			self.assertTrue(processor.special_attribute_present )
			self.assertEqual(processor.__class__.__name__       ,		'NewProcessor' )

			__lowerCAmelCase			:						str									=      processor.feature_extractor
			self.assertTrue(feature_extractor.special_attribute_present )
			self.assertEqual(feature_extractor.__class__.__name__       ,		'NewFeatureExtractor' )

			__lowerCAmelCase			:						Dict									=      processor.tokenizer
			self.assertTrue(tokenizer.special_attribute_present )
			if is_tokenizers_available():
				self.assertEqual(tokenizer.__class__.__name__       ,		'NewTokenizerFast' )

				# Test we can also load the slow version
				__lowerCAmelCase			:						str									=      AutoProcessor.from_pretrained(
				    'hf-internal-testing/test_dynamic_processor'       ,		trust_remote_code=_a       ,		use_fast=_a )
				__lowerCAmelCase			:						int									=      new_processor.tokenizer
				self.assertTrue(new_tokenizer.special_attribute_present )
				self.assertEqual(new_tokenizer.__class__.__name__       ,		'NewTokenizer' )
			else:
				self.assertEqual(tokenizer.__class__.__name__       ,		'NewTokenizer' )

		def 							__lowerCamelCase							(      self ):
			try:
				AutoConfig.register('custom'       ,		_a )
				AutoFeatureExtractor.register(_a       ,		_a )
				AutoTokenizer.register(_a       ,		slow_tokenizer_class=_a )
				AutoProcessor.register(_a       ,		_a )
				# Trying to register something existing in the Transformers library will raise an error
				with self.assertRaises(_a ):
					AutoProcessor.register(_a       ,		_a )

				# Now that the config is registered, it can be used as any other config with the auto-API
				__lowerCAmelCase			:						str									=      CustomFeatureExtractor.from_pretrained(_a )

				with tempfile.TemporaryDirectory() as tmp_dir:
					__lowerCAmelCase			:						Dict									=      os.path.join(_a       ,		'vocab.txt' )
					with open(_a       ,		'w'       ,		encoding='utf-8' ) as vocab_writer:
						vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
					__lowerCAmelCase			:						int									=      CustomTokenizer(_a )

				__lowerCAmelCase			:						Optional[Any]									=      CustomProcessor(_a       ,		_a )

				with tempfile.TemporaryDirectory() as tmp_dir:
					processor.save_pretrained(_a )
					__lowerCAmelCase			:						Optional[Any]									=      AutoProcessor.from_pretrained(_a )
					self.assertIsInstance(_a       ,		_a )

			finally:
				if "custom" in CONFIG_MAPPING._extra_content:
					del CONFIG_MAPPING._extra_content["custom"]
				if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
					del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
				if CustomConfig in TOKENIZER_MAPPING._extra_content:
					del TOKENIZER_MAPPING._extra_content[CustomConfig]
				if CustomConfig in PROCESSOR_MAPPING._extra_content:
					del PROCESSOR_MAPPING._extra_content[CustomConfig]

		def 							__lowerCamelCase							(      self ):




			class  A__       (	_lowerCamelCase):
					A_						:       List[Any]				   =	False




			class  A__       (	_lowerCamelCase):
					A_						:       List[str]				   =	False




			class  A__       (	_lowerCamelCase):
					A_						:       str				   =	'AutoFeatureExtractor'
					A_						:       int				   =	'AutoTokenizer'
					A_						:       int				   =	False

			try:
				AutoConfig.register('custom'       ,		_a )
				AutoFeatureExtractor.register(_a       ,		_a )
				AutoTokenizer.register(_a       ,		slow_tokenizer_class=_a )
				AutoProcessor.register(_a       ,		_a )
				# If remote code is not set, the default is to use local classes.
				__lowerCAmelCase			:						Optional[Any]									=      AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
				self.assertEqual(processor.__class__.__name__       ,		'NewProcessor' )
				self.assertFalse(processor.special_attribute_present )
				self.assertFalse(processor.feature_extractor.special_attribute_present )
				self.assertFalse(processor.tokenizer.special_attribute_present )

				# If remote code is disabled, we load the local ones.
				__lowerCAmelCase			:						int									=      AutoProcessor.from_pretrained(
				    'hf-internal-testing/test_dynamic_processor'       ,		trust_remote_code=_a )
				self.assertEqual(processor.__class__.__name__       ,		'NewProcessor' )
				self.assertFalse(processor.special_attribute_present )
				self.assertFalse(processor.feature_extractor.special_attribute_present )
				self.assertFalse(processor.tokenizer.special_attribute_present )

				# If remote is enabled, we load from the Hub.
				__lowerCAmelCase			:						Optional[int]									=      AutoProcessor.from_pretrained(
				    'hf-internal-testing/test_dynamic_processor'       ,		trust_remote_code=_a )
				self.assertEqual(processor.__class__.__name__       ,		'NewProcessor' )
				self.assertTrue(processor.special_attribute_present )
				self.assertTrue(processor.feature_extractor.special_attribute_present )
				self.assertTrue(processor.tokenizer.special_attribute_present )

			finally:
				if "custom" in CONFIG_MAPPING._extra_content:
					del CONFIG_MAPPING._extra_content["custom"]
				if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
					del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
				if CustomConfig in TOKENIZER_MAPPING._extra_content:
					del TOKENIZER_MAPPING._extra_content[CustomConfig]
				if CustomConfig in PROCESSOR_MAPPING._extra_content:
					del PROCESSOR_MAPPING._extra_content[CustomConfig]

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert' )
			self.assertEqual(processor.__class__.__name__       ,		'BertTokenizerFast' )







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext' )
			self.assertEqual(processor.__class__.__name__       ,		'ConvNextImageProcessor' )




@is_staging_test
class  A__       (	unittest.TestCase):
		A_						:       Dict				   =	['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

		@classmethod
		def 							__lowerCamelCase							(      cls ):
			__lowerCAmelCase			:						List[str]									=      TOKEN
			HfFolder.save_token(_a )

		@classmethod
		def 							__lowerCamelCase							(      cls ):
			try:
				delete_repo(token=cls._token       ,		repo_id='test-processor' )
			except HTTPError:
				pass

			try:
				delete_repo(token=cls._token       ,		repo_id='valid_org/test-processor-org' )
			except HTTPError:
				pass

			try:
				delete_repo(token=cls._token       ,		repo_id='test-dynamic-processor' )
			except HTTPError:
				pass

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      WavaVecaProcessor.from_pretrained(_a )
			with tempfile.TemporaryDirectory() as tmp_dir:
				processor.save_pretrained(
				    os.path.join(_a       ,		'test-processor' )       ,		push_to_hub=_a       ,		use_auth_token=self._token )

				__lowerCAmelCase			:						int									=      WavaVecaProcessor.from_pretrained(f"{USER}/test-processor" )
				for k, v in processor.feature_extractor.__dict__.items():
					self.assertEqual(_a       ,		getattr(new_processor.feature_extractor       ,		_a ) )
				self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,		processor.tokenizer.get_vocab() )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      WavaVecaProcessor.from_pretrained(_a )

			with tempfile.TemporaryDirectory() as tmp_dir:
				processor.save_pretrained(
				    os.path.join(_a       ,		'test-processor-org' )       ,		push_to_hub=_a       ,		use_auth_token=self._token       ,		organization='valid_org'       ,		)

				__lowerCAmelCase			:						Dict									=      WavaVecaProcessor.from_pretrained('valid_org/test-processor-org' )
				for k, v in processor.feature_extractor.__dict__.items():
					self.assertEqual(_a       ,		getattr(new_processor.feature_extractor       ,		_a ) )
				self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,		processor.tokenizer.get_vocab() )







		def 							__lowerCamelCase							(      self ):
			CustomFeatureExtractor.register_for_auto_class()
			CustomTokenizer.register_for_auto_class()
			CustomProcessor.register_for_auto_class()

			__lowerCAmelCase			:						str									=      CustomFeatureExtractor.from_pretrained(_a )

			with tempfile.TemporaryDirectory() as tmp_dir:
				__lowerCAmelCase			:						List[str]									=      os.path.join(_a       ,		'vocab.txt' )
				with open(_a       ,		'w'       ,		encoding='utf-8' ) as vocab_writer:
					vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
				__lowerCAmelCase			:						List[Any]									=      CustomTokenizer(_a )

			__lowerCAmelCase			:						Union[str, Any]									=      CustomProcessor(_a       ,		_a )

			with tempfile.TemporaryDirectory() as tmp_dir:
				create_repo(f"{USER}/test-dynamic-processor"       ,		token=self._token )
				__lowerCAmelCase			:						Optional[int]									=      Repository(_a       ,		clone_from=f"{USER}/test-dynamic-processor"       ,		token=self._token )
				processor.save_pretrained(_a )

				# This has added the proper auto_map field to the feature extractor config
				self.assertDictEqual(
				    processor.feature_extractor.auto_map       ,		{
				        'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
				        'AutoProcessor': 'custom_processing.CustomProcessor',
				    }       ,		)

				# This has added the proper auto_map field to the tokenizer config
				with open(os.path.join(_a       ,		'tokenizer_config.json' ) ) as f:
					__lowerCAmelCase			:						Optional[int]									=      json.load(_a )
				self.assertDictEqual(
				    tokenizer_config['auto_map']       ,		{
				        'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
				        'AutoProcessor': 'custom_processing.CustomProcessor',
				    }       ,		)

				# The code has been copied from fixtures
				self.assertTrue(os.path.isfile(os.path.join(_a       ,		'custom_feature_extraction.py' ) ) )
				self.assertTrue(os.path.isfile(os.path.join(_a       ,		'custom_tokenization.py' ) ) )
				self.assertTrue(os.path.isfile(os.path.join(_a       ,		'custom_processing.py' ) ) )

				repo.push_to_hub()

			__lowerCAmelCase			:						List[str]									=      AutoProcessor.from_pretrained(f"{USER}/test-dynamic-processor"       ,		trust_remote_code=_a )
			# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
			self.assertEqual(new_processor.__class__.__name__       ,		'CustomProcessor' )

from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def   lowerCAmelCase_						(					_snake_case      :  str = "laptop"   )				->		DataFrame:







	'''simple docstring'''


	__magic_name__		:		Tuple	   =     F'''https://www.amazon.in/laptop/s?k={product}'''
	__magic_name__		:		Dict	   =     {
	    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
	    "Accept-Language": "en-US, en;q=0.5",
	}
	__magic_name__		:		Tuple	   =     BeautifulSoup(requests.get(_snake_case      ,   headers=_snake_case   ).text   )
	# Initialize a Pandas dataframe with the column titles
	__magic_name__		:		int	   =     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:
			__magic_name__		:		Dict	   =     item.ha.text
			__magic_name__		:		Optional[int]	   =     "https://www.amazon.in/" + item.ha.a["href"]
			__magic_name__		:		Optional[Any]	   =     item.find("span"      ,   attrs={"class": "a-offscreen"}   ).text
			try:
				__magic_name__		:		Union[str, Any]	   =     item.find("span"      ,   attrs={"class": "a-icon-alt"}   ).text
			except AttributeError:
				__magic_name__		:		Dict	   =     "Not available"
			try:
				__magic_name__		:		Optional[int]	   =     (
				    "₹"
				    + item.find(
				        "span"      ,   attrs={"class": "a-price a-text-price"}   ).text.split("₹"   )[1]
				)
			except AttributeError:
				__magic_name__		:		List[str]	   =     ""
			try:
				__magic_name__		:		int	   =     float(
				    (
				        (
				            float(product_mrp.strip("₹"   ).replace(","      ,   ""   )   )
				            - float(product_price.strip("₹"   ).replace(","      ,   ""   )   )
				        )
				        / float(product_mrp.strip("₹"   ).replace(","      ,   ""   )   )
				    )
				    * 100   )
			except ValueError:
				__magic_name__		:		str	   =     float("nan"   )
		except AttributeError:
			pass
		__magic_name__		:		Optional[int]	   =     [
		    product_title,
		    product_link,
		    product_price,
		    product_rating,
		    product_mrp,
		    discount,
		]
	__magic_name__		:		Optional[Any]	   =     " "
	__magic_name__		:		str	   =     " "
	data_frame.index += 1
	return data_frame


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