



from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor





class  lowerCamelCase__	:
     """simple docstring"""



     def __init__(self					,      __a					,      __a=13					,      __a=30					,      __a=2					,      __a=3					,      __a=True					,      __a=True					,      __a=32					,      __a=2					,      __a=4					,      __a=37					,      __a="gelu"					,      __a=0.1					,      __a=0.1					,      __a=10					,      __a=0.02					,      __a=3					,      __a=None					,      ):






           '''simple docstring'''



           lowerCamelCase             =		parent
           lowerCamelCase             =		batch_size
           lowerCamelCase             =		image_size
           lowerCamelCase             =		patch_size
           lowerCamelCase             =		num_channels
           lowerCamelCase             =		is_training
           lowerCamelCase             =		use_labels
           lowerCamelCase             =		hidden_size
           lowerCamelCase             =		num_hidden_layers
           lowerCamelCase             =		num_attention_heads
           lowerCamelCase             =		intermediate_size
           lowerCamelCase             =		hidden_act
           lowerCamelCase             =		hidden_dropout_prob
           lowerCamelCase             =		attention_probs_dropout_prob
           lowerCamelCase             =		type_sequence_label_size
           lowerCamelCase             =		initializer_range
           lowerCamelCase             =		scope

           # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           lowerCamelCase             =		(image_size // patch_size) ** 2
           lowerCamelCase             =		num_patches + 1







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

           lowerCamelCase             =		None
           if self.use_labels:
                 lowerCamelCase             =		ids_tensor([self.batch_size]					,      self.type_sequence_label_size     )

           lowerCamelCase             =		self.get_config()

           return config, pixel_values, labels







     def     _a   (self     ):






           '''simple docstring'''



           return ViTConfig(
               image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      is_decoder=__a					,      initializer_range=self.initializer_range					,      )







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		TFViTModel(config=__a     )
           lowerCamelCase             =		model(__a					,      training=__a     )
           self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)     )

           # Test with an image with different size than the one specified in config.
           lowerCamelCase             =		self.image_size // 2
           lowerCamelCase             =		pixel_values[:, :, :image_size, :image_size]
           lowerCamelCase             =		model(__a					,      interpolate_pos_encoding=__a					,      training=__a     )
           lowerCamelCase             =		(image_size // self.patch_size) ** 2 + 1
           self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, seq_length, self.hidden_size)     )







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.type_sequence_label_size
           lowerCamelCase             =		TFViTForImageClassification(__a     )
           lowerCamelCase             =		model(__a					,      labels=__a					,      training=__a     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)     )

           # Test with an image with different size than the one specified in config.
           lowerCamelCase             =		self.image_size // 2
           lowerCamelCase             =		pixel_values[:, :, :image_size, :image_size]
           lowerCamelCase             =		model(__a					,      interpolate_pos_encoding=__a					,      training=__a     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)     )

           # test greyscale images
           lowerCamelCase             =		1
           lowerCamelCase             =		TFViTForImageClassification(__a     )
           lowerCamelCase             =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
           lowerCamelCase             =		model(__a     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.prepare_config_and_inputs()
           lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		config_and_inputs
           lowerCamelCase             =		{"pixel_values": pixel_values}
           return config, inputs_dict





@require_tf
class  lowerCamelCase__	(    UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     _A      =      (
         {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     _A      =      False
     _A      =      False
     _A      =      False







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		TFViTModelTester(self     )
           lowerCamelCase             =		ConfigTester(self					,      config_class=__a					,      has_text_modality=__a					,      hidden_size=37     )







     def     _a   (self     ):






           '''simple docstring'''



           self.config_tester.run_common_tests()







     @unittest.skip(reason="ViT does not use inputs_embeds"     )
     def     _a   (self     ):






           '''simple docstring'''



           pass







     @unittest.skip(reason="ViT does not use inputs_embeds"     )
     def     _a   (self     ):






           '''simple docstring'''



           pass







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase             =		model_class(__a     )
                 self.assertIsInstance(model.get_input_embeddings()					,      (tf.keras.layers.Layer)     )
                 lowerCamelCase             =		model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__a					,      tf.keras.layers.Layer     )     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase             =		model_class(__a     )
                 lowerCamelCase             =		inspect.signature(model.call     )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase             =		[*signature.parameters.keys()]

                 lowerCamelCase             =		["pixel_values"]
                 self.assertListEqual(arg_names[:1]					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__a     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		TFViTModel.from_pretrained("google/vit-base-patch16-224"     )
           self.assertIsNotNone(__a     )



def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
      return image






@require_tf
@require_vision
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     @cached_property
     def     _a   (self     ):






           '''simple docstring'''



           return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"     ) if is_vision_available() else None







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"     )

           lowerCamelCase             =		self.default_image_processor
           lowerCamelCase             =		prepare_img()
           lowerCamelCase             =		image_processor(images=__a					,      return_tensors="tf"     )

           # forward pass
           lowerCamelCase             =		model(**__a     )

           # verify the logits
           lowerCamelCase             =		tf.TensorShape((1, 10_00)     )
           self.assertEqual(outputs.logits.shape					,      __a     )

           lowerCamelCase             =		tf.constant([-0.2744, 0.8215, -0.0836]     )

           tf.debugging.assert_near(outputs.logits[0, :3]					,      __a					,      atol=1E-4     )




from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def      __lowercase(						UpperCAmelCase__ = "laptop"			):


      """simple docstring"""



      lowerCamelCase             =		F"""https://www.amazon.in/laptop/s?k={product}"""
      lowerCamelCase             =		{
          "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",
      }
      lowerCamelCase             =		BeautifulSoup(requests.get(UpperCAmelCase__ , headers=UpperCAmelCase__			).text			)
      # Initialize a Pandas dataframe with the column titles
      lowerCamelCase             =		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:
                  lowerCamelCase             =		item.ha.text
                  lowerCamelCase             =		"https://www.amazon.in/" + item.ha.a["href"]
                  lowerCamelCase             =		item.find("span" , attrs={"class": "a-offscreen"}			).text
                  try:
                        lowerCamelCase             =		item.find("span" , attrs={"class": "a-icon-alt"}			).text
                  except AttributeError:
                        lowerCamelCase             =		"Not available"
                  try:
                        lowerCamelCase             =		(
                            "₹"
                            + item.find(
                                "span" , attrs={"class": "a-price a-text-price"}			).text.split("₹"			)[1]
                        )
                  except AttributeError:
                        lowerCamelCase             =		""
                  try:
                        lowerCamelCase             =		float(
                            (
                                (
                                    float(product_mrp.strip("₹"			).replace("," , ""			)			)
                                    - float(product_price.strip("₹"			).replace("," , ""			)			)
                                )
                                / float(product_mrp.strip("₹"			).replace("," , ""			)			)
                            )
                            * 100			)
                  except ValueError:
                        lowerCamelCase             =		float("nan"			)
            except AttributeError:
                  pass
            lowerCamelCase             =		[
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      lowerCamelCase             =		" "
      lowerCamelCase             =		" "
      data_frame.index += 1
      return data_frame


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