



from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


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=0.6					,      __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             =		mask_ratio
           lowerCamelCase             =		scope

           # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
           # (we add 1 for the [CLS] token)
           lowerCamelCase             =		(image_size // patch_size) ** 2
           lowerCamelCase             =		int(math.ceil((1 - mask_ratio) * (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 ViTMAEConfig(
               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					,      decoder_hidden_size=self.hidden_size					,      decoder_num_hidden_layers=self.num_hidden_layers					,      decoder_num_attention_heads=self.num_attention_heads					,      decoder_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					,      mask_ratio=self.mask_ratio					,      )







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






           '''simple docstring'''



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







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






           '''simple docstring'''



           lowerCamelCase             =		TFViTMAEForPreTraining(__a     )
           lowerCamelCase             =		model(__a					,      training=__a     )
           # expected sequence length = num_patches
           lowerCamelCase             =		(self.image_size // self.patch_size) ** 2
           lowerCamelCase             =		self.patch_size**2 * self.num_channels
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)     )

           # test greyscale images
           lowerCamelCase             =		1
           lowerCamelCase             =		TFViTMAEForPreTraining(__a     )

           lowerCamelCase             =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
           lowerCamelCase             =		model(__a					,      training=__a     )
           lowerCamelCase             =		self.patch_size**2
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)     )







     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      =      (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     _A      =      {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

     _A      =      False
     _A      =      False
     _A      =      False
     _A      =      False







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		TFViTMAEModelTester(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="ViTMAE 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_pretraining(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           np.random.seed(2     )

           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase             =		int((config.image_size // config.patch_size) ** 2     )
           lowerCamelCase             =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

           for model_class in self.all_model_classes:
                 lowerCamelCase             =		model_class(__a     )
                 lowerCamelCase             =		self._prepare_for_class(__a					,      __a     )

                 lowerCamelCase             =		model(__a					,      noise=__a     )

                 lowerCamelCase             =		copy.deepcopy(self._prepare_for_class(__a					,      __a     )     )
                 lowerCamelCase             =		model(**__a					,      noise=__a     )
                 lowerCamelCase             =		outputs_dict[0].numpy()
                 lowerCamelCase             =		outputs_keywords[0].numpy()

                 self.assertLess(np.sum(np.abs(output_dict - output_keywords     )     )					,      1E-6     )







     def     _a   (self     ):






           '''simple docstring'''



           np.random.seed(2     )

           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase             =		int((config.image_size // config.patch_size) ** 2     )
           lowerCamelCase             =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

           def prepare_numpy_arrays(__a     ):
                 lowerCamelCase             =		{}
                 for k, v in inputs_dict.items():
                       if tf.is_tensor(__a     ):
                             lowerCamelCase             =		v.numpy()
                       else:
                             lowerCamelCase             =		np.array(__a     )

                 return inputs_np_dict

           for model_class in self.all_model_classes:
                 lowerCamelCase             =		model_class(__a     )

                 lowerCamelCase             =		self._prepare_for_class(__a					,      __a     )
                 lowerCamelCase             =		prepare_numpy_arrays(__a     )

                 lowerCamelCase             =		model(__a					,      noise=__a     )
                 lowerCamelCase             =		model(**__a					,      noise=__a     )
                 self.assert_outputs_same(__a					,      __a     )







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






           '''simple docstring'''



           np.random.seed(2     )

           lowerCamelCase             =		int((tf_model.config.image_size // tf_model.config.patch_size) ** 2     )
           lowerCamelCase             =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
           lowerCamelCase             =		tf.constant(__a     )

           # Add `noise` argument.
           # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
           lowerCamelCase             =		tf_noise

           super().check_pt_tf_models(__a					,      __a					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           np.random.seed(2     )

           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase             =		{
               module_member
               for model_class in self.all_model_classes
               for module in (import_module(model_class.__module__     ),)
               for module_member_name in dir(__a     )
               if module_member_name.endswith("MainLayer"     )
               # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
               and module_member_name[: -len("MainLayer"     )] == model_class.__name__[: -len("Model"     )]
               for module_member in (getattr(__a					,      __a     ),)
               if isinstance(__a					,      __a     )
               and tf.keras.layers.Layer in module_member.__bases__
               and getattr(__a					,      "_keras_serializable"					,      __a     )
           }

           lowerCamelCase             =		int((config.image_size // config.patch_size) ** 2     )
           lowerCamelCase             =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
           lowerCamelCase             =		tf.convert_to_tensor(__a     )
           inputs_dict.update({"noise": noise}     )

           for main_layer_class in tf_main_layer_classes:
                 lowerCamelCase             =		main_layer_class(__a     )

                 lowerCamelCase             =		{
                     name: tf.keras.Input(tensor.shape[1:]					,      dtype=tensor.dtype     ) for name, tensor in inputs_dict.items()
                 }

                 lowerCamelCase             =		tf.keras.Model(__a					,      outputs=main_layer(__a     )     )
                 lowerCamelCase             =		model(__a     )

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       lowerCamelCase             =		os.path.join(__a					,      "keras_model.h5"     )
                       model.save(__a     )
                       lowerCamelCase             =		tf.keras.models.load_model(
                           __a					,      custom_objects={main_layer_class.__name__: main_layer_class}     )
                       assert isinstance(__a					,      tf.keras.Model     )
                       lowerCamelCase             =		model(__a     )
                       self.assert_outputs_same(__a					,      __a     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           np.random.seed(2     )

           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase             =		int((config.image_size // config.patch_size) ** 2     )
           lowerCamelCase             =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

           for model_class in self.all_model_classes:
                 lowerCamelCase             =		model_class(__a     )
                 lowerCamelCase             =		self._prepare_for_class(__a					,      __a     )
                 lowerCamelCase             =		model(__a					,      noise=__a     )

                 if model_class.__name__ == "TFViTMAEModel":
                       lowerCamelCase             =		outputs.last_hidden_state.numpy()
                       lowerCamelCase             =		0
                 else:
                       lowerCamelCase             =		outputs.logits.numpy()
                       lowerCamelCase             =		0

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(__a					,      saved_model=__a     )
                       lowerCamelCase             =		model_class.from_pretrained(__a     )
                       lowerCamelCase             =		model(__a					,      noise=__a     )

                       if model_class.__name__ == "TFViTMAEModel":
                             lowerCamelCase             =		after_outputs["last_hidden_state"].numpy()
                             lowerCamelCase             =		0
                       else:
                             lowerCamelCase             =		after_outputs["logits"].numpy()
                             lowerCamelCase             =		0

                       lowerCamelCase             =		np.amax(np.abs(out_a - out_a     )     )
                       self.assertLessEqual(__a					,      1E-5     )







     def     _a   (self     ):






           '''simple docstring'''



           np.random.seed(2     )

           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase             =		int((config.image_size // config.patch_size) ** 2     )
           lowerCamelCase             =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

           for model_class in self.all_model_classes:
                 lowerCamelCase             =		model_class(__a     )
                 lowerCamelCase             =		self._prepare_for_class(__a					,      __a     )

                 lowerCamelCase             =		model(__a					,      noise=__a     )
                 lowerCamelCase             =		model.get_config()
                 # make sure that returned config is jsonifiable, which is required by keras
                 json.dumps(__a     )
                 lowerCamelCase             =		model_class.from_config(model.get_config()     )
                 # make sure it also accepts a normal config
                 lowerCamelCase             =		model_class.from_config(model.config     )
                 lowerCamelCase             =		new_model(__a     )  # Build model
                 new_model.set_weights(model.get_weights()     )
                 lowerCamelCase             =		new_model(__a					,      noise=__a     )

                 self.assert_outputs_same(__a					,      __a     )







     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."     )
     def     _a   (self     ):






           '''simple docstring'''



           pass







     @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"     )
     def     _a   (self     ):






           '''simple docstring'''



           pass







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		TFViTMAEModel.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("facebook/vit-mae-base"     ) if is_vision_available() else None







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           np.random.seed(2     )

           lowerCamelCase             =		TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"     )

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

           # prepare a noise vector that will be also used for testing the TF model
           # (this way we can ensure that the PT and TF models operate on the same inputs)
           lowerCamelCase             =		ViTMAEConfig()
           lowerCamelCase             =		int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
           lowerCamelCase             =		np.random.uniform(size=(1, num_patches)     )

           # forward pass
           lowerCamelCase             =		model(**__a					,      noise=__a     )

           # verify the logits
           lowerCamelCase             =		tf.convert_to_tensor([1, 1_96, 7_68]     )
           self.assertEqual(outputs.logits.shape					,      __a     )

           lowerCamelCase             =		tf.convert_to_tensor(
               [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]     )

           tf.debugging.assert_near(outputs.logits[0, :3, :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""")