




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Union[str, Any] ,       snake_case					: str ,       snake_case					: str=14 ,       snake_case					: int=7 ,       snake_case					: List[Any]=True ,       snake_case					: List[Any]=True ,       snake_case					: Optional[int]=False ,       snake_case					: Dict=True ,       snake_case					: List[str]=99 ,       snake_case					: Optional[Any]=32 ,       snake_case					: Optional[int]=4 ,       snake_case					: Tuple=4 ,       snake_case					: Optional[Any]=4 ,       snake_case					: List[Any]=37 ,       snake_case					: Dict="gelu" ,       snake_case					: Any=0.1 ,       snake_case					: str=0.1 ,       snake_case					: Any=512 ,       snake_case					: Tuple=0.02 ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						seq_length
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						use_input_mask
          __UpperCamelCase  		=						use_token_type_ids
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						rotary_dim
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						vocab_size - 1
          __UpperCamelCase  		=						vocab_size - 1
          __UpperCamelCase  		=						vocab_size - 1







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size				)

          __UpperCamelCase  		=						None
          if self.use_input_mask:
              __UpperCamelCase  		=						random_attention_mask([self.batch_size, self.seq_length]				)

          __UpperCamelCase  		=						GPTJConfig(
              vocab_size=self.vocab_size ,       n_embd=self.hidden_size ,       n_layer=self.num_hidden_layers ,       n_head=self.num_attention_heads ,       n_positions=self.max_position_embeddings ,       use_cache=snake_case ,       bos_token_id=self.bos_token_id ,       eos_token_id=self.eos_token_id ,       pad_token_id=self.pad_token_id ,       rotary_dim=self.rotary_dim ,       )

          return (config, input_ids, input_mask)







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						config_and_inputs
          __UpperCamelCase  		=						{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict







      def       snake_case	(     self					: Optional[int] ,       snake_case					: int ,       snake_case					: Optional[Any] ,       snake_case					: List[str] ,       snake_case					: str				):
          __UpperCamelCase  		=						20
          __UpperCamelCase  		=						model_class_name(snake_case				)

          __UpperCamelCase  		=						model.init_cache(input_ids.shape[0] ,       snake_case				)
          __UpperCamelCase  		=						jnp.ones((input_ids.shape[0], max_decoder_length) ,       dtype='''i4'''				)

          __UpperCamelCase  		=						jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)				)
          __UpperCamelCase  		=						model(
              input_ids[:, :-1] ,       attention_mask=snake_case ,       past_key_values=snake_case ,       position_ids=snake_case ,       )

          __UpperCamelCase  		=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype='''i4'''				)
          __UpperCamelCase  		=						model(
              input_ids[:, -1:] ,       attention_mask=snake_case ,       past_key_values=outputs_cache.past_key_values ,       position_ids=snake_case ,       )

          __UpperCamelCase  		=						model(snake_case				)

          __UpperCamelCase  		=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
          self.parent.assertTrue(diff < 1E-3 ,       msg=F"Max diff is {diff}"				)







      def       snake_case	(     self					: List[str] ,       snake_case					: Any ,       snake_case					: List[Any] ,       snake_case					: Optional[Any] ,       snake_case					: str				):
          __UpperCamelCase  		=						20
          __UpperCamelCase  		=						model_class_name(snake_case				)

          __UpperCamelCase  		=						jnp.concatenate(
              [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)] ,       axis=-1 ,       )

          __UpperCamelCase  		=						model.init_cache(input_ids.shape[0] ,       snake_case				)
          __UpperCamelCase  		=						jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)				)

          __UpperCamelCase  		=						model(
              input_ids[:, :-1] ,       attention_mask=snake_case ,       past_key_values=snake_case ,       position_ids=snake_case ,       )
          __UpperCamelCase  		=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype='''i4'''				)
          __UpperCamelCase  		=						model(
              input_ids[:, -1:] ,       past_key_values=outputs_cache.past_key_values ,       attention_mask=snake_case ,       position_ids=snake_case ,       )

          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case				)

          __UpperCamelCase  		=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
          self.parent.assertTrue(diff < 1E-3 ,       msg=F"Max diff is {diff}"				)







@require_flax
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[Any]    =					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__			: Dict    =					(FlaxGPTJForCausalLM,) if is_flax_available() else ()







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						FlaxGPTJModelTester(self				)







      def       snake_case	(     self					: Any				):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(snake_case ,       snake_case ,       snake_case ,       snake_case				)







      def       snake_case	(     self					: Union[str, Any]				):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  snake_case ,       snake_case ,       snake_case ,       snake_case				)







      @tooslow
      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						GPTaTokenizer.from_pretrained('''gpt2''' ,       pad_token='''<|endoftext|>''' ,       padding_side='''left'''				)
          __UpperCamelCase  		=						tokenizer(['''Hello this is a long string''', '''Hey'''] ,       return_tensors='''np''' ,       padding=snake_case ,       truncation=snake_case				)

          __UpperCamelCase  		=						FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''				)
          __UpperCamelCase  		=						False
          __UpperCamelCase  		=						model.config.eos_token_id

          __UpperCamelCase  		=						jax.jit(model.generate				)

          __UpperCamelCase  		=						jit_generate(
              inputs['''input_ids'''] ,       attention_mask=inputs['''attention_mask'''] ,       pad_token_id=tokenizer.pad_token_id				).sequences

          __UpperCamelCase  		=						tokenizer.batch_decode(snake_case ,       skip_special_tokens=snake_case				)

          __UpperCamelCase  		=						[
              '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
              '''Hey, I\'m a little late to the party. I\'m going to''',
          ]

          self.assertListEqual(snake_case ,       snake_case				)







      @is_pt_flax_cross_test
      def       snake_case	(     self					: Any				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__				):
                  # prepare inputs
                  __UpperCamelCase  		=						self._prepare_for_class(snake_case ,       snake_case				)
                  __UpperCamelCase  		=						{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  __UpperCamelCase  		=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  __UpperCamelCase  		=						getattr(snake_case ,       snake_case				)

                  __UpperCamelCase		,      __UpperCamelCase  		=						pt_inputs['''input_ids'''].shape
                  __UpperCamelCase  		=						np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)				)
                  for batch_idx, start_index in enumerate(snake_case				):
                      __UpperCamelCase  		=						0
                      __UpperCamelCase  		=						1
                      __UpperCamelCase  		=						0
                      __UpperCamelCase  		=						1
                  __UpperCamelCase  		=						pt_model_class(snake_case				).eval()
                  __UpperCamelCase  		=						model_class(snake_case ,       dtype=jnp.floataa				)

                  __UpperCamelCase  		=						convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,       snake_case				)
                  __UpperCamelCase  		=						fx_state

                  with torch.no_grad():
                      __UpperCamelCase  		=						pt_model(**snake_case				).to_tuple()

                  __UpperCamelCase  		=						fx_model(**snake_case				).to_tuple()
                  self.assertEqual(len(snake_case				) ,       len(snake_case				) ,       '''Output lengths differ between Flax and PyTorch'''				)
                  for fx_output, pt_output in zip(snake_case ,       snake_case				):
                      self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2				)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      pt_model.save_pretrained(snake_case				)
                      __UpperCamelCase  		=						model_class.from_pretrained(snake_case ,       from_pt=snake_case				)

                  __UpperCamelCase  		=						fx_model_loaded(**snake_case				).to_tuple()
                  self.assertEqual(
                      len(snake_case				) ,       len(snake_case				) ,       '''Output lengths differ between Flax and PyTorch'''				)
                  for fx_output_loaded, pt_output in zip(snake_case ,       snake_case				):
                      self.assert_almost_equals(fx_output_loaded[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2				)







      @is_pt_flax_cross_test
      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__				):
                  # prepare inputs
                  __UpperCamelCase  		=						self._prepare_for_class(snake_case ,       snake_case				)
                  __UpperCamelCase  		=						{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  __UpperCamelCase  		=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  __UpperCamelCase  		=						getattr(snake_case ,       snake_case				)

                  __UpperCamelCase  		=						pt_model_class(snake_case				).eval()
                  __UpperCamelCase  		=						model_class(snake_case ,       dtype=jnp.floataa				)

                  __UpperCamelCase  		=						load_flax_weights_in_pytorch_model(snake_case ,       fx_model.params				)
                  __UpperCamelCase		,      __UpperCamelCase  		=						pt_inputs['''input_ids'''].shape
                  __UpperCamelCase  		=						np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)				)
                  for batch_idx, start_index in enumerate(snake_case				):
                      __UpperCamelCase  		=						0
                      __UpperCamelCase  		=						1
                      __UpperCamelCase  		=						0
                      __UpperCamelCase  		=						1

                  # make sure weights are tied in PyTorch
                  pt_model.tie_weights()

                  with torch.no_grad():
                      __UpperCamelCase  		=						pt_model(**snake_case				).to_tuple()

                  __UpperCamelCase  		=						fx_model(**snake_case				).to_tuple()
                  self.assertEqual(len(snake_case				) ,       len(snake_case				) ,       '''Output lengths differ between Flax and PyTorch'''				)
                  for fx_output, pt_output in zip(snake_case ,       snake_case				):
                      self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2				)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      fx_model.save_pretrained(snake_case				)
                      __UpperCamelCase  		=						pt_model_class.from_pretrained(snake_case ,       from_flax=snake_case				)

                  with torch.no_grad():
                      __UpperCamelCase  		=						pt_model_loaded(**snake_case				).to_tuple()

                  self.assertEqual(
                      len(snake_case				) ,       len(snake_case				) ,       '''Output lengths differ between Flax and PyTorch'''				)
                  for fx_output, pt_output in zip(snake_case ,       snake_case				):
                      self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2				)







      @tooslow
      def       snake_case	(     self					: Optional[Any]				):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase  		=						model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''				)
              __UpperCamelCase  		=						model(np.ones((1, 1)				)				)
              self.assertIsNotNone(snake_case				)












import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
 print("Googling.....")
 a_   		=     "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
 a_   		=     requests.get(url, headers={"UserAgent": UserAgent().random})
 # res.raise_for_status()
 with open("project1a.html", "wb") as out_file:  # only for knowing the class
  for data in res.iter_content(1_0000):
   out_file.write(data)
 a_   		=     BeautifulSoup(res.text, "html.parser")
 a_   		=     list(soup.select(".eZt8xd"))[:5]

 print(len(links))
 for link in links:
  if link.text == "Maps":
   webbrowser.open(link.get("href"))
  else:
   webbrowser.open(f"""https://google.com{link.get('href')}""")






