
"""simple docstring"""




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       __magic_name__							:
       def __init__(  self			,					A_			,					A_=14			,					A_=7			,					A_=True			,					A_=True			,					A_=False			,					A_=True			,					A_=99			,					A_=32			,					A_=4			,					A_=4			,					A_=4			,					A_=37			,					A_="gelu"			,					A_=0.1			,					A_=0.1			,					A_=512			,					A_=0.02			,					)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  parent
         _lowercase:      str   			=  batch_size
         _lowercase:      Dict   			=  seq_length
         _lowercase:      Tuple   			=  is_training
         _lowercase:      Tuple   			=  use_input_mask
         _lowercase:      Dict   			=  use_token_type_ids
         _lowercase:      Tuple   			=  use_labels
         _lowercase:      List[str]   			=  vocab_size
         _lowercase:      List[str]   			=  hidden_size
         _lowercase:      str   			=  rotary_dim
         _lowercase:      Optional[int]   			=  num_hidden_layers
         _lowercase:      str   			=  num_attention_heads
         _lowercase:      Union[str, Any]   			=  intermediate_size
         _lowercase:      List[str]   			=  hidden_act
         _lowercase:      List[str]   			=  hidden_dropout_prob
         _lowercase:      Optional[int]   			=  attention_probs_dropout_prob
         _lowercase:      str   			=  max_position_embeddings
         _lowercase:      Optional[int]   			=  initializer_range
         _lowercase:      Dict   			=  None
         _lowercase:      Tuple   			=  vocab_size - 1
         _lowercase:      str   			=  vocab_size - 1
         _lowercase:      Dict   			=  vocab_size - 1
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Tuple   			=  ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size							)

         _lowercase:      Optional[int]   			=  None
         if self.use_input_mask:
           _lowercase:      Optional[Any]   			=  random_attention_mask([self.batch_size, self.seq_length]							)

         _lowercase:      int   			=  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=A_			,					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 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  self.prepare_config_and_inputs()
         _lowercase			,					_lowercase			,					_lowercase:      Optional[Any]   			=  config_and_inputs
         _lowercase:      Dict   			=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict
       def 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[str]   			=  20
         _lowercase:      List[Any]   			=  model_class_name(A_							)

         _lowercase:      List[Any]   			=  model.init_cache(input_ids.shape[0]			,					A_							)
         _lowercase:      Union[str, Any]   			=  jnp.ones((input_ids.shape[0], max_decoder_length)			,					dtype='''i4'''							)

         _lowercase:      Union[str, Any]   			=  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1							)[None, :]			,					(input_ids.shape[0], input_ids.shape[-1] - 1)							)
         _lowercase:      Tuple   			=  model(
             input_ids[:, :-1]			,					attention_mask=A_			,					past_key_values=A_			,					position_ids=A_			,					)

         _lowercase:      Any   			=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,					dtype='''i4'''							)
         _lowercase:      Optional[int]   			=  model(
             input_ids[:, -1:]			,					attention_mask=A_			,					past_key_values=outputs_cache.past_key_values			,					position_ids=A_			,					)

         _lowercase:      List[str]   			=  model(A_							)

         _lowercase:      Optional[int]   			=  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 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  20
         _lowercase:      Dict   			=  model_class_name(A_							)

         _lowercase:      List[Any]   			=  jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])							)]			,					axis=-1			,					)

         _lowercase:      Dict   			=  model.init_cache(input_ids.shape[0]			,					A_							)
         _lowercase:      Tuple   			=  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1							)[None, :]			,					(input_ids.shape[0], input_ids.shape[-1] - 1)							)

         _lowercase:      Any   			=  model(
             input_ids[:, :-1]			,					attention_mask=A_			,					past_key_values=A_			,					position_ids=A_			,					)
         _lowercase:      Optional[Any]   			=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,					dtype='''i4'''							)
         _lowercase:      Union[str, Any]   			=  model(
             input_ids[:, -1:]			,					past_key_values=outputs_cache.past_key_values			,					attention_mask=A_			,					position_ids=A_			,					)

         _lowercase:      Union[str, Any]   			=  model(A_			,					attention_mask=A_							)

         _lowercase:      List[str]   			=  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       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
       UpperCamelCase_ =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  FlaxGPTJModelTester(self							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         for model_class_name in self.all_model_classes:
           _lowercase			,					_lowercase			,					_lowercase:      List[str]   			=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(A_			,					A_			,					A_			,					A_							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         for model_class_name in self.all_model_classes:
           _lowercase			,					_lowercase			,					_lowercase:      Any   			=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               A_			,					A_			,					A_			,					A_							)
       @tooslow
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  GPTaTokenizer.from_pretrained('''gpt2'''			,					pad_token='''<|endoftext|>'''			,					padding_side='''left'''							)
         _lowercase:      int   			=  tokenizer(['''Hello this is a long string''', '''Hey''']			,					return_tensors='''np'''			,					padding=A_			,					truncation=A_							)

         _lowercase:      Union[str, Any]   			=  FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''							)
         _lowercase:      str   			=  False
         _lowercase:      Union[str, Any]   			=  model.config.eos_token_id

         _lowercase:      Dict   			=  jax.jit(model.generate							)

         _lowercase:      Optional[Any]   			=  jit_generate(
             inputs['''input_ids''']			,					attention_mask=inputs['''attention_mask''']			,					pad_token_id=tokenizer.pad_token_id							).sequences

         _lowercase:      Union[str, Any]   			=  tokenizer.batch_decode(A_			,					skip_special_tokens=A_							)

         _lowercase:      List[str]   			=  [
             '''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(A_			,					A_							)
       @is_pt_flax_cross_test
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase			,					_lowercase:      Tuple   			=  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
             _lowercase:      Union[str, Any]   			=  self._prepare_for_class(A_			,					A_							)
             _lowercase:      Union[str, Any]   			=  {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             _lowercase:      List[Any]   			=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             _lowercase:      Any   			=  getattr(A_			,					A_							)

             _lowercase			,					_lowercase:      int   			=  pt_inputs['''input_ids'''].shape
             _lowercase:      Optional[int]   			=  np.random.randint(0			,					seq_length - 1			,					size=(batch_size,)							)
             for batch_idx, start_index in enumerate(A_							):
               _lowercase:      Union[str, Any]   			=  0
               _lowercase:      Optional[Any]   			=  1
               _lowercase:      Tuple   			=  0
               _lowercase:      Union[str, Any]   			=  1
             _lowercase:      Union[str, Any]   			=  pt_model_class(A_							).eval()
             _lowercase:      Any   			=  model_class(A_			,					dtype=jnp.floataa							)

             _lowercase:      str   			=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,					A_							)
             _lowercase:      List[Any]   			=  fx_state

             with torch.no_grad():
               _lowercase:      Any   			=  pt_model(**A_							).to_tuple()

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

             with tempfile.TemporaryDirectory() as tmpdirname:
               pt_model.save_pretrained(A_							)
               _lowercase:      Any   			=  model_class.from_pretrained(A_			,					from_pt=A_							)

             _lowercase:      Optional[int]   			=  fx_model_loaded(**A_							).to_tuple()
             self.assertEqual(
                 len(A_							)			,					len(A_							)			,					'''Output lengths differ between Flax and PyTorch'''							)
             for fx_output_loaded, pt_output in zip(A_			,					A_							):
               self.assert_almost_equals(fx_output_loaded[:, -1]			,					pt_output[:, -1].numpy()			,					4E-2							)
       @is_pt_flax_cross_test
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase			,					_lowercase:      Any   			=  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
             _lowercase:      Any   			=  self._prepare_for_class(A_			,					A_							)
             _lowercase:      Tuple   			=  {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             _lowercase:      int   			=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             _lowercase:      str   			=  getattr(A_			,					A_							)

             _lowercase:      int   			=  pt_model_class(A_							).eval()
             _lowercase:      Union[str, Any]   			=  model_class(A_			,					dtype=jnp.floataa							)

             _lowercase:      Any   			=  load_flax_weights_in_pytorch_model(A_			,					fx_model.params							)
             _lowercase			,					_lowercase:      List[str]   			=  pt_inputs['''input_ids'''].shape
             _lowercase:      Tuple   			=  np.random.randint(0			,					seq_length - 1			,					size=(batch_size,)							)
             for batch_idx, start_index in enumerate(A_							):
               _lowercase:      int   			=  0
               _lowercase:      int   			=  1
               _lowercase:      Dict   			=  0
               _lowercase:      Tuple   			=  1

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

             with torch.no_grad():
               _lowercase:      Union[str, Any]   			=  pt_model(**A_							).to_tuple()

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

             with tempfile.TemporaryDirectory() as tmpdirname:
               fx_model.save_pretrained(A_							)
               _lowercase:      Optional[int]   			=  pt_model_class.from_pretrained(A_			,					from_flax=A_							)

             with torch.no_grad():
               _lowercase:      str   			=  pt_model_loaded(**A_							).to_tuple()

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



       @tooslow
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         for model_class_name in self.all_model_classes:
           _lowercase:      Union[str, Any]   			=  model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''							)
           _lowercase:      Optional[int]   			=  model(np.ones((1, 1)							)							)
           self.assertIsNotNone(A_							)


"""simple docstring"""




from collections.abc import Callable




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      float   			=  a
  _lowercase:      float   			=  b
  if function(_UpperCamelCase    ) == 0:  # one of the a or b is a root for the function
    return a
  elif function(_UpperCamelCase    ) == 0:
    return b
  elif (
      function(_UpperCamelCase    ) * function(_UpperCamelCase    ) > 0
  ):  # if none of these are root and they are both positive or negative,
    # then this algorithm can't find the root
    raise ValueError('''could not find root in given interval.'''    )
  else:
    _lowercase:      float   			=  start + (end - start) / 2.0
    while abs(start - mid    ) > 10**-7:  # until precisely equals to 10^-7
      if function(_UpperCamelCase    ) == 0:
        return mid
      elif function(_UpperCamelCase    ) * function(_UpperCamelCase    ) < 0:
        _lowercase:      Union[str, Any]   			=  mid
      else:
        _lowercase:      Any   			=  mid
      _lowercase:      List[Any]   			=  start + (end - start) / 2.0
    return mid




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return x**3 - 2 * x - 5


if __name__ == "__main__":
  print(bisection(f, 1, 1_0_0_0))

  import doctest

  doctest.testmod()
