



'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch






class __A      :



 '''simple docstring'''







 def __init__(self			,       A			,       A=14			,       A=7			,       A=True			,       A=True			,       A=False			,       A=True			,       A=99			,       A=32			,       A=5			,       A=4			,       A=37			,       A="gelu"			,       A=0.1			,       A=0.1			,       A=512			,       A=0.02			,       )   ->					str:


      """simple docstring"""



      _a     =  parent
      _a     =  batch_size
      _a     =  seq_length
      _a     =  is_training
      _a     =  use_input_mask
      _a     =  use_token_type_ids
      _a     =  use_labels
      _a     =  vocab_size
      _a     =  hidden_size
      _a     =  num_hidden_layers
      _a     =  num_attention_heads
      _a     =  intermediate_size
      _a     =  hidden_act
      _a     =  hidden_dropout_prob
      _a     =  attention_probs_dropout_prob
      _a     =  max_position_embeddings
      _a     =  initializer_range
      _a     =  None
      _a     =  vocab_size - 1
      _a     =  vocab_size - 1
      _a     =  vocab_size - 1



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  ids_tensor([self.batch_size, self.seq_length]			,       self.vocab_size	)

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

      _a     =  GPTaConfig(
          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			,       )

      return (config, input_ids, input_mask)



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  self.prepare_config_and_inputs()
      _a  ,   _a  ,   _a     =  config_and_inputs
      _a     =  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a  ,   _a  ,   _a     =  self.prepare_config_and_inputs()

      _a     =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
      _a     =  ids_tensor([self.batch_size, self.seq_length]			,       vocab_size=2	)

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )



 def  a__  (self			,       A			,       A			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



      _a     =  20
      _a     =  model_class_name(A	)

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

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

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

      _a     =  model(A	)

      _a     =  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  a__  (self			,       A			,       A			,       A			,       A	)   ->					str:


      """simple docstring"""



      _a     =  20
      _a     =  model_class_name(A	)

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

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

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

      _a     =  model(A			,       attention_mask=A	)

      _a     =  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 __A      (  A       ,       A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	str										=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
 __lowerCamelCase   :	Optional[Any]										=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      _a     =  FlaxGPTaModelTester(self	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a  ,   _a  ,   _a     =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(A			,       A			,       A			,       A	)



 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a  ,   _a  ,   _a     =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               A			,       A			,       A			,       A	)



 @slow
 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



      _a     =  GPTaTokenizer.from_pretrained('''gpt2'''			,       pad_token='''</s>'''			,       padding_side='''left'''	)
      _a     =  tokenizer(['''Hello this is a long string''', '''Hey''']			,       return_tensors='''np'''			,       padding=A			,       truncation=A	)

      _a     =  FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''	)
      _a     =  False
      _a     =  model.config.eos_token_id

      _a     =  jax.jit(model.generate	)

      _a     =  jit_generate(inputs['''input_ids''']			,       attention_mask=inputs['''attention_mask''']	).sequences

      _a     =  tokenizer.batch_decode(A			,       skip_special_tokens=A	)

      _a     =  [
          '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
          '''Hey, I\'m not sure if I\'m going to be able to do''',
      ]

      self.assertListEqual(A			,       A	)



 @is_pt_flax_cross_test
 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a  ,   _a     =  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
                _a     =  self._prepare_for_class(A			,       A	)
                _a     =  {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

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

                _a     =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,       A	)
                _a     =  fx_state

                with torch.no_grad():
                     _a     =  pt_model(**A	).to_tuple()

                _a     =  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	)
                     _a     =  model_class.from_pretrained(A			,       from_pt=A	)

                _a     =  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  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      _a  ,   _a     =  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
                _a     =  self._prepare_for_class(A			,       A	)
                _a     =  {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

                _a     =  pt_model_class(A	).eval()
                _a     =  model_class(A			,       dtype=jnp.floataa	)

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

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

                with torch.no_grad():
                     _a     =  pt_model(**A	).to_tuple()

                _a     =  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	)
                     _a     =  pt_model_class.from_pretrained(A			,       from_flax=A	)

                with torch.no_grad():
                     _a     =  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	)



 @slow
 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a     =  model_class_name.from_pretrained('''gpt2'''			,       from_pt=A	)
           _a     =  model(np.ones((1, 1)	)	)
           self.assertIsNotNone(A	)








'''simple docstring'''





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



