

"""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      __a				:






     '''simple docstring'''

     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	,    )  ->      str:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           if self.use_input_mask:
                 SCREAMING_SNAKE_CASE__   :      Any      		=	random_attention_mask([self.batch_size, self.seq_length]  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__   :      int      		=	config_and_inputs
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{"""input_ids""": input_ids, """attention_mask""": attention_mask}
           return config, inputs_dict

     def 					_a			(						self	,    _a	,    _a	,    _a	,    _a  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	20
           SCREAMING_SNAKE_CASE__   :      int      		=	model_class_name(_a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	model.init_cache(input_ids.shape[0]	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	jnp.ones((input_ids.shape[0], max_decoder_length)	,    dtype="""i4"""  )

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

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

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	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  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	20
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model_class_name(_a  )

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

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

           SCREAMING_SNAKE_CASE__   :      Dict      		=	model(
               input_ids[:, :-1]	,    attention_mask=_a	,    past_key_values=_a	,    position_ids=_a	,    )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,    dtype="""i4"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(
               input_ids[:, -1:]	,    past_key_values=outputs_cache.past_key_values	,    attention_mask=_a	,    position_ids=_a	,    )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	model(_a	,    attention_mask=_a  )

           SCREAMING_SNAKE_CASE__   :      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      __a				(UpperCamelCase_   ,	UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[str]       = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     _SCREAMING_SNAKE_CASE						:Tuple       = (FlaxGPTJForCausalLM,) if is_flax_available() else ()

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	FlaxGPTJModelTester(self  )

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


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(_a	,    _a	,    _a	,    _a  )

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     _a	,    _a	,    _a	,    _a  )

     @tooslow
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	GPTaTokenizer.from_pretrained("""gpt2"""	,    pad_token="""<|endoftext|>"""	,    padding_side="""left"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	tokenizer(["""Hello this is a long string""", """Hey"""]	,    return_tensors="""np"""	,    padding=_a	,    truncation=_a  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	False
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model.config.eos_token_id

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	jax.jit(model.generate  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	jit_generate(
               inputs["""input_ids"""]	,    attention_mask=inputs["""attention_mask"""]	,    pad_token_id=tokenizer.pad_token_id  ).sequences

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer.batch_decode(_a	,    skip_special_tokens=_a  )

           SCREAMING_SNAKE_CASE__   :      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 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[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
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	self._prepare_for_class(_a	,    _a  )
                       SCREAMING_SNAKE_CASE__   :      int      		=	{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	pt_inputs["""input_ids"""].shape
                       SCREAMING_SNAKE_CASE__   :      Any      		=	np.random.randint(0	,    seq_length - 1	,    size=(batch_size,)  )
                       for batch_idx, start_index in enumerate(_a  ):
                             SCREAMING_SNAKE_CASE__   :      Any      		=	0
                             SCREAMING_SNAKE_CASE__   :      Dict      		=	1
                             SCREAMING_SNAKE_CASE__   :      List[str]      		=	0
                             SCREAMING_SNAKE_CASE__   :      Any      		=	1
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	pt_model_class(_a  ).eval()
                       SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model_class(_a	,    dtype=jnp.floataa  )

                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,    _a  )
                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	fx_state

                       with torch.no_grad():
                             SCREAMING_SNAKE_CASE__   :      List[Any]      		=	pt_model(**_a  ).to_tuple()

                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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  )
                             SCREAMING_SNAKE_CASE__   :      str      		=	model_class.from_pretrained(_a	,    from_pt=_a  )

                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	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  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      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
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self._prepare_for_class(_a	,    _a  )
                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

                       SCREAMING_SNAKE_CASE__   :      int      		=	pt_model_class(_a  ).eval()
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model_class(_a	,    dtype=jnp.floataa  )

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

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

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

                       SCREAMING_SNAKE_CASE__   :      Any      		=	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  )
                             SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	pt_model_class.from_pretrained(_a	,    from_flax=_a  )

                       with torch.no_grad():
                             SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""  )
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model(np.ones((1, 1)  )  )
                 self.assertIsNotNone(_a  )







"""simple docstring"""


def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				bool:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							) + 1
      SCREAMING_SNAKE_CASE__   :      int      		=	len(__lowerCAmelCase							) + 1

      # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
      # length i of input_string matches with prefix string of length j of
      # given pattern.
      # "dp" stands for dynamic programming.
      SCREAMING_SNAKE_CASE__   :      Dict      		=	[[0 for i in range(__lowerCAmelCase							)] for j in range(__lowerCAmelCase							)]

      # since string of zero length match pattern of zero length
      SCREAMING_SNAKE_CASE__   :      Dict      		=	1

      # since pattern of zero length will never match with string of non-zero length
      for i in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	0

      # since string of zero length will match with pattern where there
      # is at least one * alternatively
      for j in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      int      		=	dp[0][j - 2] if pattern[j - 1] == """*""" else 0

      # now using bottom-up approach to find for all remaining lengths
      for i in range(1  ,					__lowerCAmelCase							):
            for j in range(1  ,					__lowerCAmelCase							):
                  if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                        SCREAMING_SNAKE_CASE__   :      Any      		=	dp[i - 1][j - 1]

                  elif pattern[j - 1] == "*":
                        if dp[i][j - 2] == 1:
                              SCREAMING_SNAKE_CASE__   :      List[str]      		=	1
                        elif pattern[j - 2] in (input_string[i - 1], "."):
                              SCREAMING_SNAKE_CASE__   :      List[Any]      		=	dp[i - 1][j]
                        else:
                              SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0
                  else:
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	0

      return bool(dp[-1][-1]							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # inputing the strings
  # input_string = input("input a string :")
  # pattern = input("input a pattern :")

  a					:Any    = "aab"
  a					:Optional[Any]    = "c*a*b"

  # using function to check whether given string matches the given pattern
  if match_pattern(input_string, pattern):
    print(f'{input_string} matches the given pattern {pattern}')
  else:
    print(f'{input_string} does not match with the given pattern {pattern}')




