


'''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 				UpperCAmelCase			:


 def __init__(      self							:	Any			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	List[str]=1_4			,       __lowerCamelCase							:	List[str]=7			,       __lowerCamelCase							:	Union[str, Any]=True			,       __lowerCamelCase							:	List[Any]=True			,       __lowerCamelCase							:	Tuple=False			,       __lowerCamelCase							:	Tuple=True			,       __lowerCamelCase							:	Union[str, Any]=9_9			,       __lowerCamelCase							:	Any=3_2			,       __lowerCamelCase							:	Optional[Any]=4			,       __lowerCamelCase							:	Dict=4			,       __lowerCamelCase							:	List[Any]=4			,       __lowerCamelCase							:	Any=3_7			,       __lowerCamelCase							:	Dict="gelu"			,       __lowerCamelCase							:	Dict=0.1			,       __lowerCamelCase							:	Union[str, Any]=0.1			,       __lowerCamelCase							:	Optional[Any]=5_1_2			,       __lowerCamelCase							:	Union[str, Any]=0.02			,       ):
    UpperCAmelCase__	:str      =      parent
    UpperCAmelCase__	:List[str]      =      batch_size
    UpperCAmelCase__	:List[Any]      =      seq_length
    UpperCAmelCase__	:List[str]      =      is_training
    UpperCAmelCase__	:Optional[int]      =      use_input_mask
    UpperCAmelCase__	:List[Any]      =      use_token_type_ids
    UpperCAmelCase__	:int      =      use_labels
    UpperCAmelCase__	:str      =      vocab_size
    UpperCAmelCase__	:List[Any]      =      hidden_size
    UpperCAmelCase__	:List[str]      =      rotary_dim
    UpperCAmelCase__	:Optional[int]      =      num_hidden_layers
    UpperCAmelCase__	:Any      =      num_attention_heads
    UpperCAmelCase__	:Tuple      =      intermediate_size
    UpperCAmelCase__	:Tuple      =      hidden_act
    UpperCAmelCase__	:Tuple      =      hidden_dropout_prob
    UpperCAmelCase__	:str      =      attention_probs_dropout_prob
    UpperCAmelCase__	:Tuple      =      max_position_embeddings
    UpperCAmelCase__	:List[str]      =      initializer_range
    UpperCAmelCase__	:Tuple      =      None
    UpperCAmelCase__	:Union[str, Any]      =      vocab_size - 1
    UpperCAmelCase__	:List[Any]      =      vocab_size - 1
    UpperCAmelCase__	:Union[str, Any]      =      vocab_size - 1


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    UpperCAmelCase__	:Tuple      =      ids_tensor([self.batch_size, self.seq_length]			,       self.vocab_size )

    UpperCAmelCase__	:Any      =      None
    if self.use_input_mask:
       UpperCAmelCase__	:Tuple      =      random_attention_mask([self.batch_size, self.seq_length] )

    UpperCAmelCase__	:Union[str, 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=__lowerCamelCase			,       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        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Union[str, Any]      =      self.prepare_config_and_inputs()
    UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:int      =      config_and_inputs
    UpperCAmelCase__	:int      =      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any ):
    UpperCAmelCase__	:Union[str, Any]      =      2_0
    UpperCAmelCase__	:Any      =      model_class_name(__lowerCamelCase )

    UpperCAmelCase__	:Dict      =      model.init_cache(input_ids.shape[0]			,       __lowerCamelCase )
    UpperCAmelCase__	:Dict      =      jnp.ones((input_ids.shape[0], max_decoder_length)			,       dtype='''i4''' )

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

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

    UpperCAmelCase__	:List[Any]      =      model(__lowerCamelCase )

    UpperCAmelCase__	:Union[str, 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        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	Dict ):
    UpperCAmelCase__	:Tuple      =      2_0
    UpperCAmelCase__	:Tuple      =      model_class_name(__lowerCamelCase )

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

    UpperCAmelCase__	:int      =      model.init_cache(input_ids.shape[0]			,       __lowerCamelCase )
    UpperCAmelCase__	:Union[str, Any]      =      jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1 )[None, :]			,       (input_ids.shape[0], input_ids.shape[-1] - 1) )

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

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase			,       attention_mask=__lowerCamelCase )

    UpperCAmelCase__	: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}''' )




@require_flax
class 				UpperCAmelCase			(			_snake_case						, _snake_case						, unittest.TestCase						):
 UpperCAmelCase				=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 UpperCAmelCase				=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Optional[int]      =      FlaxGPTJModelTester(self )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict ):
    for model_class_name in self.all_model_classes:
       UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:int      =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(__lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    for model_class_name in self.all_model_classes:
       UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           __lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase )


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

    UpperCAmelCase__	:Tuple      =      FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''' )
    UpperCAmelCase__	:Tuple      =      False
    UpperCAmelCase__	:Optional[int]      =      model.config.eos_token_id

    UpperCAmelCase__	:List[str]      =      jax.jit(model.generate )

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

    UpperCAmelCase__	:Dict      =      tokenizer.batch_decode(__lowerCamelCase			,       skip_special_tokens=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      [
        '''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(__lowerCamelCase			,       __lowerCamelCase )


 @is_pt_flax_cross_test
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[int]      =      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__	:str      =      self._prepare_for_class(__lowerCamelCase			,       __lowerCamelCase )
          UpperCAmelCase__	:int      =      {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          UpperCAmelCase__	:Dict      =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
          UpperCAmelCase__	:Tuple      =      getattr(__lowerCamelCase			,       __lowerCamelCase )

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

          UpperCAmelCase__	:Any      =      convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,       __lowerCamelCase )
          UpperCAmelCase__	:Tuple      =      fx_state

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

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

          with tempfile.TemporaryDirectory() as tmpdirname:
             pt_model.save_pretrained(__lowerCamelCase )
             UpperCAmelCase__	:Optional[Any]      =      model_class.from_pretrained(__lowerCamelCase			,       from_pt=__lowerCamelCase )

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


 @is_pt_flax_cross_test
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      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__	:Optional[Any]      =      self._prepare_for_class(__lowerCamelCase			,       __lowerCamelCase )
          UpperCAmelCase__	:Optional[int]      =      {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

          UpperCAmelCase__	:Union[str, Any]      =      pt_model_class(__lowerCamelCase ).eval()
          UpperCAmelCase__	:Tuple      =      model_class(__lowerCamelCase			,       dtype=jnp.floataa )

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

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

          with torch.no_grad():
             UpperCAmelCase__	:List[str]      =      pt_model(**__lowerCamelCase ).to_tuple()

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

          with tempfile.TemporaryDirectory() as tmpdirname:
             fx_model.save_pretrained(__lowerCamelCase )
             UpperCAmelCase__	:List[Any]      =      pt_model_class.from_pretrained(__lowerCamelCase			,       from_flax=__lowerCamelCase )

          with torch.no_grad():
             UpperCAmelCase__	:str      =      pt_model_loaded(**__lowerCamelCase ).to_tuple()

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





 @tooslow
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    for model_class_name in self.all_model_classes:
       UpperCAmelCase__	:str      =      model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''' )
       UpperCAmelCase__	:Any      =      model(np.ones((1, 1) ) )
       self.assertIsNotNone(__lowerCamelCase )







'''simple docstring'''





class 				UpperCAmelCase			:


 def __init__(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:dict[str, TrieNode]      =      {}  # Mapping from char to TrieNode
    UpperCAmelCase__	:Union[str, Any]      =      False


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	list[str] ):
    for word in words:
       self.insert(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Tuple      =      self
    for char in word:
       if char not in curr.nodes:
          UpperCAmelCase__	:Optional[int]      =      TrieNode()
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    UpperCAmelCase__	:Optional[int]      =      True


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:str      =      self
    for char in word:
       if char not in curr.nodes:
          return False
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    return curr.is_leaf





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	str ):

    def _delete(__lowerCamelCase							:	TrieNode			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int ) -> bool:
       if index == len(__lowerCamelCase ):
          # If word does not exist
          if not curr.is_leaf:
             return False
          UpperCAmelCase__	:int      =      False
          return len(curr.nodes ) == 0
       UpperCAmelCase__	:str      =      word[index]
       UpperCAmelCase__	:Optional[Any]      =      curr.nodes.get(__lowerCamelCase )
       # If char not in current trie node
       if not char_node:
          return False
       # Flag to check if node can be deleted
       UpperCAmelCase__	:Any      =      _delete(__lowerCamelCase			,       __lowerCamelCase			,       index + 1 )
       if delete_curr:
          del curr.nodes[char]
          return len(curr.nodes ) == 0
       return delete_curr

    _delete(self			,       __lowerCamelCase			,       0 )


def        a__     (					UpperCamelCase_						:      TrieNode,   UpperCamelCase_						:      str						):
   if node.is_leaf:
      print(UpperCamelCase_,   end=''' '''						)

   for key, value in node.nodes.items():
      print_words(UpperCamelCase_,   word + key						)


def        a__     (					):
   UpperCAmelCase__	:Union[str, Any]      =      '''banana bananas bandana band apple all beast'''.split()
   UpperCAmelCase__	:Union[str, Any]      =      TrieNode()
   root.insert_many(UpperCamelCase_						)
   # print_words(root, "")
   assert all(root.find(UpperCamelCase_						) for word in words						)
   assert root.find('''banana'''						)
   assert not root.find('''bandanas'''						)
   assert not root.find('''apps'''						)
   assert root.find('''apple'''						)
   assert root.find('''all'''						)
   root.delete('''all'''						)
   assert not root.find('''all'''						)
   root.delete('''banana'''						)
   assert not root.find('''banana'''						)
   assert root.find('''bananas'''						)
   return True


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      bool						):
   print(str(UpperCamelCase_						),   '''works!''' if passes else '''doesn\'t work :('''						)


def        a__     (					):
   assert test_trie()


def        a__     (					):
   print_results('''Testing trie functionality''',   test_trie()						)


if __name__ == "__main__":
    main()



