

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
      import torch


class     lowerCamelCase_   :




 def __init__( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase=14     ,     _UpperCAmelCase=7     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=99     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=4     ,     _UpperCAmelCase=4     ,     _UpperCAmelCase=[[["global", "local"], 2]]     ,     _UpperCAmelCase=37     ,     _UpperCAmelCase="gelu"     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=7     ,     _UpperCAmelCase=0.0_2     ,     ):
   __UpperCAmelCase  :     Tuple		=    parent
   __UpperCAmelCase  :     List[Any]		=    batch_size
   __UpperCAmelCase  :     int		=    seq_length
   __UpperCAmelCase  :     Any		=    is_training
   __UpperCAmelCase  :     str		=    use_input_mask
   __UpperCAmelCase  :     int		=    use_token_type_ids
   __UpperCAmelCase  :     Tuple		=    use_labels
   __UpperCAmelCase  :     List[Any]		=    vocab_size
   __UpperCAmelCase  :     Dict		=    hidden_size
   __UpperCAmelCase  :     int		=    num_hidden_layers
   __UpperCAmelCase  :     Union[str, Any]		=    num_attention_heads
   __UpperCAmelCase  :     Any		=    attention_types
   __UpperCAmelCase  :     Any		=    intermediate_size
   __UpperCAmelCase  :     Dict		=    hidden_act
   __UpperCAmelCase  :     int		=    hidden_dropout_prob
   __UpperCAmelCase  :     Tuple		=    attention_probs_dropout_prob
   __UpperCAmelCase  :     Union[str, Any]		=    max_position_embeddings
   __UpperCAmelCase  :     Optional[int]		=    window_size
   __UpperCAmelCase  :     List[str]		=    initializer_range
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     Tuple		=    vocab_size - 1
   __UpperCAmelCase  :     Dict		=    vocab_size - 1
   __UpperCAmelCase  :     int		=    vocab_size - 1




 def 						a					( self       ):
   __UpperCAmelCase  :     Any		=    ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size       )

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

   __UpperCAmelCase  :     str		=    GPTNeoConfig(
       vocab_size=self.vocab_size     ,     hidden_size=self.hidden_size     ,     num_layers=self.num_hidden_layers     ,     num_heads=self.num_attention_heads     ,     max_position_embeddings=self.max_position_embeddings     ,     use_cache=_UpperCAmelCase     ,     bos_token_id=self.bos_token_id     ,     eos_token_id=self.eos_token_id     ,     pad_token_id=self.pad_token_id     ,     window_size=self.window_size     ,     attention_types=self.attention_types     ,     )

   return (config, input_ids, input_mask)




 def 						a					( self       ):
   __UpperCAmelCase  :     List[Any]		=    self.prepare_config_and_inputs()
   __UpperCAmelCase  :     Any		=    config_and_inputs
   __UpperCAmelCase  :     Any		=    {'input_ids': input_ids, 'attention_mask': attention_mask}
   return config, inputs_dict




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Union[str, Any]		=    20
   __UpperCAmelCase  :     List[str]		=    model_class_name(_UpperCAmelCase       )

   __UpperCAmelCase  :     Optional[Any]		=    model.init_cache(input_ids.shape[0]     ,     _UpperCAmelCase       )
   __UpperCAmelCase  :     Union[str, Any]		=    jnp.ones((input_ids.shape[0], max_decoder_length)     ,     dtype='i4'       )

   __UpperCAmelCase  :     Dict		=    jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1       )[None, :]     ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )
   __UpperCAmelCase  :     Optional[Any]		=    model(
       input_ids[:, :-1]     ,     attention_mask=_UpperCAmelCase     ,     past_key_values=_UpperCAmelCase     ,     position_ids=_UpperCAmelCase     ,     )

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

   __UpperCAmelCase  :     Union[str, Any]		=    model(_UpperCAmelCase       )

   __UpperCAmelCase  :     List[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     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Tuple		=    20
   __UpperCAmelCase  :     Optional[Any]		=    model_class_name(_UpperCAmelCase       )

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

   __UpperCAmelCase  :     List[Any]		=    model.init_cache(input_ids.shape[0]     ,     _UpperCAmelCase       )
   __UpperCAmelCase  :     Dict		=    jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1       )[None, :]     ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )

   __UpperCAmelCase  :     Tuple		=    model(
       input_ids[:, :-1]     ,     attention_mask=_UpperCAmelCase     ,     past_key_values=_UpperCAmelCase     ,     position_ids=_UpperCAmelCase     ,     )
   __UpperCAmelCase  :     Tuple		=    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=_UpperCAmelCase     ,     position_ids=_UpperCAmelCase     ,     )

   __UpperCAmelCase  :     Optional[int]		=    model(_UpperCAmelCase     ,     attention_mask=_UpperCAmelCase       )

   __UpperCAmelCase  :     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     lowerCamelCase_   ( _lowerCAmelCase  ,							_lowerCAmelCase  ,							unittest.TestCase):
 _lowerCAmelCase       		=  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 _lowerCAmelCase       		=  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




 def 						a					( self       ):
   __UpperCAmelCase  :     Union[str, Any]		=    FlaxGPTNeoModelTester(self       )




 def 						a					( self       ):
   for model_class_name in self.all_model_classes:
     __UpperCAmelCase  :     Any		=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )




 def 						a					( self       ):
   for model_class_name in self.all_model_classes:
     __UpperCAmelCase  :     Tuple		=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward_with_attn_mask(
         _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )




 @slow
 def 						a					( self       ):
   __UpperCAmelCase  :     Union[str, Any]		=    GPTaTokenizer.from_pretrained('gpt2'     ,     pad_token='<|endoftext|>'     ,     padding_side='left'       )
   __UpperCAmelCase  :     Optional[Any]		=    tokenizer(['Hello this is a long string', 'Hey']     ,     return_tensors='np'     ,     padding=_UpperCAmelCase     ,     truncation=_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'       )
   __UpperCAmelCase  :     int		=    False
   __UpperCAmelCase  :     Dict		=    model.config.eos_token_id

   __UpperCAmelCase  :     int		=    jax.jit(model.generate       )

   __UpperCAmelCase  :     List[str]		=    jit_generate(
       inputs['input_ids']     ,     attention_mask=inputs['attention_mask']     ,     pad_token_id=tokenizer.pad_token_id       ).sequences

   __UpperCAmelCase  :     Union[str, Any]		=    tokenizer.batch_decode(_UpperCAmelCase     ,     skip_special_tokens=_UpperCAmelCase       )

   __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(_UpperCAmelCase     ,     _UpperCAmelCase       )




 @is_pt_flax_cross_test
 def 						a					( self       ):
   __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  :     Union[str, Any]		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )
       __UpperCAmelCase  :     List[str]		=    {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

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

       __UpperCAmelCase  :     Optional[int]		=    convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,     _UpperCAmelCase       )
       __UpperCAmelCase  :     List[str]		=    fx_state

       with torch.no_grad():
         __UpperCAmelCase  :     Optional[int]		=    pt_model(**_UpperCAmelCase       ).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(_UpperCAmelCase       )
         __UpperCAmelCase  :     Union[str, Any]		=    model_class.from_pretrained(_UpperCAmelCase     ,     from_pt=_UpperCAmelCase       )

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




 @is_pt_flax_cross_test
 def 						a					( self       ):
   __UpperCAmelCase  :     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  :     int		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )
       __UpperCAmelCase  :     str		=    {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

       __UpperCAmelCase  :     Tuple		=    pt_model_class(_UpperCAmelCase       ).eval()
       __UpperCAmelCase  :     str		=    model_class(_UpperCAmelCase     ,     dtype=jnp.floataa       )

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

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

       with torch.no_grad():
         __UpperCAmelCase  :     Any		=    pt_model(**_UpperCAmelCase       ).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(_UpperCAmelCase       )
         __UpperCAmelCase  :     Any		=    pt_model_class.from_pretrained(_UpperCAmelCase     ,     from_flax=_UpperCAmelCase       )

       with torch.no_grad():
         __UpperCAmelCase  :     str		=    pt_model_loaded(**_UpperCAmelCase       ).to_tuple()

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




 @slow
 def 						a					( self       ):
   for model_class_name in self.all_model_classes:
     __UpperCAmelCase  :     List[Any]		=    model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'       )
     __UpperCAmelCase  :     Dict		=    model(np.ones((1, 1)       )       )
     self.assertIsNotNone(_UpperCAmelCase       )








def    _snake_case		(snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    int     )       ->		int:
  return len_board * len_board_column * row + column





def    _snake_case		(snake_case_				:    list[list[str]]						,					snake_case_				:    str						,					snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    set[int]						,					)       ->		bool:

  if board[row][column] != word[word_index]:
    return False

  if word_index == len(snake_case_     ) - 1:
    return True

  __UpperCAmelCase  :     List[Any]		=    [(0, 1), (0, -1), (-1, 0), (1, 0)]
  __UpperCAmelCase  :     Dict		=    len(snake_case_     )
  __UpperCAmelCase  :     str		=    len(board[0]     )
  for direction in traverts_directions:
    __UpperCAmelCase  :     Dict		=    row + direction[0]
    __UpperCAmelCase  :     Optional[int]		=    column + direction[1]
    if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
      continue

    __UpperCAmelCase  :     str		=    get_point_key(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_     )
    if key in visited_points_set:
      continue

    visited_points_set.add(snake_case_     )
    if exits_word(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_						,					word_index + 1						,					snake_case_     ):
      return True

    visited_points_set.remove(snake_case_     )

  return False





def    _snake_case		(snake_case_				:    list[list[str]]						,					snake_case_				:    str     )       ->		bool:
  __UpperCAmelCase  :     str		=    (
      'The board should be a non empty matrix of single chars strings.'
  )

  __UpperCAmelCase  :     Any		=    len(snake_case_     )
  if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) == 0:
    raise ValueError(snake_case_     )

  for row in board:
    if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) == 0:
      raise ValueError(snake_case_     )

    for item in row:
      if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) != 1:
        raise ValueError(snake_case_     )

    # Validate word
  if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) == 0:
    raise ValueError(
        'The word parameter should be a string of length greater than 0.'     )

  __UpperCAmelCase  :     str		=    len(board[0]     )
  for i in range(snake_case_     ):
    for j in range(snake_case_     ):
      if exits_word(
          snake_case_						,					snake_case_						,					snake_case_						,					snake_case_						,					0						,					{get_point_key(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_     )}     ):
        return True

  return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()





