



"""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  SCREAMING_SNAKE_CASE__    :

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





    '''simple docstring'''




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

   def       SCREAMING_SNAKE_CASE	(  self		)    -> Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase      :   Optional[Any]      =      ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size		)

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

    UpperCAmelCase      :   List[str]      =      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[Any]:





    '''simple docstring'''




    UpperCAmelCase      :   Tuple      =      self.prepare_config_and_inputs()
    UpperCAmelCase      :   str      =      config_and_inputs
    UpperCAmelCase      :   List[Any]      =      {'input_ids': input_ids, 'attention_mask': attention_mask}
    return config, inputs_dict

   def       SCREAMING_SNAKE_CASE	(  self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE		)    -> Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase      :   Optional[Any]      =      20
    UpperCAmelCase      :   List[Any]      =      model_class_name(lowerCamelCase__		)

    UpperCAmelCase      :   Dict      =      model.init_cache(input_ids.shape[0] ,	lowerCamelCase__		)
    UpperCAmelCase      :   List[str]      =      jnp.ones((input_ids.shape[0], max_decoder_length) ,	dtype="""i4"""		)

    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      :   Optional[int]      =      model(
        input_ids[:, :-1] ,	attention_mask=lowerCamelCase__ ,	past_key_values=lowerCamelCase__ ,	position_ids=lowerCamelCase__ ,	)

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

    UpperCAmelCase      :   List[str]      =      model(lowerCamelCase__		)

    UpperCAmelCase      :   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 ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE		)    -> List[str]:





    '''simple docstring'''




    UpperCAmelCase      :   str      =      20
    UpperCAmelCase      :   Tuple      =      model_class_name(lowerCamelCase__		)

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

    UpperCAmelCase      :   Union[str, Any]      =      model(
        input_ids[:, :-1] ,	attention_mask=lowerCamelCase__ ,	past_key_values=lowerCamelCase__ ,	position_ids=lowerCamelCase__ ,	)
    UpperCAmelCase      :   Optional[int]      =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype="""i4"""		)
    UpperCAmelCase      :   Optional[int]      =      model(
        input_ids[:, -1:] ,	past_key_values=outputs_cache.past_key_values ,	attention_mask=lowerCamelCase__ ,	position_ids=lowerCamelCase__ ,	)

    UpperCAmelCase      :   Tuple      =      model(lowerCamelCase__ ,	attention_mask=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}"		)



@require_flax
class  SCREAMING_SNAKE_CASE__    (   UpperCAmelCase__ ,     UpperCAmelCase__ ,     unittest.TestCase					):
   __lowerCAmelCase    :  List[Any]		=		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   __lowerCAmelCase    :  Union[str, Any]		=		(FlaxGPTJForCausalLM,) if is_flax_available() else ()

   def       SCREAMING_SNAKE_CASE	(  self		)    -> List[Any]:





    '''simple docstring'''




    UpperCAmelCase      :   Optional[int]      =      FlaxGPTJModelTester(self		)

   def       SCREAMING_SNAKE_CASE	(  self		)    -> List[Any]:





    '''simple docstring'''




    for model_class_name in self.all_model_classes:
     UpperCAmelCase      :   Dict      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(lowerCamelCase__ ,	lowerCamelCase__ ,	lowerCamelCase__ ,	lowerCamelCase__		)

   def       SCREAMING_SNAKE_CASE	(  self		)    -> List[Any]:





    '''simple docstring'''




    for model_class_name in self.all_model_classes:
     UpperCAmelCase      :   Union[str, Any]      =      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		)    -> List[Any]:





    '''simple docstring'''




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

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

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

    UpperCAmelCase      :   Union[str, Any]      =      jit_generate(
        inputs["""input_ids"""] ,	attention_mask=inputs["""attention_mask"""] ,	pad_token_id=tokenizer.pad_token_id		).sequences

    UpperCAmelCase      :   str      =      tokenizer.batch_decode(lowerCamelCase__ ,	skip_special_tokens=lowerCamelCase__		)

    UpperCAmelCase      :   Optional[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		)    -> int:





    '''simple docstring'''




    UpperCAmelCase      :   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
      UpperCAmelCase      :   Dict      =      self._prepare_for_class(lowerCamelCase__ ,	lowerCamelCase__		)
      UpperCAmelCase      :   Dict      =      {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

      UpperCAmelCase      :   Optional[int]      =      convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,	lowerCamelCase__		)
      UpperCAmelCase      :   Any      =      fx_state

      with torch.no_grad():
       UpperCAmelCase      :   Optional[Any]      =      pt_model(**lowerCamelCase__		).to_tuple()

      UpperCAmelCase      :   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:
       pt_model.save_pretrained(lowerCamelCase__		)
       UpperCAmelCase      :   Dict      =      model_class.from_pretrained(lowerCamelCase__ ,	from_pt=lowerCamelCase__		)

      UpperCAmelCase      :   Optional[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		)    -> Optional[Any]:





    '''simple docstring'''




    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      :   Optional[int]      =      self._prepare_for_class(lowerCamelCase__ ,	lowerCamelCase__		)
      UpperCAmelCase      :   Tuple      =      {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      :   str      =      getattr(lowerCamelCase__ ,	lowerCamelCase__		)

      UpperCAmelCase      :   int      =      pt_model_class(lowerCamelCase__		).eval()
      UpperCAmelCase      :   List[Any]      =      model_class(lowerCamelCase__ ,	dtype=jnp.floataa		)

      UpperCAmelCase      :   int      =      load_flax_weights_in_pytorch_model(lowerCamelCase__ ,	fx_model.params		)
      UpperCAmelCase      :   Optional[Any]      =      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      :   Union[str, Any]      =      0
       UpperCAmelCase      :   str      =      1
       UpperCAmelCase      :   List[str]      =      0
       UpperCAmelCase      :   Tuple      =      1

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

      with torch.no_grad():
       UpperCAmelCase      :   Any      =      pt_model(**lowerCamelCase__		).to_tuple()

      UpperCAmelCase      :   Union[str, 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[str]      =      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		)    -> Dict:





    '''simple docstring'''




    for model_class_name in self.all_model_classes:
     UpperCAmelCase      :   Optional[int]      =      model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""		)
     UpperCAmelCase      :   Optional[int]      =      model(np.ones((1, 1)		)		)
     self.assertIsNotNone(lowerCamelCase__		)





'''simple docstring'''
from __future__ import annotations






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (y + 1, x + 2),
						    (y - 1, x + 2),
						    (y + 1, x - 2),
						    (y - 1, x - 2),
						    (y + 2, x + 1),
						    (y + 2, x - 1),
						    (y - 2, x + 1),
						    (y - 2, x - 1),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


