import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
    import tensorflow as tf

if is_keras_nlp_available():
    from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase			:							Optional[int]     =["gpt2"]
lowerCAmelCase			:							Optional[int]     ="gpt2"

if is_tf_available():




    class 	__snake_case	( tf.Module		):





          '''simple docstring'''






          def __init__(					self     :				List[str]    ,  _UpperCamelCase     :				List[str])				->Optional[Any]:


              """simple docstring"""


              super().__init__()
              _lowerCamelCase     :		int 						=						tokenizer
              _lowerCamelCase     :		Any 						=						AutoConfig.from_pretrained(_UpperCamelCase)
              _lowerCamelCase     :		Optional[Any] 						=						TFGPTaLMHeadModel.from_config(_UpperCamelCase)




          @tf.function(input_signature=(tf.TensorSpec((None,)    ,  tf.string    ,  name="""text"""),))
          def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				Tuple)				->List[Any]:


              """simple docstring"""


              _lowerCamelCase     :		List[Any] 						=						self.tokenizer(_UpperCamelCase)
              _lowerCamelCase     :		Dict 						=						tokenized["""input_ids"""].to_tensor()

              _lowerCamelCase     :		int 						=						tf.cast(input_ids_dense > 0    ,  tf.intaa)
              # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

              _lowerCamelCase     :		Union[str, Any] 						=						self.model(input_ids=_UpperCamelCase    ,  attention_mask=_UpperCamelCase)["""logits"""]

              return outputs




@require_tf
@require_keras_nlp
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Dict:


          """simple docstring"""


          super().setUp()

          _lowerCamelCase     :		List[str] 						=						[GPTaTokenizer.from_pretrained(_UpperCamelCase) for checkpoint in (TOKENIZER_CHECKPOINTS)]
          _lowerCamelCase     :		Optional[int] 						=						[TFGPTaTokenizer.from_pretrained(_UpperCamelCase) for checkpoint in TOKENIZER_CHECKPOINTS]
          assert len(self.tokenizers) == len(self.tf_tokenizers)

          _lowerCamelCase     :		int 						=						[
              """This is a straightforward English test sentence.""",
              """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
              """Now we're going to add some Chinese: 一 二 三 一二三""",
              """And some much more rare Chinese: 齉 堃 齉堃""",
              """Je vais aussi écrire en français pour tester les accents""",
              """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
          ]
          _lowerCamelCase     :		List[Any] 						=						list(zip(self.test_sentences    ,  self.test_sentences[::-1]))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Optional[Any]:


          """simple docstring"""


          for tokenizer, tf_tokenizer in zip(self.tokenizers    ,  self.tf_tokenizers):
              for test_inputs in self.test_sentences:
                  _lowerCamelCase     :		List[str] 						=						tokenizer([test_inputs]    ,  return_tensors="""tf""")
                  _lowerCamelCase     :		Tuple 						=						tf_tokenizer([test_inputs])

                  for key in python_outputs.keys():
                      # convert them to numpy to avoid messing with ragged tensors
                      _lowerCamelCase     :		Any 						=						python_outputs[key].numpy()
                      _lowerCamelCase     :		str 						=						tf_outputs[key].numpy()

                      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
                      self.assertTrue(tf.reduce_all(tf.cast(_UpperCamelCase    ,  tf.intaa) == tf_outputs_values))



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Tuple:


          """simple docstring"""


          for tf_tokenizer in self.tf_tokenizers:
              _lowerCamelCase     :		Dict 						=						tf.function(_UpperCamelCase)
              for test_inputs in self.test_sentences:
                  _lowerCamelCase     :		List[Any] 						=						tf.constant(_UpperCamelCase)
                  _lowerCamelCase     :		str 						=						compiled_tokenizer(_UpperCamelCase)
                  _lowerCamelCase     :		Any 						=						tf_tokenizer(_UpperCamelCase)

                  for key in eager_outputs.keys():
                      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->List[Any]:


          """simple docstring"""


          for tf_tokenizer in self.tf_tokenizers:
              _lowerCamelCase     :		Optional[Any] 						=						ModelToSave(tokenizer=_UpperCamelCase)
              _lowerCamelCase     :		Any 						=						tf.convert_to_tensor([self.test_sentences[0]])
              _lowerCamelCase     :		Any 						=						model.serving(_UpperCamelCase)  # Build model with some sample inputs
              with TemporaryDirectory() as tempdir:
                  _lowerCamelCase     :		Dict 						=						Path(_UpperCamelCase) / """saved.model"""
                  tf.saved_model.save(_UpperCamelCase    ,  _UpperCamelCase    ,  signatures={"""serving_default""": model.serving})
                  _lowerCamelCase     :		Union[str, Any] 						=						tf.saved_model.load(_UpperCamelCase)
              _lowerCamelCase     :		Union[str, Any] 						=						loaded_model.signatures["""serving_default"""](_UpperCamelCase)["""output_0"""]
              # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
              self.assertTrue(tf.reduce_all(out == loaded_output))



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


          """simple docstring"""


          for tf_tokenizer in self.tf_tokenizers:
              _lowerCamelCase     :		Dict 						=						tf.convert_to_tensor([self.test_sentences[0]])
              _lowerCamelCase     :		Optional[Any] 						=						tf_tokenizer(_UpperCamelCase)  # Build model with some sample inputs

              _lowerCamelCase     :		List[Any] 						=						tf_tokenizer.get_config()
              _lowerCamelCase     :		Union[str, Any] 						=						TFGPTaTokenizer.from_config(_UpperCamelCase)
              _lowerCamelCase     :		List[Any] 						=						model_from_config(_UpperCamelCase)

              for key in from_config_output.keys():
                  self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Optional[Any]:


          """simple docstring"""


          for tf_tokenizer in self.tf_tokenizers:
              # for the test to run
              _lowerCamelCase     :		Optional[Any] 						=						12_3123

              for max_length in [3, 5, 1024]:
                  _lowerCamelCase     :		List[str] 						=						tf.convert_to_tensor([self.test_sentences[0]])
                  _lowerCamelCase     :		Optional[Any] 						=						tf_tokenizer(_UpperCamelCase    ,  max_length=_UpperCamelCase)

                  _lowerCamelCase     :		Tuple 						=						out["""input_ids"""].numpy().shape[1]

                  assert out_length == max_length





from __future__ import annotations

lowerCAmelCase			:							int     =[]







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        if board[row][i] == 1:
            return False
    for i in range(len(__A	)	):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , -1   , -1	)	):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , len(__A	)	)	):
        if board[i][j] == 1:
            return False
    return True







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if row >= len(__A	):
        solution.append(__A	)
        printboard(__A	)
        print()
        return True
    for i in range(len(__A	)	):
        if is_safe(__A   , __A   , __A	):
            _lowerCamelCase     :		int 						=						1
            solve(__A   , row + 1	)
            _lowerCamelCase     :		List[str] 						=						0
    return False







def 						A__  (					__A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        for j in range(len(__A	)	):
            if board[i][j] == 1:
                print("""Q"""   , end=""" """	)
            else:
                print("""."""   , end=""" """	)
        print()


# n=int(input("The no. of queens"))
lowerCAmelCase			:							int     =8
lowerCAmelCase			:							Union[str, Any]     =[[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))




