
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel






@require_tf
class UpperCAmelCase   :





   '''simple docstring'''
   __UpperCamelCase		:    str 				=    MBartConfig
   __UpperCamelCase		:    Dict 				=    {}
   __UpperCamelCase		:    Tuple 				=    '''gelu'''


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							str=1_3			,			lowerCAmelCase_     :							Union[str, Any]=7			,			lowerCAmelCase_     :							Dict=True			,			lowerCAmelCase_     :							Tuple=False			,			lowerCAmelCase_     :							Dict=9_9			,			lowerCAmelCase_     :							int=3_2			,			lowerCAmelCase_     :							Tuple=2			,			lowerCAmelCase_     :							str=4			,			lowerCAmelCase_     :							Any=3_7			,			lowerCAmelCase_     :							int=0.1			,			lowerCAmelCase_     :							Union[str, Any]=0.1			,			lowerCAmelCase_     :							List[str]=2_0			,			lowerCAmelCase_     :							Dict=2			,			lowerCAmelCase_     :							Optional[int]=1			,			lowerCAmelCase_     :							Dict=0			,			):

      """simple docstring"""
      _A:  Union[str, Any]					=       parent
      _A:  List[str]					=       batch_size
      _A:  str					=       seq_length
      _A:  str					=       is_training
      _A:  Dict					=       use_labels
      _A:  List[Any]					=       vocab_size
      _A:  int					=       hidden_size
      _A:  List[Any]					=       num_hidden_layers
      _A:  Dict					=       num_attention_heads
      _A:  Optional[Any]					=       intermediate_size
      _A:  Optional[int]					=       hidden_dropout_prob
      _A:  Union[str, Any]					=       attention_probs_dropout_prob
      _A:  Dict					=       max_position_embeddings
      _A:  Dict					=       eos_token_id
      _A:  Any					=       pad_token_id
      _A:  int					=       bos_token_id


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  str					=       ids_tensor([self.batch_size, self.seq_length - 1]			,			self.vocab_size     )
      _A:  Optional[int]					=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )			,			1     )
      _A:  List[str]					=       tf.concat([input_ids, eos_tensor]			,			axis=1     )

      _A:  List[Any]					=       ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size     )

      _A:  List[Any]					=       self.config_cls(
          vocab_size=self.vocab_size			,			d_model=self.hidden_size			,			encoder_layers=self.num_hidden_layers			,			decoder_layers=self.num_hidden_layers			,			encoder_attention_heads=self.num_attention_heads			,			decoder_attention_heads=self.num_attention_heads			,			encoder_ffn_dim=self.intermediate_size			,			decoder_ffn_dim=self.intermediate_size			,			dropout=self.hidden_dropout_prob			,			attention_dropout=self.attention_probs_dropout_prob			,			max_position_embeddings=self.max_position_embeddings			,			eos_token_ids=[2]			,			bos_token_id=self.bos_token_id			,			pad_token_id=self.pad_token_id			,			decoder_start_token_id=self.pad_token_id			,			**self.config_updates			,			)
      _A:  int					=       prepare_mbart_inputs_dict(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     )
      return config, inputs_dict


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      _A:  Tuple					=       TFMBartModel(config=lowerCAmelCase_     ).get_decoder()
      _A:  int					=       inputs_dict['input_ids']

      _A:  Union[str, Any]					=       input_ids[:1, :]
      _A:  str					=       inputs_dict['attention_mask'][:1, :]
      _A:  Union[str, Any]					=       inputs_dict['head_mask']
      _A:  Union[str, Any]					=       1

      # first forward pass
      _A:  Union[str, Any]					=       model(lowerCAmelCase_			,			attention_mask=lowerCAmelCase_			,			head_mask=lowerCAmelCase_			,			use_cache=lowerCAmelCase_     )

      _A:  Any					=       outputs.to_tuple()
      _A:  Optional[int]					=       past_key_values[1]






def   lowerCamelCase__							(					a		,		a		,		a		,		a=None		,		a=None		,		a=None		,		a=None		,		a=None		,		)      ->   Union[str, Any]:
   if attention_mask is None:
      _A:  Union[str, Any]					=       tf.cast(tf.math.not_equal(_UpperCAmelCase		,		config.pad_token_id      )		,		tf.inta      )
   if decoder_attention_mask is None:
      _A:  Any					=       tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape		,		dtype=tf.inta      ),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,		config.pad_token_id      )		,		tf.inta      ),
          ]		,		axis=-1		,		)
   if head_mask is None:
      _A:  Dict					=       tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
   if decoder_head_mask is None:
      _A:  Dict					=       tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
   if cross_attn_head_mask is None:
      _A:  Dict					=       tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }






@require_tf
class UpperCAmelCase   (						__UpperCamelCase							,							__UpperCamelCase							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Optional[Any] 				=    (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
   __UpperCamelCase		:    Tuple 				=    (TFMBartForConditionalGeneration,) if is_tf_available() else ()
   __UpperCamelCase		:    Optional[int] 				=    (
       {
           '''conversational''': TFMBartForConditionalGeneration,
           '''feature-extraction''': TFMBartModel,
           '''summarization''': TFMBartForConditionalGeneration,
           '''text2text-generation''': TFMBartForConditionalGeneration,
           '''translation''': TFMBartForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   __UpperCamelCase		:    List[Any] 				=    True
   __UpperCamelCase		:    Union[str, Any] 				=    False
   __UpperCamelCase		:    str 				=    False


   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							str     ):

      """simple docstring"""
      if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
         # Exception encountered when calling layer '...'
         return True

      return False


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  Dict					=       TFMBartModelTester(self     )
      _A:  List[str]					=       ConfigTester(self			,			config_class=lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      self.config_tester.run_common_tests()


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  int					=       self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_     )






@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    str 				=    [
       ''' UN Chief Says There Is No Military Solution in Syria''',
   ]
   __UpperCamelCase		:    Optional[Any] 				=    [
       '''Şeful ONU declară că nu există o soluţie militară în Siria''',
   ]
   __UpperCamelCase		:    Optional[Any] 				=    '''facebook/mbart-large-en-ro'''


   @cached_property
   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      return AutoTokenizer.from_pretrained(self.model_name     )


   @cached_property
   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  str					=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
      return model


   def  __magic_name__				(					self     :							List[Any]			,			**lowerCAmelCase_     :							Optional[Any]     ):

      """simple docstring"""
      _A:  List[str]					=       self.translate_src_text(**lowerCAmelCase_     )
      self.assertListEqual(self.expected_text			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							str			,			**lowerCAmelCase_     :							int     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       self.tokenizer(self.src_text			,			**lowerCAmelCase_			,			return_tensors='''tf'''     )
      _A:  Optional[int]					=       self.model.generate(
          model_inputs.input_ids			,			attention_mask=model_inputs.attention_mask			,			num_beams=2     )
      _A:  str					=       self.tokenizer.batch_decode(lowerCAmelCase_			,			skip_special_tokens=lowerCAmelCase_     )
      return generated_words


   @slow
   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      self._assert_generated_batch_equal_expected()










from __future__ import annotations


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     list[tuple[int, int]]:
    lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		position
    lowerCamelCase__    :      Optional[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),
    ]
    lowerCamelCase__    :      Dict					 =		[]

    for position in positions:
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(_UpperCAmelCase )

    return permissible_positions


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     bool:
    return not any(elem == 0 for row in board for elem in row )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     bool:

    if is_complete(_UpperCAmelCase ):
        return True

    for position in get_valid_pos(_UpperCAmelCase						,	len(_UpperCAmelCase ) ):
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position

        if board[y][x] == 0:
            lowerCamelCase__    :      List[Any]					 =		curr + 1
            if open_knight_tour_helper(_UpperCAmelCase						,	_UpperCAmelCase						,	curr + 1 ):
                return True
            lowerCamelCase__    :      Optional[Any]					 =		0

    return False


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     list[list[int]]:
    lowerCamelCase__    :      Any					 =		[[0 for i in range(_UpperCAmelCase )] for j in range(_UpperCAmelCase )]

    for i in range(_UpperCAmelCase ):
        for j in range(_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[int]					 =		1
            if open_knight_tour_helper(_UpperCAmelCase						,	(i, j)						,	1 ):
                return board
            lowerCamelCase__    :      Optional[Any]					 =		0

    lowerCamelCase__    :      Any					 =		F"""Open Kight Tour cannot be performed on a board of size {n}"""
    raise ValueError(_UpperCAmelCase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





