





'''simple docstring'''






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


A__				:   Optional[int]      =logging.get_logger(__name__)

A__				:   Union[str, Any]      ={
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}




class  UpperCAmelCase     (			_UpperCAmelCase   ):
      _lowercase:			int						     =   """marian"""
      _lowercase:			List[str]						     =   ["""past_key_values"""]
      _lowercase:			str						     =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




      def __init__(			self			: Union[str, Any]			,			__snake_case			: Tuple=5_81_01			,			__snake_case			: int=None			,			__snake_case			: Optional[Any]=10_24			,			__snake_case			: Dict=12			,			__snake_case			: int=40_96			,			__snake_case			: List[Any]=16			,			__snake_case			: Optional[int]=12			,			__snake_case			: List[str]=40_96			,			__snake_case			: Tuple=16			,			__snake_case			: str=0.0			,			__snake_case			: str=0.0			,			__snake_case			: int=True			,			__snake_case			: str=True			,			__snake_case			: List[Any]="gelu"			,			__snake_case			: Union[str, Any]=10_24			,			__snake_case			: List[Any]=0.1			,			__snake_case			: str=0.0			,			__snake_case			: Dict=0.0			,			__snake_case			: Optional[Any]=0.02			,			__snake_case			: List[Any]=5_81_00			,			__snake_case			: List[Any]=False			,			__snake_case			: str=5_81_00			,			__snake_case			: Dict=0			,			__snake_case			: List[str]=0			,			__snake_case			: int=True			,			**__snake_case			: List[str]			,			)				->      str:
            _lowerCAmelCase							     =							vocab_size
            _lowerCAmelCase							     =							decoder_vocab_size or vocab_size
            _lowerCAmelCase							     =							max_position_embeddings
            _lowerCAmelCase							     =							d_model
            _lowerCAmelCase							     =							encoder_ffn_dim
            _lowerCAmelCase							     =							encoder_layers
            _lowerCAmelCase							     =							encoder_attention_heads
            _lowerCAmelCase							     =							decoder_ffn_dim
            _lowerCAmelCase							     =							decoder_layers
            _lowerCAmelCase							     =							decoder_attention_heads
            _lowerCAmelCase							     =							dropout
            _lowerCAmelCase							     =							attention_dropout
            _lowerCAmelCase							     =							activation_dropout
            _lowerCAmelCase							     =							activation_function
            _lowerCAmelCase							     =							init_std
            _lowerCAmelCase							     =							encoder_layerdrop
            _lowerCAmelCase							     =							decoder_layerdrop
            _lowerCAmelCase							     =							use_cache
            _lowerCAmelCase							     =							encoder_layers
            _lowerCAmelCase							     =							scale_embedding  # scale factor will be sqrt(d_model) if True
            _lowerCAmelCase							     =							share_encoder_decoder_embeddings
            super().__init__(
                pad_token_id=UpperCAmelCase_			,			eos_token_id=UpperCAmelCase_			,			is_encoder_decoder=UpperCAmelCase_			,			decoder_start_token_id=UpperCAmelCase_			,			forced_eos_token_id=UpperCAmelCase_			,			**UpperCAmelCase_			,			)




class  UpperCAmelCase     (			_UpperCAmelCase   ):




      @property
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
      def 				lowercase__    (			self			: Tuple  )				->      Optional[Any]:
            if self.task in ["default", "seq2seq-lm"]:
                  _lowerCAmelCase							     =							OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]  )

                  if self.use_past:
                        _lowerCAmelCase							     =							{0: """batch"""}
                        _lowerCAmelCase							     =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
                  else:
                        _lowerCAmelCase							     =							{0: """batch""", 1: """decoder_sequence"""}
                        _lowerCAmelCase							     =							{0: """batch""", 1: """decoder_sequence"""}

                  if self.use_past:
                        self.fill_with_past_key_values_(UpperCAmelCase_			,			direction="""inputs"""  )
            elif self.task == "causal-lm":
                  # TODO: figure this case out.
                  _lowerCAmelCase							     =							OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]  )
                  if self.use_past:
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.num_layers
                        for i in range(UpperCAmelCase_  ):
                              _lowerCAmelCase							     =							{0: """batch""", 2: """past_sequence + sequence"""}
                              _lowerCAmelCase							     =							{0: """batch""", 2: """past_sequence + sequence"""}
            else:
                  _lowerCAmelCase							     =							OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                          ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                      ]  )

            return common_inputs




      @property
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def 				lowercase__    (			self			: Union[str, Any]  )				->      Tuple:
            if self.task in ["default", "seq2seq-lm"]:
                  _lowerCAmelCase							     =							super().outputs
            else:
                  _lowerCAmelCase							     =							super(UpperCAmelCase_			,			self  ).outputs
                  if self.use_past:
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.num_layers
                        for i in range(UpperCAmelCase_  ):
                              _lowerCAmelCase							     =							{0: """batch""", 2: """past_sequence + sequence"""}
                              _lowerCAmelCase							     =							{0: """batch""", 2: """past_sequence + sequence"""}
            return common_outputs




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: PreTrainedTokenizer			,			__snake_case			: int = -1			,			__snake_case			: int = -1			,			__snake_case			: bool = False			,			__snake_case			: Optional[TensorType] = None			,			)				->      str:
            _lowerCAmelCase							     =							self._generate_dummy_inputs_for_encoder_and_decoder(
                UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_  )

            # Generate decoder inputs
            _lowerCAmelCase							     =							seq_length if not self.use_past else 1
            _lowerCAmelCase							     =							self._generate_dummy_inputs_for_encoder_and_decoder(
                UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_  )
            _lowerCAmelCase							     =							{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
            _lowerCAmelCase							     =							dict(**UpperCAmelCase_			,			**UpperCAmelCase_  )

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
                  else:
                        import torch
                  _lowerCAmelCase     ,							_lowerCAmelCase							     =							common_inputs["""input_ids"""].shape
                  _lowerCAmelCase							     =							common_inputs["""decoder_input_ids"""].shape[1]
                  _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.num_attention_heads
                  _lowerCAmelCase							     =							(
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  _lowerCAmelCase							     =							decoder_seq_length + 3
                  _lowerCAmelCase							     =							(
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

                  _lowerCAmelCase							     =							torch.cat(
                      [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCAmelCase_			,			UpperCAmelCase_  )]			,			dim=1  )

                  _lowerCAmelCase							     =							[]
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.num_layers
                  _lowerCAmelCase							     =							min(UpperCAmelCase_			,			UpperCAmelCase_  )
                  _lowerCAmelCase							     =							max(UpperCAmelCase_			,			UpperCAmelCase_  ) - min_num_layers
                  _lowerCAmelCase							     =							"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

                  for _ in range(UpperCAmelCase_  ):
                        common_inputs["past_key_values"].append(
                            (
                                torch.zeros(UpperCAmelCase_  ),
                                torch.zeros(UpperCAmelCase_  ),
                                torch.zeros(UpperCAmelCase_  ),
                                torch.zeros(UpperCAmelCase_  ),
                            )  )
                  # TODO: test this.
                  _lowerCAmelCase							     =							encoder_shape if remaining_side_name == """encoder""" else decoder_shape
                  for _ in range(UpperCAmelCase_			,			UpperCAmelCase_  ):
                        common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase_  ), torch.zeros(UpperCAmelCase_  ))  )
            return common_inputs




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: PreTrainedTokenizer			,			__snake_case			: int = -1			,			__snake_case			: int = -1			,			__snake_case			: bool = False			,			__snake_case			: Optional[TensorType] = None			,			)				->      Any:
            _lowerCAmelCase							     =							self._generate_dummy_inputs_for_encoder_and_decoder(
                UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_  )

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
                  else:
                        import torch
                  _lowerCAmelCase     ,							_lowerCAmelCase							     =							common_inputs["""input_ids"""].shape
                  # Not using the same length for past_key_values
                  _lowerCAmelCase							     =							seqlen + 2
                  _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.num_layers
                  _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.num_attention_heads
                  _lowerCAmelCase							     =							(
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

                  _lowerCAmelCase							     =							common_inputs["""attention_mask"""].dtype
                  _lowerCAmelCase							     =							torch.cat(
                      [common_inputs["""attention_mask"""], torch.ones(UpperCAmelCase_			,			UpperCAmelCase_			,			dtype=UpperCAmelCase_  )]			,			dim=1  )
                  _lowerCAmelCase							     =							[
                      (torch.zeros(UpperCAmelCase_  ), torch.zeros(UpperCAmelCase_  )) for _ in range(UpperCAmelCase_  )
                  ]
            return common_inputs




      def 				lowercase__    (			self			: Optional[int]			,			__snake_case			: PreTrainedTokenizer			,			__snake_case			: int = -1			,			__snake_case			: int = -1			,			__snake_case			: bool = False			,			__snake_case			: Optional[TensorType] = None			,			)				->      Union[str, Any]:
            # Copied from OnnxConfig.generate_dummy_inputs
            # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            _lowerCAmelCase							     =							compute_effective_axis_dimension(
                UpperCAmelCase_			,			fixed_dimension=OnnxConfig.default_fixed_batch			,			num_token_to_add=0  )

            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            _lowerCAmelCase							     =							tokenizer.num_special_tokens_to_add(UpperCAmelCase_  )
            _lowerCAmelCase							     =							compute_effective_axis_dimension(
                UpperCAmelCase_			,			fixed_dimension=OnnxConfig.default_fixed_sequence			,			num_token_to_add=UpperCAmelCase_  )

            # Generate dummy inputs according to compute batch and sequence
            _lowerCAmelCase							     =							[""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
            _lowerCAmelCase							     =							dict(tokenizer(UpperCAmelCase_			,			return_tensors=UpperCAmelCase_  )  )
            return common_inputs




      def 				lowercase__    (			self			: Union[str, Any]			,			__snake_case			: PreTrainedTokenizer			,			__snake_case			: int = -1			,			__snake_case			: int = -1			,			__snake_case			: bool = False			,			__snake_case			: Optional[TensorType] = None			,			)				->      Any:
            if self.task in ["default", "seq2seq-lm"]:
                  _lowerCAmelCase							     =							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                      UpperCAmelCase_			,			batch_size=UpperCAmelCase_			,			seq_length=UpperCAmelCase_			,			is_pair=UpperCAmelCase_			,			framework=UpperCAmelCase_  )

            else:
                  _lowerCAmelCase							     =							self._generate_dummy_inputs_for_causal_lm(
                      UpperCAmelCase_			,			batch_size=UpperCAmelCase_			,			seq_length=UpperCAmelCase_			,			is_pair=UpperCAmelCase_			,			framework=UpperCAmelCase_  )

            return common_inputs




      def 				lowercase__    (			self			: str			,			__snake_case			: List[Any]			,			__snake_case			: List[Any]			,			__snake_case			: Any			,			__snake_case			: List[str]  )				->      str:
            if self.task in ["default", "seq2seq-lm"]:
                  _lowerCAmelCase							     =							super()._flatten_past_key_values_(UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_  )
            else:
                  _lowerCAmelCase							     =							super(UpperCAmelCase_			,			self  )._flatten_past_key_values_(
                      UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_  )




      @property
      def 				lowercase__    (			self			: List[Any]  )				->      Dict:
            return 1E-4








def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					def update_area_of_max_square(UpperCamelCase_	,	UpperCamelCase_		) -> int:
										# BASE CASE
										if row >= rows or col >= cols:
															return 0

										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(UpperCamelCase_	,	col + 1		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(row + 1	,	col + 1		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(row + 1	,	UpperCamelCase_		)

										if mat[row][col]:
															SCREAMING_SNAKE_CASE__													= 1 + min([right, diagonal, down]		)
															SCREAMING_SNAKE_CASE__													= max(largest_square_area[0]	,	UpperCamelCase_		)
															return sub_problem_sol
										else:
															return 0

					SCREAMING_SNAKE_CASE__													= [0]
					update_area_of_max_square(0	,	0		)
					return largest_square_area[0]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					def update_area_of_max_square_using_dp_array(
					    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		) -> int:
										if row >= rows or col >= cols:
															return 0
										if dp_array[row][col] != -1:
															return dp_array[row][col]

										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(UpperCamelCase_	,	col + 1	,	UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(row + 1	,	col + 1	,	UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(row + 1	,	UpperCamelCase_	,	UpperCamelCase_		)

										if mat[row][col]:
															SCREAMING_SNAKE_CASE__													= 1 + min([right, diagonal, down]		)
															SCREAMING_SNAKE_CASE__													= max(largest_square_area[0]	,	UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= sub_problem_sol
															return sub_problem_sol
										else:
															return 0

					SCREAMING_SNAKE_CASE__													= [0]
					SCREAMING_SNAKE_CASE__													= [[-1] * cols for _ in range(UpperCamelCase_		)]
					update_area_of_max_square_using_dp_array(0	,	0	,	UpperCamelCase_		)

					return largest_square_area[0]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= [[0] * (cols + 1) for _ in range(rows + 1		)]
					SCREAMING_SNAKE_CASE__													= 0
					for row in range(rows - 1	,	-1	,	-1		):
										for col in range(cols - 1	,	-1	,	-1		):
															SCREAMING_SNAKE_CASE__													= dp_array[row][col + 1]
															SCREAMING_SNAKE_CASE__													= dp_array[row + 1][col + 1]
															SCREAMING_SNAKE_CASE__													= dp_array[row + 1][col]

															if mat[row][col] == 1:
																				SCREAMING_SNAKE_CASE__													= 1 + min(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
																				SCREAMING_SNAKE_CASE__													= max(dp_array[row][col]	,	UpperCamelCase_		)
															else:
																				SCREAMING_SNAKE_CASE__													= 0

					return largest_square_area






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= [0] * (cols + 1)
					SCREAMING_SNAKE_CASE__													= [0] * (cols + 1)
					SCREAMING_SNAKE_CASE__													= 0
					for row in range(rows - 1	,	-1	,	-1		):
										for col in range(cols - 1	,	-1	,	-1		):
															SCREAMING_SNAKE_CASE__													= current_row[col + 1]
															SCREAMING_SNAKE_CASE__													= next_row[col + 1]
															SCREAMING_SNAKE_CASE__													= next_row[col]

															if mat[row][col] == 1:
																				SCREAMING_SNAKE_CASE__													= 1 + min(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
																				SCREAMING_SNAKE_CASE__													= max(current_row[col]	,	UpperCamelCase_		)
															else:
																				SCREAMING_SNAKE_CASE__													= 0
										SCREAMING_SNAKE_CASE__													= current_row

					return largest_square_area


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))





