






import warnings
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


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				List[str] 				= "bart"
       lowerCamelCase							:				int 				= ["past_key_values"]
       lowerCamelCase							:				List[Any] 				= {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



       def __init__(							self			,						a__=5_02_65			,						a__=10_24			,						a__=12			,						a__=40_96			,						a__=16			,						a__=12			,						a__=40_96			,						a__=16			,						a__=0.0			,						a__=0.0			,						a__="gelu"			,						a__=10_24			,						a__=0.1			,						a__=0.0			,						a__=0.0			,						a__=0.02			,						a__=0.0			,						a__=False			,						a__=True			,						a__=3			,						a__=1			,						a__=0			,						a__=2			,						a__=True			,						a__=2			,						a__=2			,						**a__			,						) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						vocab_size
            __snake_case			:Union[str, Any]          =						max_position_embeddings
            __snake_case			:Tuple          =						d_model
            __snake_case			:List[Any]          =						encoder_ffn_dim
            __snake_case			:str          =						encoder_layers
            __snake_case			:Optional[int]          =						encoder_attention_heads
            __snake_case			:str          =						decoder_ffn_dim
            __snake_case			:List[Any]          =						decoder_layers
            __snake_case			:Tuple          =						decoder_attention_heads
            __snake_case			:Any          =						dropout
            __snake_case			:int          =						attention_dropout
            __snake_case			:str          =						activation_dropout
            __snake_case			:Tuple          =						activation_function
            __snake_case			:Union[str, Any]          =						init_std
            __snake_case			:str          =						encoder_layerdrop
            __snake_case			:List[str]          =						decoder_layerdrop
            __snake_case			:List[Any]          =						classifier_dropout
            __snake_case			:Tuple          =						use_cache
            __snake_case			:Dict          =						encoder_layers
            __snake_case			:Dict          =						scale_embedding  # scale factor will be sqrt(d_model) if True

            super().__init__(
                num_labels=a__			,						pad_token_id=a__			,						bos_token_id=a__			,						eos_token_id=a__			,						is_encoder_decoder=a__			,						decoder_start_token_id=a__			,						forced_eos_token_id=a__			,						**a__			,						)

            # ensure backward compatibility for BART CNN models
            if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""			,						a__    ):
                 __snake_case			:Optional[Any]          =						self.bos_token_id
                 warnings.warn(
                     F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                     """The config can simply be saved and uploaded again to be fixed."""    )




class 			snake_case__   (							lowercase_):

       '''simple docstring'''



       @property
       def 					__lowercase						(							self    ) ->      Mapping[str, Mapping[int, str]]:






            '''simple docstring'''





            if self.task in ["default", "seq2seq-lm"]:
                 __snake_case			:List[str]          =						OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ]    )

                 if self.use_past:
                      __snake_case			:str          =						{0: """batch"""}
                      __snake_case			:Dict          =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
                 else:
                      __snake_case			:Union[str, Any]          =						{0: """batch""", 1: """decoder_sequence"""}
                      __snake_case			:List[str]          =						{0: """batch""", 1: """decoder_sequence"""}

                 if self.use_past:
                      self.fill_with_past_key_values_(a__			,						direction="""inputs"""    )
            elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 __snake_case			:int          =						OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ]    )
                 if self.use_past:
                      __snake_case       , __snake_case			:Dict          =						self.num_layers
                      for i in range(a__    ):
                           __snake_case			:Union[str, Any]          =						{0: """batch""", 2: """past_sequence + sequence"""}
                           __snake_case			:Optional[Any]          =						{0: """batch""", 2: """past_sequence + sequence"""}
            else:
                 __snake_case			:Optional[Any]          =						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
       def 					__lowercase						(							self    ) ->      Mapping[str, Mapping[int, str]]:






            '''simple docstring'''





            if self.task in ["default", "seq2seq-lm"]:
                 __snake_case			:Union[str, Any]          =						super().outputs
            else:
                 __snake_case			:Any          =						super(a__			,						self    ).outputs
                 if self.use_past:
                      __snake_case       , __snake_case			:Tuple          =						self.num_layers
                      for i in range(a__    ):
                           __snake_case			:Dict          =						{0: """batch""", 2: """past_sequence + sequence"""}
                           __snake_case			:Any          =						{0: """batch""", 2: """past_sequence + sequence"""}
            return common_outputs



       def 					__lowercase						(							self			,						a__			,						a__ = -1			,						a__ = -1			,						a__ = False			,						a__ = None			,						) ->      Mapping[str, Any]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                a__			,						a__			,						a__			,						a__			,						a__    )

            # Generate decoder inputs
            __snake_case			:Union[str, Any]          =						seq_length if not self.use_past else 1
            __snake_case			:Dict          =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                a__			,						a__			,						a__			,						a__			,						a__    )
            __snake_case			:List[Any]          =						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
            __snake_case			:int          =						dict(**a__			,						**a__    )

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

                 __snake_case			:Optional[Any]          =						torch.cat(
                     [common_inputs["""decoder_attention_mask"""], torch.ones(a__			,						a__    )]			,						dim=1    )

                 __snake_case			:str          =						[]
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 __snake_case       , __snake_case			:List[str]          =						self.num_layers
                 __snake_case			:Union[str, Any]          =						min(a__			,						a__    )
                 __snake_case			:List[str]          =						max(a__			,						a__    ) - min_num_layers
                 __snake_case			:List[Any]          =						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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



       def 					__lowercase						(							self			,						a__			,						a__ = -1			,						a__ = -1			,						a__ = False			,						a__ = None			,						) ->      Mapping[str, Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                a__			,						a__			,						a__			,						a__			,						a__    )

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

                 __snake_case			:Optional[int]          =						common_inputs["""attention_mask"""].dtype
                 __snake_case			:List[Any]          =						torch.cat(
                     [common_inputs["""attention_mask"""], torch.ones(a__			,						a__			,						dtype=a__    )]			,						dim=1    )
                 __snake_case			:Optional[int]          =						[
                     (torch.zeros(a__    ), torch.zeros(a__    )) for _ in range(a__    )
                 ]
            return common_inputs



       def 					__lowercase						(							self			,						a__			,						a__ = -1			,						a__ = -1			,						a__ = False			,						a__ = None			,						) ->      Mapping[str, Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						compute_effective_axis_dimension(
                a__			,						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
            __snake_case			:str          =						tokenizer.num_special_tokens_to_add(a__    )
            __snake_case			:List[Any]          =						compute_effective_axis_dimension(
                a__			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=a__    )

            # Generate dummy inputs according to compute batch and sequence
            __snake_case			:List[str]          =						[""" """.join([tokenizer.unk_token]    ) * seq_length] * batch_size
            __snake_case			:Dict          =						dict(tokenizer(a__			,						return_tensors=a__    )    )
            return common_inputs



       def 					__lowercase						(							self			,						a__			,						a__ = -1			,						a__ = -1			,						a__ = False			,						a__ = None			,						) ->      Mapping[str, Any]:






            '''simple docstring'''





            if self.task in ["default", "seq2seq-lm"]:
                 __snake_case			:List[Any]          =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     a__			,						batch_size=a__			,						seq_length=a__			,						is_pair=a__			,						framework=a__    )

            elif self.task == "causal-lm":
                 __snake_case			:Union[str, Any]          =						self._generate_dummy_inputs_for_causal_lm(
                     a__			,						batch_size=a__			,						seq_length=a__			,						is_pair=a__			,						framework=a__    )
            else:
                 __snake_case			:Union[str, Any]          =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                     a__			,						batch_size=a__			,						seq_length=a__			,						is_pair=a__			,						framework=a__    )

            return common_inputs



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__    ) ->      Optional[Any]:






            '''simple docstring'''





            if self.task in ["default", "seq2seq-lm"]:
                 __snake_case			:Any          =						super()._flatten_past_key_values_(a__			,						a__			,						a__			,						a__    )
            else:
                 __snake_case			:Optional[Any]          =						super(a__			,						self    )._flatten_past_key_values_(
                     a__			,						a__			,						a__			,						a__    )










def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]
     __snake_case			:Union[str, Any]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1 or len(snake_case__  ) <= key:
          return input_string

     for position, character in enumerate(snake_case__  ):
          __snake_case			:Any          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:Optional[int]          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append(snake_case__  )
     __snake_case			:List[Any]          =						["""""".join(snake_case__  ) for row in temp_grid]
     __snake_case			:Any          =						"""""".join(snake_case__  )

     return output_string






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						[]
     __snake_case			:Optional[int]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1:
          return input_string

     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]  # generates template
     for position in range(len(snake_case__  )  ):
          __snake_case			:List[str]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append("""*"""  )

     __snake_case			:str          =						0
     for row in temp_grid:  # fills in the characters
          __snake_case			:str          =						input_string[counter : counter + len(snake_case__  )]
          grid.append(list(snake_case__  )  )
          counter += len(snake_case__  )

     __snake_case			:Any          =						""""""  # reads as zigzag
     for position in range(len(snake_case__  )  ):
          __snake_case			:Optional[int]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          output_string += grid[num][0]
          grid[num].pop(0  )
     return output_string






def   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:Optional[Any]          =						{}
     for key_guess in range(1	,len(snake_case__  )  ):  # tries every key
          __snake_case			:Dict          =						decrypt(snake_case__	,snake_case__  )
     return results


if __name__ == "__main__":
     import doctest

     doctest.testmod()


