
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_						: str         =  logging.get_logger(__name__)

lowerCamelCase_						: Any         =  {
    """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        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = """bart"""
			__UpperCamelCase      :						List[str]					      = ["""past_key_values"""]
			__UpperCamelCase      :						List[str]					      = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

			def __init__(       self :       List[Any]						,	snake_case_ :       Tuple=5_0265						,	snake_case_ :       int=1024						,	snake_case_ :       Any=12						,	snake_case_ :       Optional[Any]=4096						,	snake_case_ :       Union[str, Any]=16						,	snake_case_ :       Any=12						,	snake_case_ :       List[str]=4096						,	snake_case_ :       str=16						,	snake_case_ :       Union[str, Any]=0.0						,	snake_case_ :       Dict=0.0						,	snake_case_ :       List[Any]="gelu"						,	snake_case_ :       Union[str, Any]=1024						,	snake_case_ :       Dict=0.1						,	snake_case_ :       List[str]=0.0						,	snake_case_ :       Union[str, Any]=0.0						,	snake_case_ :       Dict=0.02						,	snake_case_ :       Optional[Any]=0.0						,	snake_case_ :       Any=False						,	snake_case_ :       Any=True						,	snake_case_ :       List[Any]=3						,	snake_case_ :       int=1						,	snake_case_ :       Optional[Any]=0						,	snake_case_ :       Any=2						,	snake_case_ :       Optional[int]=True						,	snake_case_ :       List[str]=2						,	snake_case_ :       Dict=2						,	**snake_case_ :       Optional[int]						,	):
							UpperCamelCase_:							Optional[Any]	=						vocab_size
							UpperCamelCase_:							Optional[int]	=						max_position_embeddings
							UpperCamelCase_:							Dict	=						d_model
							UpperCamelCase_:							List[Any]	=						encoder_ffn_dim
							UpperCamelCase_:							int	=						encoder_layers
							UpperCamelCase_:							Dict	=						encoder_attention_heads
							UpperCamelCase_:							Optional[int]	=						decoder_ffn_dim
							UpperCamelCase_:							Any	=						decoder_layers
							UpperCamelCase_:							Tuple	=						decoder_attention_heads
							UpperCamelCase_:							Tuple	=						dropout
							UpperCamelCase_:							Tuple	=						attention_dropout
							UpperCamelCase_:							Optional[int]	=						activation_dropout
							UpperCamelCase_:							Any	=						activation_function
							UpperCamelCase_:							Union[str, Any]	=						init_std
							UpperCamelCase_:							List[Any]	=						encoder_layerdrop
							UpperCamelCase_:							Union[str, Any]	=						decoder_layerdrop
							UpperCamelCase_:							str	=						classifier_dropout
							UpperCamelCase_:							List[Any]	=						use_cache
							UpperCamelCase_:							Optional[int]	=						encoder_layers
							UpperCamelCase_:							Optional[Any]	=						scale_embedding  # scale factor will be sqrt(d_model) if True

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

							# ensure backward compatibility for BART CNN models
							if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""						,	snake_case_      ):
											UpperCamelCase_:							List[str]	=						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        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			@property
			def       lowerCAmelCase__	(       self :       Tuple      ):
							if self.task in ["default", "seq2seq-lm"]:
											UpperCamelCase_:							Tuple	=						OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]      )

											if self.use_past:
															UpperCamelCase_:							int	=						{0: """batch"""}
															UpperCamelCase_:							Tuple	=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
															UpperCamelCase_:							Union[str, Any]	=						{0: """batch""", 1: """decoder_sequence"""}
															UpperCamelCase_:							Optional[int]	=						{0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
															self.fill_with_past_key_values_(snake_case_						,	direction="""inputs"""      )
							elif self.task == "causal-lm":
											# TODO: figure this case out.
											UpperCamelCase_:							List[Any]	=						OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]      )
											if self.use_past:
															UpperCamelCase_:							Tuple	=						self.num_layers
															for i in range(snake_case_      ):
																			UpperCamelCase_:							Optional[int]	=						{0: """batch""", 2: """past_sequence + sequence"""}
																			UpperCamelCase_:							Any	=						{0: """batch""", 2: """past_sequence + sequence"""}
							else:
											UpperCamelCase_:							Tuple	=						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       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							if self.task in ["default", "seq2seq-lm"]:
											UpperCamelCase_:							Optional[int]	=						super().outputs
							else:
											UpperCamelCase_:							List[Any]	=						super(snake_case_						,	self      ).outputs
											if self.use_past:
															UpperCamelCase_:							List[str]	=						self.num_layers
															for i in range(snake_case_      ):
																			UpperCamelCase_:							Union[str, Any]	=						{0: """batch""", 2: """past_sequence + sequence"""}
																			UpperCamelCase_:							List[Any]	=						{0: """batch""", 2: """past_sequence + sequence"""}
							return common_outputs

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       PreTrainedTokenizer						,	snake_case_ :       int = -1						,	snake_case_ :       int = -1						,	snake_case_ :       bool = False						,	snake_case_ :       Optional[TensorType] = None						,	):
							UpperCamelCase_:							Dict	=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    snake_case_						,	snake_case_						,	snake_case_						,	snake_case_						,	snake_case_      )

							# Generate decoder inputs
							UpperCamelCase_:							Optional[Any]	=						seq_length if not self.use_past else 1
							UpperCamelCase_:							str	=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    snake_case_						,	snake_case_						,	snake_case_						,	snake_case_						,	snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
							UpperCamelCase_:							Union[str, Any]	=						dict(**snake_case_						,	**snake_case_      )

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

											UpperCamelCase_:							Union[str, Any]	=						torch.cat(
											    [common_inputs["""decoder_attention_mask"""], torch.ones(snake_case_						,	snake_case_      )]						,	dim=1      )

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

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

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       PreTrainedTokenizer						,	snake_case_ :       int = -1						,	snake_case_ :       int = -1						,	snake_case_ :       bool = False						,	snake_case_ :       Optional[TensorType] = None						,	):
							UpperCamelCase_:							int	=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    snake_case_						,	snake_case_						,	snake_case_						,	snake_case_						,	snake_case_      )

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

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

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       PreTrainedTokenizer						,	snake_case_ :       int = -1						,	snake_case_ :       int = -1						,	snake_case_ :       bool = False						,	snake_case_ :       Optional[TensorType] = None						,	):
							# 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
							UpperCamelCase_:							Any	=						compute_effective_axis_dimension(
							    snake_case_						,	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
							UpperCamelCase_:							Dict	=						tokenizer.num_special_tokens_to_add(snake_case_      )
							UpperCamelCase_:							List[str]	=						compute_effective_axis_dimension(
							    snake_case_						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=snake_case_      )

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

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       PreTrainedTokenizer						,	snake_case_ :       int = -1						,	snake_case_ :       int = -1						,	snake_case_ :       bool = False						,	snake_case_ :       Optional[TensorType] = None						,	):
							if self.task in ["default", "seq2seq-lm"]:
											UpperCamelCase_:							str	=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    snake_case_						,	batch_size=snake_case_						,	seq_length=snake_case_						,	is_pair=snake_case_						,	framework=snake_case_      )

							elif self.task == "causal-lm":
											UpperCamelCase_:							Optional[int]	=						self._generate_dummy_inputs_for_causal_lm(
											    snake_case_						,	batch_size=snake_case_						,	seq_length=snake_case_						,	is_pair=snake_case_						,	framework=snake_case_      )
							else:
											UpperCamelCase_:							Optional[int]	=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    snake_case_						,	batch_size=snake_case_						,	seq_length=snake_case_						,	is_pair=snake_case_						,	framework=snake_case_      )

							return common_inputs




			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Optional[int]						,	snake_case_ :       Optional[int]						,	snake_case_ :       Optional[int]      ):
							if self.task in ["default", "seq2seq-lm"]:
											UpperCamelCase_:							Tuple	=						super()._flatten_past_key_values_(snake_case_						,	snake_case_						,	snake_case_						,	snake_case_      )
							else:
											UpperCamelCase_:							List[str]	=						super(snake_case_						,	self      )._flatten_past_key_values_(
											    snake_case_						,	snake_case_						,	snake_case_						,	snake_case_      )








lowerCamelCase_						: Optional[Any]         =  """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCamelCase_						: Union[str, Any]         =  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCamelCase_						: Optional[Any]         =  {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






