



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/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}






class 				SCREAMING_SNAKE_CASE     (							__A   ):
							__lowerCamelCase		:		Union[str, Any]      ='mbart'
							__lowerCamelCase		:		Optional[int]      =['past_key_values']
							__lowerCamelCase		:		Any      ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





							def __init__(				self						:						Tuple ,			__lowercase						:						Any=50265 ,			__lowercase						:						Optional[int]=1024 ,			__lowercase						:						Union[str, Any]=12 ,			__lowercase						:						Optional[Any]=4096 ,			__lowercase						:						List[str]=16 ,			__lowercase						:						int=12 ,			__lowercase						:						Dict=4096 ,			__lowercase						:						int=16 ,			__lowercase						:						int=0.0 ,			__lowercase						:						Tuple=0.0 ,			__lowercase						:						Any=True ,			__lowercase						:						Tuple=True ,			__lowercase						:						List[str]="gelu" ,			__lowercase						:						Tuple=1024 ,			__lowercase						:						str=0.1 ,			__lowercase						:						str=0.0 ,			__lowercase						:						List[str]=0.0 ,			__lowercase						:						Dict=0.02 ,			__lowercase						:						Dict=0.0 ,			__lowercase						:						Union[str, Any]=False ,			__lowercase						:						Optional[int]=1 ,			__lowercase						:						Optional[Any]=0 ,			__lowercase						:						Optional[Any]=2 ,			__lowercase						:						int=2 ,			**__lowercase						:						Optional[Any] ,			):

											'''simple docstring'''
											__a					       =						vocab_size
											__a					       =						max_position_embeddings
											__a					       =						d_model
											__a					       =						encoder_ffn_dim
											__a					       =						encoder_layers
											__a					       =						encoder_attention_heads
											__a					       =						decoder_ffn_dim
											__a					       =						decoder_layers
											__a					       =						decoder_attention_heads
											__a					       =						dropout
											__a					       =						attention_dropout
											__a					       =						activation_dropout
											__a					       =						activation_function
											__a					       =						init_std
											__a					       =						encoder_layerdrop
											__a					       =						decoder_layerdrop
											__a					       =						classifier_dropout
											__a					       =						use_cache
											__a					       =						encoder_layers
											__a					       =						scale_embedding  # scale factor will be sqrt(d_model) if True
											super().__init__(
											    pad_token_id=__lowercase ,			bos_token_id=__lowercase ,			eos_token_id=__lowercase ,			is_encoder_decoder=__lowercase ,			forced_eos_token_id=__lowercase ,			**__lowercase ,			)






class 				SCREAMING_SNAKE_CASE     (							__A   ):





							@property
							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''

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

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

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

											'''simple docstring'''

											if self.task in ["default", "seq2seq-lm"]:
															__a					       =						super().outputs
											else:
															__a					       =						super(__lowercase ,			self		).outputs
															if self.use_past:
																			__a					       =						self.num_layers
																			for i in range(__lowercase		):
																							__a					       =						{0: "batch", 2: "past_sequence + sequence"}
																							__a					       =						{0: "batch", 2: "past_sequence + sequence"}
											return common_outputs





							def   UpperCamelCase_   (				self						:						Tuple ,			__lowercase						:						PreTrainedTokenizer ,			__lowercase						:						int = -1 ,			__lowercase						:						int = -1 ,			__lowercase						:						bool = False ,			__lowercase						:						Optional[TensorType] = None ,			):

											'''simple docstring'''
											__a					       =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __lowercase ,			__lowercase ,			__lowercase ,			__lowercase ,			__lowercase		)

											# Generate decoder inputs
											__a					       =						seq_length if not self.use_past else 1
											__a					       =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __lowercase ,			__lowercase ,			__lowercase ,			__lowercase ,			__lowercase		)
											__a					       =						{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
											__a					       =						dict(**__lowercase ,			**__lowercase		)

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

															__a					       =						torch.cat(
															    [common_inputs["""decoder_attention_mask"""], torch.ones(__lowercase ,			__lowercase		)] ,			dim=1		)

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

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





							def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						PreTrainedTokenizer ,			__lowercase						:						int = -1 ,			__lowercase						:						int = -1 ,			__lowercase						:						bool = False ,			__lowercase						:						Optional[TensorType] = None ,			):

											'''simple docstring'''
											__a					       =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __lowercase ,			__lowercase ,			__lowercase ,			__lowercase ,			__lowercase		)

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

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





							def   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						PreTrainedTokenizer ,			__lowercase						:						int = -1 ,			__lowercase						:						int = -1 ,			__lowercase						:						bool = False ,			__lowercase						:						Optional[TensorType] = None ,			):

											'''simple docstring'''
											__a					       =						compute_effective_axis_dimension(
											    __lowercase ,			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
											__a					       =						tokenizer.num_special_tokens_to_add(__lowercase		)
											__a					       =						compute_effective_axis_dimension(
											    __lowercase ,			fixed_dimension=OnnxConfig.default_fixed_sequence ,			num_token_to_add=__lowercase		)

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





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						PreTrainedTokenizer ,			__lowercase						:						int = -1 ,			__lowercase						:						int = -1 ,			__lowercase						:						bool = False ,			__lowercase						:						Optional[TensorType] = None ,			):

											'''simple docstring'''

											if self.task in ["default", "seq2seq-lm"]:
															__a					       =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
															    __lowercase ,			batch_size=__lowercase ,			seq_length=__lowercase ,			is_pair=__lowercase ,			framework=__lowercase		)

											elif self.task == "causal-lm":
															__a					       =						self._generate_dummy_inputs_for_causal_lm(
															    __lowercase ,			batch_size=__lowercase ,			seq_length=__lowercase ,			is_pair=__lowercase ,			framework=__lowercase		)
											else:
															__a					       =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    __lowercase ,			batch_size=__lowercase ,			seq_length=__lowercase ,			is_pair=__lowercase ,			framework=__lowercase		)

											return common_inputs







							def   UpperCamelCase_   (				self						:						str ,			__lowercase						:						Optional[Any] ,			__lowercase						:						Dict ,			__lowercase						:						Optional[int] ,			__lowercase						:						Union[str, Any]		):

											'''simple docstring'''

											if self.task in ["default", "seq2seq-lm"]:
															__a					       =						super()._flatten_past_key_values_(__lowercase ,			__lowercase ,			__lowercase ,			__lowercase		)
											else:
															__a					       =						super(__lowercase ,			self		)._flatten_past_key_values_(
															    __lowercase ,			__lowercase ,			__lowercase ,			__lowercase		)





# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase__     =	"""Update an existing config file with the latest defaults while maintaining the old configuration."""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







				__a					       =						args.config_file
				if config_file is None and Path(_SCREAMING_SNAKE_CASE      ).exists():
								__a					       =						default_config_file
				elif not Path(_SCREAMING_SNAKE_CASE      ).exists():
								raise ValueError(f"The passed config file located at {config_file} doesn't exist."      )
				__a					       =						load_config_from_file(_SCREAMING_SNAKE_CASE      )

				if config_file.endswith(""".json"""      ):
								config.to_json_file(_SCREAMING_SNAKE_CASE      )
				else:
								config.to_yaml_file(_SCREAMING_SNAKE_CASE      )
				return config_file

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     int      ):
				"""simple docstring"""







				__a					       =						parser.add_parser("""update"""			,       parents=_SCREAMING_SNAKE_CASE			,       help=_SCREAMING_SNAKE_CASE			,       formatter_class=_SCREAMING_SNAKE_CASE      )
				parser.add_argument(
				    """--config_file"""			,       default=_SCREAMING_SNAKE_CASE			,       help=(
				        """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
				        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
				        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
				        """with 'huggingface'."""
				    )			,       )

				parser.set_defaults(func=_SCREAMING_SNAKE_CASE      )
				return parser

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				__a					       =						update_config(_SCREAMING_SNAKE_CASE      )
				print(f"Sucessfully updated the configuration file at {config_file}."      )
