






# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

__magic_name__:					str							=     2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
__magic_name__:					Optional[Any]							=     {
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
__magic_name__:					List[str]							=     {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		__magic_name__:					Dict							=     "facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
		__magic_name__:					Optional[int]							=     "allenai"

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Tuple       						= dict((re.sub(R"""@@$""",   """""",   _A			), v) if k.endswith("""@@"""			) else (re.sub(R"""$""",   """</w>""",   _A			), v) for k, v in d.items()			)
						__magic_name__      :       List[str]       						= """<s> <pad> </s> <unk>""".split()
						# restore the special tokens
						for k in keep_keys:
												del da[f'{k}</w>']
												__magic_name__      :       Dict       						= d[k]  # restore
						return da

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						assert os.path.exists(_A			)
						os.makedirs(_A,   exist_ok=_A			)
						print(f'Writing results to {pytorch_dump_folder_path}'			)

						# handle various types of models

						__magic_name__      :       List[str]       						= basename(_A			)
						__magic_name__      :       Tuple       						= dirname(_A			)

						__magic_name__      :       List[Any]       						= fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
						__magic_name__      :       Tuple       						= cls.hub_models()
						__magic_name__      :       Optional[Any]       						= {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
						__magic_name__      :       Union[str, Any]       						= """."""
						# note: since the model dump is old, fairseq has upgraded its model some
						# time later, and it does a whole lot of rewrites and splits on the saved
						# weights, therefore we can't use torch.load() directly on the model file.
						# see: upgrade_state_dict(state_dict) in fairseq_model.py
						print(f'using checkpoint {checkpoint_file}'			)
						__magic_name__      :       Dict       						= hub_utils.from_pretrained(
						    _A,   _A,   _A,   archive_map=_A,   **_A			)

						__magic_name__      :       int       						= vars(chkpt["""args"""]["""model"""]			)

						__magic_name__      :       Tuple       						= args["""source_lang"""]
						__magic_name__      :       int       						= args["""target_lang"""]

						__magic_name__      :       int       						= dirname(_A			)
						__magic_name__      :       List[Any]       						= basename(_A			)

						# dicts
						__magic_name__      :       List[Any]       						= os.path.join(_A,   f'dict.{src_lang}.txt'			)
						__magic_name__      :       List[str]       						= os.path.join(_A,   f'dict.{tgt_lang}.txt'			)

						__magic_name__      :       Tuple       						= Dictionary.load(_A			)
						__magic_name__      :       List[str]       						= rewrite_dict_keys(src_dict.indices			)
						__magic_name__      :       str       						= len(_A			)
						__magic_name__      :       Tuple       						= os.path.join(_A,   """vocab-src.json"""			)
						print(f'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'			)
						with open(_A,   """w""",   encoding="""utf-8"""			) as f:
												f.write(json.dumps(_A,   ensure_ascii=_A,   indent=_A			)			)

						# detect whether this is a do_lower_case situation, which can be derived by checking whether we
						# have at least one uppercase letter in the source vocab
						__magic_name__      :       List[Any]       						= True
						for k in src_vocab.keys():
												if not k.islower():
																		__magic_name__      :       Optional[int]       						= False
																		break

						__magic_name__      :       List[str]       						= Dictionary.load(_A			)
						__magic_name__      :       List[str]       						= rewrite_dict_keys(tgt_dict.indices			)
						__magic_name__      :       int       						= len(_A			)
						__magic_name__      :       Dict       						= os.path.join(_A,   """vocab-tgt.json"""			)
						print(f'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'			)
						with open(_A,   """w""",   encoding="""utf-8"""			) as f:
												f.write(json.dumps(_A,   ensure_ascii=_A,   indent=_A			)			)

						# merges_file (bpecodes)
						__magic_name__      :       Tuple       						= os.path.join(_A,   VOCAB_FILES_NAMES["""merges_file"""]			)
						for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
												__magic_name__      :       Tuple       						= os.path.join(_A,   _A			)
												if os.path.exists(_A			):
																		break
						with open(_A,   encoding="""utf-8"""			) as fin:
												__magic_name__      :       List[str]       						= fin.read()
						__magic_name__      :       Any       						= re.sub(R""" \d+$""",   """""",   _A,   0,   re.M			)  # remove frequency number
						print(f'Generating {merges_file}'			)
						with open(_A,   """w""",   encoding="""utf-8"""			) as fout:
												fout.write(_A			)

						# model config
						__magic_name__      :       Optional[Any]       						= os.path.join(_A,   """config.json"""			)

						# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
						# may have to modify the tokenizer if a different type is used by a future model
						assert args["bpe"] == "fastbpe", f'need to extend tokenizer to support bpe={args["bpe"]}'
						assert args["tokenizer"] == "moses", f'need to extend tokenizer to support bpe={args["tokenizer"]}'

						__magic_name__      :       List[Any]       						= {
						    """architectures""": ["""FSMTForConditionalGeneration"""],
						    """model_type""": """fsmt""",
						    """activation_dropout""": args["""activation_dropout"""],
						    """activation_function""": """relu""",
						    """attention_dropout""": args["""attention_dropout"""],
						    """d_model""": args["""decoder_embed_dim"""],
						    """dropout""": args["""dropout"""],
						    """init_std""": 0.02,
						    """max_position_embeddings""": args["""max_source_positions"""],
						    """num_hidden_layers""": args["""encoder_layers"""],
						    """src_vocab_size""": src_vocab_size,
						    """tgt_vocab_size""": tgt_vocab_size,
						    """langs""": [src_lang, tgt_lang],
						    """encoder_attention_heads""": args["""encoder_attention_heads"""],
						    """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
						    """encoder_layerdrop""": args["""encoder_layerdrop"""],
						    """encoder_layers""": args["""encoder_layers"""],
						    """decoder_attention_heads""": args["""decoder_attention_heads"""],
						    """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
						    """decoder_layerdrop""": args["""decoder_layerdrop"""],
						    """decoder_layers""": args["""decoder_layers"""],
						    """bos_token_id""": 0,
						    """pad_token_id""": 1,
						    """eos_token_id""": 2,
						    """is_encoder_decoder""": True,
						    """scale_embedding""": not args["""no_scale_embedding"""],
						    """tie_word_embeddings""": args["""share_all_embeddings"""],
						}

						# good hparam defaults to start with
						__magic_name__      :       Union[str, Any]       						= 5
						__magic_name__      :       Tuple       						= False
						if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
												__magic_name__      :       Tuple       						= best_score_hparams[model_dir]["""length_penalty"""]
						else:
												__magic_name__      :       List[str]       						= 1.0

						print(f'Generating {fsmt_model_config_file}'			)
						with open(_A,   """w""",   encoding="""utf-8"""			) as f:
												f.write(json.dumps(_A,   ensure_ascii=_A,   indent=_A			)			)

						# tokenizer config
						__magic_name__      :       Tuple       						= os.path.join(_A,   _A			)

						__magic_name__      :       Optional[int]       						= {
						    """langs""": [src_lang, tgt_lang],
						    """model_max_length""": 1024,
						    """do_lower_case""": do_lower_case,
						}

						print(f'Generating {fsmt_tokenizer_config_file}'			)
						with open(_A,   """w""",   encoding="""utf-8"""			) as f:
												f.write(json.dumps(_A,   ensure_ascii=_A,   indent=_A			)			)

						# model
						__magic_name__      :       Dict       						= chkpt["""models"""][0]
						__magic_name__      :       Any       						= model.state_dict()

						# rename keys to start with 'model.'
						__magic_name__      :       Optional[int]       						= OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()			)

						# remove unneeded keys
						__magic_name__      :       Optional[Any]       						= [
						    """model.model""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """model.encoder_embed_tokens.weight""",
						    """model.decoder_embed_tokens.weight""",
						    """model.encoder.embed_positions._float_tensor""",
						    """model.decoder.embed_positions._float_tensor""",
						]
						for k in ignore_keys:
												model_state_dict.pop(_A,   _A			)

						__magic_name__      :       Optional[int]       						= FSMTConfig.from_pretrained(_A			)
						__magic_name__      :       List[Any]       						= FSMTForConditionalGeneration(_A			)

						# check that it loads ok
						model_new.load_state_dict(_A,   strict=_A			)

						# save
						__magic_name__      :       Union[str, Any]       						= os.path.join(_A,   _A			)
						print(f'Generating {pytorch_weights_dump_path}'			)
						torch.save(_A,   _A			)

						print("""Conversion is done!"""			)
						print("""\nLast step is to upload the files to s3"""			)
						print(f'cd {data_root}'			)
						print(f'transformers-cli upload {model_dir}'			)


if __name__ == "__main__":
		__magic_name__:					Optional[int]							=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--fsmt_checkpoint_path",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
		        " bpecodes, etc."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__magic_name__:					str							=     parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)













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

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


__magic_name__:					Any							=     logging.get_logger(__name__)

__magic_name__:					Dict							=     {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Any											=   '''blenderbot-small'''
	lowercase__						:					Optional[int]											=   ['''past_key_values''']
	lowercase__						:					Dict											=   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
	def __init__(    self      ,       lowerCAmelCase__=5_02_65      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=8      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=16      ,       lowerCAmelCase__=8      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=16      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=0      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=2      ,       **lowerCAmelCase__      ,       )							->				Tuple:
							__magic_name__      :       Tuple       						= vocab_size
							__magic_name__      :       List[str]       						= max_position_embeddings
							__magic_name__      :       Union[str, Any]       						= d_model
							__magic_name__      :       Optional[int]       						= encoder_ffn_dim
							__magic_name__      :       Union[str, Any]       						= encoder_layers
							__magic_name__      :       List[str]       						= encoder_attention_heads
							__magic_name__      :       List[Any]       						= decoder_ffn_dim
							__magic_name__      :       str       						= decoder_layers
							__magic_name__      :       List[str]       						= decoder_attention_heads
							__magic_name__      :       Union[str, Any]       						= dropout
							__magic_name__      :       Tuple       						= attention_dropout
							__magic_name__      :       List[Any]       						= activation_dropout
							__magic_name__      :       List[Any]       						= activation_function
							__magic_name__      :       Optional[int]       						= init_std
							__magic_name__      :       Dict       						= encoder_layerdrop
							__magic_name__      :       Union[str, Any]       						= decoder_layerdrop
							__magic_name__      :       Optional[int]       						= use_cache
							__magic_name__      :       List[Any]       						= encoder_layers
							__magic_name__      :       Dict       						= scale_embedding  # scale factor will be sqrt(d_model) if True

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





class 	snake_case__ (							_lowerCAmelCase				):
	@property
	def       __magic_name__    (    self			)							->				Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       Optional[int]       						= OrderedDict(
													    [
													        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
													        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
													    ]			)

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

													if self.use_past:
																			self.fill_with_past_key_values_(lowerCAmelCase__      ,       direction="""inputs"""			)
							elif self.task == "causal-lm":
													# TODO: figure this case out.
													__magic_name__      :       Union[str, Any]       						= OrderedDict(
													    [
													        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
													        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
													    ]			)
													if self.use_past:
																			__magic_name__						,__magic_name__      :       Dict       						= self.num_layers
																			for i in range(lowerCAmelCase__			):
																									__magic_name__      :       Dict       						= {0: """batch""", 2: """past_sequence + sequence"""}
																									__magic_name__      :       int       						= {0: """batch""", 2: """past_sequence + sequence"""}
							else:
													__magic_name__      :       Union[str, 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       __magic_name__    (    self			)							->				Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       Any       						= super().outputs
							else:
													__magic_name__      :       int       						= super(lowerCAmelCase__      ,       self			).outputs
													if self.use_past:
																			__magic_name__						,__magic_name__      :       str       						= self.num_layers
																			for i in range(lowerCAmelCase__			):
																									__magic_name__      :       Tuple       						= {0: """batch""", 2: """past_sequence + sequence"""}
																									__magic_name__      :       Dict       						= {0: """batch""", 2: """past_sequence + sequence"""}
							return common_outputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       )							->				Mapping[str, Any]:
							__magic_name__      :       Tuple       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

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

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

													__magic_name__      :       List[str]       						= torch.cat(
													    [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase__      ,       lowerCAmelCase__			)]      ,       dim=1			)

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

													for _ in range(lowerCAmelCase__			):
																			common_inputs["past_key_values"].append(
																			    (
																			        torch.zeros(lowerCAmelCase__			),
																			        torch.zeros(lowerCAmelCase__			),
																			        torch.zeros(lowerCAmelCase__			),
																			        torch.zeros(lowerCAmelCase__			),
																			    )			)
													# TODO: test this.
													__magic_name__      :       Union[str, Any]       						= encoder_shape if remaining_side_name == """encoder""" else decoder_shape
													for _ in range(lowerCAmelCase__      ,       lowerCAmelCase__			):
																			common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase__			), torch.zeros(lowerCAmelCase__			))			)
							return common_inputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       )							->				Mapping[str, Any]:
							__magic_name__      :       int       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

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

													__magic_name__      :       Optional[int]       						= common_inputs["""attention_mask"""].dtype
													__magic_name__      :       Optional[Any]       						= torch.cat(
													    [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase__      ,       lowerCAmelCase__      ,       dtype=lowerCAmelCase__			)]      ,       dim=1			)
													__magic_name__      :       Tuple       						= [
													    (torch.zeros(lowerCAmelCase__			), torch.zeros(lowerCAmelCase__			)) for _ in range(lowerCAmelCase__			)
													]
							return common_inputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       )							->				Mapping[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
							__magic_name__      :       Tuple       						= compute_effective_axis_dimension(
							    lowerCAmelCase__      ,       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
							__magic_name__      :       str       						= tokenizer.num_special_tokens_to_add(lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= compute_effective_axis_dimension(
							    lowerCAmelCase__      ,       fixed_dimension=OnnxConfig.default_fixed_sequence      ,       num_token_to_add=lowerCAmelCase__			)

							# Generate dummy inputs according to compute batch and sequence
							__magic_name__      :       List[Any]       						= [""" """.join([tokenizer.unk_token]			) * seq_length] * batch_size
							__magic_name__      :       List[str]       						= dict(tokenizer(lowerCAmelCase__      ,       return_tensors=lowerCAmelCase__			)			)
							return common_inputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       )							->				Mapping[str, Any]:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       Tuple       						= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    lowerCAmelCase__      ,       batch_size=lowerCAmelCase__      ,       seq_length=lowerCAmelCase__      ,       is_pair=lowerCAmelCase__      ,       framework=lowerCAmelCase__			)

							elif self.task == "causal-lm":
													__magic_name__      :       str       						= self._generate_dummy_inputs_for_causal_lm(
													    lowerCAmelCase__      ,       batch_size=lowerCAmelCase__      ,       seq_length=lowerCAmelCase__      ,       is_pair=lowerCAmelCase__      ,       framework=lowerCAmelCase__			)
							else:
													__magic_name__      :       Dict       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCAmelCase__      ,       batch_size=lowerCAmelCase__      ,       seq_length=lowerCAmelCase__      ,       is_pair=lowerCAmelCase__      ,       framework=lowerCAmelCase__			)

							return common_inputs







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Dict:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       List[Any]       						= super()._flatten_past_key_values_(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
							else:
													__magic_name__      :       Tuple       						= super(lowerCAmelCase__      ,       self			)._flatten_past_key_values_(
													    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
