






import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


__magic_name__:					Any							=     trt.Logger(trt.Logger.WARNING)
__magic_name__:					Union[str, Any]							=     absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__magic_name__:					Union[str, Any]							=     logging.getLogger(__name__)

__magic_name__:					Any							=     argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    "--onnx_model_path",
    default=None,
    type=str,
    required=True,
    help="Path to ONNX model: ",
)

parser.add_argument(
    "--output_dir",
    default=None,
    type=str,
    required=True,
    help="The output directory where the model checkpoints and predictions will be written.",
)

# Other parameters

parser.add_argument(
    "--tokenizer_name",
    default="",
    type=str,
    required=True,
    help="Pretrained tokenizer name or path if not the same as model_name",
)

parser.add_argument(
    "--version_2_with_negative",
    action="store_true",
    help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
    "--null_score_diff_threshold",
    type=float,
    default=0.0,
    help="If null_score - best_non_null is greater than the threshold predict null.",
)

parser.add_argument(
    "--max_seq_length",
    default=384,
    type=int,
    help=(
        "The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded."
    ),
)
parser.add_argument(
    "--doc_stride",
    default=128,
    type=int,
    help="When splitting up a long document into chunks, how much stride to take between chunks.",
)

parser.add_argument("--per_device_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.")

parser.add_argument(
    "--n_best_size",
    default=20,
    type=int,
    help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
    "--max_answer_length",
    default=30,
    type=int,
    help=(
        "The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another."
    ),
)

parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")

parser.add_argument(
    "--dataset_name",
    type=str,
    default=None,
    required=True,
    help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--dataset_config_name",
    type=str,
    default=None,
    help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
)
parser.add_argument("--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument(
    "--fp16",
    action="store_true",
    help="Whether to use 16-bit (mixed) precision instead of 32-bit",
)
parser.add_argument(
    "--int8",
    action="store_true",
    help="Whether to use INT8",
)

__magic_name__:					List[Any]							=     parser.parse_args()

if args.tokenizer_name:
		__magic_name__:					Optional[int]							=     AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
		raise ValueError(
		    "You are instantiating a new tokenizer from scratch. This is not supported by this script."
		    "You can do it from another script, save it, and load it from here, using --tokenizer_name."
		)

logger.info("Training/evaluation parameters %s", args)

__magic_name__:					Optional[int]							=     args.per_device_eval_batch_size

__magic_name__:					Optional[Any]							=     (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__magic_name__:					Union[str, Any]							=     True

__magic_name__:					Optional[int]							=     "temp_engine/bert-fp32.engine"
if args.fpaa:
		__magic_name__:					Any							=     "temp_engine/bert-fp16.engine"
if args.inta:
		__magic_name__:					Tuple							=     "temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("temp_engine"):
		os.makedirs("temp_engine")

__magic_name__:					Union[str, Any]							=     1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
		with open(args.onnx_model_path, "rb") as model:
				if not parser.parse(model.read()):
						for error in range(parser.num_errors):
								print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
		__magic_name__:					Any							=     [network.get_input(i) for i in range(network.num_inputs)]
		__magic_name__:					List[str]							=     [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

		with builder.create_builder_config() as config:
				__magic_name__:					int							=     1 << 50
				if STRICT_TYPES:
						config.set_flag(trt.BuilderFlag.STRICT_TYPES)
				if args.fpaa:
						config.set_flag(trt.BuilderFlag.FPaa)
				if args.inta:
						config.set_flag(trt.BuilderFlag.INTa)
				__magic_name__:					List[str]							=     builder.create_optimization_profile()
				config.add_optimization_profile(profile)
				for i in range(len(input_names)):
						profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
				__magic_name__:					str							=     builder.build_engine(network, config)

				# serialize_engine and store in file (can be directly loaded and deserialized):
				with open(engine_name, "wb") as f:
						f.write(engine.serialize())

def    UpperCamelCase						(	_A,   _A,   _A,   _A,   _A,   _A,   _A,   _A			):






						"""simple docstring"""







						__magic_name__      :       Any       						= np.asarray(inputs["""input_ids"""],   dtype=np.intaa			)
						__magic_name__      :       str       						= np.asarray(inputs["""attention_mask"""],   dtype=np.intaa			)
						__magic_name__      :       Tuple       						= np.asarray(inputs["""token_type_ids"""],   dtype=np.intaa			)

						# Copy inputs
						cuda.memcpy_htod_async(d_inputs[0],   input_ids.ravel(),   _A			)
						cuda.memcpy_htod_async(d_inputs[1],   attention_mask.ravel(),   _A			)
						cuda.memcpy_htod_async(d_inputs[2],   token_type_ids.ravel(),   _A			)
						# start time
						__magic_name__      :       Optional[int]       						= time.time()
						# Run inference
						context.execute_async(
						    bindings=[int(_A			) for d_inp in d_inputs] + [int(_A			), int(_A			)],   stream_handle=stream.handle			)
						# Transfer predictions back from GPU
						cuda.memcpy_dtoh_async(_A,   _A,   _A			)
						cuda.memcpy_dtoh_async(_A,   _A,   _A			)
						# Synchronize the stream and take time
						stream.synchronize()
						# end time
						__magic_name__      :       Tuple       						= time.time()
						__magic_name__      :       int       						= end_time - start_time
						__magic_name__      :       List[str]       						= (h_outputa, h_outputa)
						# print(outputs)
						return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__magic_name__:					Union[str, Any]							=     Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_info()
else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
		set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		__magic_name__:					Tuple							=     load_dataset(args.dataset_name, args.dataset_config_name)
else:
		raise ValueError("Evaluation requires a dataset name")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

__magic_name__:					Union[str, Any]							=     raw_datasets["validation"].column_names

__magic_name__:					str							=     "question" if "question" in column_names else column_names[0]
__magic_name__:					str							=     "context" if "context" in column_names else column_names[1]
__magic_name__:					Tuple							=     "answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__magic_name__:					str							=     tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
		logger.warning(
		    F"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
		    F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
		)

__magic_name__:					Optional[Any]							=     min(args.max_seq_length, tokenizer.model_max_length)

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Dict       						= [q.lstrip() for q in examples[question_column_name]]

						# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
						# in one example possible giving several features when a context is long, each of those features having a
						# context that overlaps a bit the context of the previous feature.
						__magic_name__      :       Tuple       						= tokenizer(
						    examples[question_column_name if pad_on_right else context_column_name],   examples[context_column_name if pad_on_right else question_column_name],   truncation="""only_second""" if pad_on_right else """only_first""",   max_length=_A,   stride=args.doc_stride,   return_overflowing_tokens=_A,   return_offsets_mapping=_A,   padding="""max_length""",   )

						# Since one example might give us several features if it has a long context, we need a map from a feature to
						# its corresponding example. This key gives us just that.
						__magic_name__      :       List[str]       						= tokenized_examples.pop("""overflow_to_sample_mapping"""			)

						# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
						# corresponding example_id and we will store the offset mappings.
						__magic_name__      :       Tuple       						= []

						for i in range(len(tokenized_examples["""input_ids"""]			)			):
												# Grab the sequence corresponding to that example (to know what is the context and what is the question).
												__magic_name__      :       List[str]       						= tokenized_examples.sequence_ids(_A			)
												__magic_name__      :       List[Any]       						= 1 if pad_on_right else 0

												# One example can give several spans, this is the index of the example containing this span of text.
												__magic_name__      :       int       						= sample_mapping[i]
												tokenized_examples["example_id"].append(examples["""id"""][sample_index]			)

												# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
												# position is part of the context or not.
												__magic_name__      :       Optional[int]       						= [
												    (o if sequence_ids[k] == context_index else None)
												    for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]			)
												]

						return tokenized_examples


__magic_name__:					Optional[Any]							=     raw_datasets["validation"]
# Validation Feature Creation
__magic_name__:					Any							=     eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

__magic_name__:					Union[str, Any]							=     default_data_collator

__magic_name__:					Any							=     eval_dataset.remove_columns(["example_id", "offset_mapping"])
__magic_name__:					Union[str, Any]							=     DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)

def    UpperCamelCase						(	_A,   _A,   _A,   _A="eval"			):






						"""simple docstring"""







						__magic_name__      :       List[str]       						= postprocess_qa_predictions(
						    examples=_A,   features=_A,   predictions=_A,   version_2_with_negative=args.version_2_with_negative,   n_best_size=args.n_best_size,   max_answer_length=args.max_answer_length,   null_score_diff_threshold=args.null_score_diff_threshold,   output_dir=args.output_dir,   prefix=_A,   )
						# Format the result to the format the metric expects.
						if args.version_2_with_negative:
												__magic_name__      :       int       						= [
												    {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
												]
						else:
												__magic_name__      :       List[Any]       						= [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

						__magic_name__      :       Any       						= [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
						return EvalPrediction(predictions=_A,   label_ids=_A			)


__magic_name__:					Optional[Any]							=     load_metric("squad_v2" if args.version_2_with_negative else "squad")

# Evaluation!
logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
		# setup for TRT inferrence
		for i in range(len(input_names)):
				context.set_binding_shape(i, INPUT_SHAPE)
		assert context.all_binding_shapes_specified

		def    UpperCamelCase						(	_A			):






								"""simple docstring"""







								return trt.volume(engine.get_binding_shape(_A			)			) * engine.get_binding_dtype(_A			).itemsize

		# Allocate device memory for inputs and outputs.
		__magic_name__:					List[Any]							=     [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

		# Allocate output buffer
		__magic_name__:					str							=     cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
		__magic_name__:					Optional[int]							=     cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
		__magic_name__:					List[str]							=     cuda.mem_alloc(h_outputa.nbytes)
		__magic_name__:					Any							=     cuda.mem_alloc(h_outputa.nbytes)

		# Create a stream in which to copy inputs/outputs and run inference.
		__magic_name__:					int							=     cuda.Stream()

		# Evaluation
		logger.info("***** Running Evaluation *****")
		logger.info(F"""  Num examples = {len(eval_dataset)}""")
		logger.info(F"""  Batch size = {args.per_device_eval_batch_size}""")

		__magic_name__:					str							=     0.0
		__magic_name__:					Any							=     0
		__magic_name__:					str							=     timeit.default_timer()

		__magic_name__:					Tuple							=     None
		for step, batch in enumerate(eval_dataloader):
				__magic_name__		,						__magic_name__:					List[str]							=     model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
				total_time += infer_time
				niter += 1

				__magic_name__		,						__magic_name__:					Tuple							=     outputs
				__magic_name__:					Optional[int]							=     torch.tensor(start_logits)
				__magic_name__:					List[str]							=     torch.tensor(end_logits)

				# necessary to pad predictions and labels for being gathered
				__magic_name__:					str							=     accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
				__magic_name__:					List[str]							=     accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

				__magic_name__:					List[str]							=     (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
				__magic_name__:					Optional[int]							=     logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

		if all_preds is not None:
				__magic_name__:					Dict							=     nested_truncate(all_preds, len(eval_dataset))

		__magic_name__:					Dict							=     timeit.default_timer() - start_time
		logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
		# Inference time from TRT
		logger.info("Average Inference Time = {:.3f} ms".format(total_time * 1_000 / niter))
		logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1_000))

		logger.info("Total Number of Inference =  %d", niter)

__magic_name__:					Optional[Any]							=     post_processing_function(eval_examples, eval_dataset, all_preds)
__magic_name__:					int							=     metric.compute(predictions=prediction.predictions, references=prediction.label_ids)

logger.info(F"""Evaluation metrics: {eval_metric}""")








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__			)
