


"""simple docstring"""
import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('SDP_ENABLED') or is_sagemaker_dp_enabled():
				__SCREAMING_SNAKE_CASE				:     int		=     True
				__SCREAMING_SNAKE_CASE				:     str		=     'p3dn.24xlarge'
				import smdistributed.dataparallel.tensorflow as sdp
else:
				__SCREAMING_SNAKE_CASE				:     List[str]		=     False







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE=None					)     ->					int:
							snake_case_     							=		tqdm(_SCREAMING_SNAKE_CASE					)
							for i, batch in enumerate(_SCREAMING_SNAKE_CASE					):
														with tf.GradientTape() as tape:
																					snake_case_							,    snake_case_     							=		batch
																					snake_case_     							=		model(_SCREAMING_SNAKE_CASE					)
																					snake_case_     							=		loss(_SCREAMING_SNAKE_CASE					,			outputs.logits					)

														if SDP_ENABLED:
																					snake_case_     							=		sdp.DistributedGradientTape(_SCREAMING_SNAKE_CASE					,			sparse_as_dense=_SCREAMING_SNAKE_CASE					)

														snake_case_     							=		tape.gradient(_SCREAMING_SNAKE_CASE					,			model.trainable_variables					)
														opt.apply_gradients(zip(_SCREAMING_SNAKE_CASE					,			model.trainable_variables					)					)

														pbar.set_description(f"""Loss: {loss_value:.4f}"""					)

														if SDP_ENABLED and i == 0:
																					sdp.broadcast_variables(model.variables					,			root_rank=0					)
																					sdp.broadcast_variables(opt.variables()					,			root_rank=0					)

														if max_steps and i >= max_steps:
																					break

							snake_case_     							=		{"""loss""": loss_value.numpy()}
							return train_results







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					int:
							# Load dataset
							snake_case_							,    snake_case_     							=		load_dataset("""imdb"""					,			split=["""train""", """test"""]					)

							# Preprocess train dataset
							snake_case_     							=		train_dataset.map(
							    lambda _SCREAMING_SNAKE_CASE					: tokenizer(e["""text"""]					,			truncation=_SCREAMING_SNAKE_CASE					,			padding="""max_length"""					)					,			batched=_SCREAMING_SNAKE_CASE					)
							train_dataset.set_format(type="""tensorflow"""					,			columns=["""input_ids""", """attention_mask""", """label"""]					)

							snake_case_     							=		{
							    x: train_dataset[x].to_tensor(default_value=0					,			shape=[None, tokenizer.model_max_length]					)
							    for x in ["""input_ids""", """attention_mask"""]
							}
							snake_case_     							=		tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])					)

							# Preprocess test dataset
							snake_case_     							=		test_dataset.map(
							    lambda _SCREAMING_SNAKE_CASE					: tokenizer(e["""text"""]					,			truncation=_SCREAMING_SNAKE_CASE					,			padding="""max_length"""					)					,			batched=_SCREAMING_SNAKE_CASE					)
							test_dataset.set_format(type="""tensorflow"""					,			columns=["""input_ids""", """attention_mask""", """label"""]					)

							snake_case_     							=		{
							    x: test_dataset[x].to_tensor(default_value=0					,			shape=[None, tokenizer.model_max_length]					)
							    for x in ["""input_ids""", """attention_mask"""]
							}
							snake_case_     							=		tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])					)

							if SDP_ENABLED:
														snake_case_     							=		tf_train_dataset.shard(sdp.size()					,			sdp.rank()					)
														snake_case_     							=		tf_test_dataset.shard(sdp.size()					,			sdp.rank()					)
							snake_case_     							=		tf_train_dataset.batch(_SCREAMING_SNAKE_CASE					,			drop_remainder=_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		tf_test_dataset.batch(_SCREAMING_SNAKE_CASE					,			drop_remainder=_SCREAMING_SNAKE_CASE					)

							return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     int		=     argparse.ArgumentParser()

				# Hyperparameters sent by the client are passed as command-line arguments to the script.
				parser.add_argument('--epochs', type=int, default=3)
				parser.add_argument('--per_device_train_batch_size', type=int, default=16)
				parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
				parser.add_argument('--model_name_or_path', type=str)
				parser.add_argument('--learning_rate', type=str, default=5E-5)
				parser.add_argument('--do_train', type=bool, default=True)
				parser.add_argument('--do_eval', type=bool, default=True)
				parser.add_argument('--output_dir', type=str)
				parser.add_argument('--max_steps', type=int, default=None)

				# Data, model, and output directories
				parser.add_argument('--output_data_dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
				parser.add_argument('--model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
				parser.add_argument('--n_gpus', type=str, default=os.environ['SM_NUM_GPUS'])

				__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE				:     int		=     parser.parse_known_args()

				# Set up logging
				__SCREAMING_SNAKE_CASE				:     Optional[int]		=     logging.getLogger(__name__)

				logging.basicConfig(
				    level=logging.getLevelName('INFO'),
				    handlers=[logging.StreamHandler(sys.stdout)],
				    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
				)

				if SDP_ENABLED:
								sdp.init()

								__SCREAMING_SNAKE_CASE				:     Tuple		=     tf.config.experimental.list_physical_devices('GPU')
								for gpu in gpus:
												tf.config.experimental.set_memory_growth(gpu, True)
								if gpus:
												tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], 'GPU')

    # Load model and tokenizer
				__SCREAMING_SNAKE_CASE				:     int		=     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
				__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     AutoTokenizer.from_pretrained(args.model_name_or_path)

				# get datasets
				__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE				:     str		=     get_datasets(
				    tokenizer=tokenizer,
				    train_batch_size=args.per_device_train_batch_size,
				    eval_batch_size=args.per_device_eval_batch_size,
				)

				# fine optimizer and loss
				__SCREAMING_SNAKE_CASE				:     Optional[int]		=     tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
				__SCREAMING_SNAKE_CASE				:     str		=     tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
				__SCREAMING_SNAKE_CASE				:     int		=     [tf.keras.metrics.SparseCategoricalAccuracy()]
				model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

				# Training
				if args.do_train:
								# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
								__SCREAMING_SNAKE_CASE				:     Any		=     time.time()
								__SCREAMING_SNAKE_CASE				:     List[str]		=     fit(
								    model,
								    loss,
								    optimizer,
								    tf_train_dataset,
								    args.epochs,
								    args.per_device_train_batch_size,
								    max_steps=args.max_steps,
								)
								__SCREAMING_SNAKE_CASE				:     Optional[int]		=     time.time() - start_train_time
								logger.info('*** Train ***')
								logger.info(f"""train_runtime = {end_train_time}""")

								__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     os.path.join(args.output_dir, 'train_results.txt')

								if not SDP_ENABLED or sdp.rank() == 0:
												with open(output_eval_file, 'w') as writer:
																logger.info('***** Train results *****')
																logger.info(train_results)
																for key, value in train_results.items():
																				logger.info(f"""  {key} = {value}""")
																				writer.write(f"""{key} = {value}\n""")

    # Evaluation
				if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
								__SCREAMING_SNAKE_CASE				:     Any		=     model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
								logger.info('*** Evaluate ***')

								__SCREAMING_SNAKE_CASE				:     Dict		=     os.path.join(args.output_dir, 'eval_results.txt')

								with open(output_eval_file, 'w') as writer:
												logger.info('***** Eval results *****')
												logger.info(result)
												for key, value in result.items():
																logger.info(f"""  {key} = {value}""")
																writer.write(f"""{key} = {value}\n""")

    # Save result
				if SDP_ENABLED:
								if sdp.rank() == 0:
												model.save_pretrained(args.output_dir)
												tokenizer.save_pretrained(args.output_dir)
				else:
								model.save_pretrained(args.output_dir)
								tokenizer.save_pretrained(args.output_dir)







"""simple docstring"""
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


__SCREAMING_SNAKE_CASE				:     int		=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:     str		=     {
    'google/bigbird-pegasus-large-arxiv': (
        'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-pubmed': (
        'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-bigpatent': (
        'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}





class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		str				   =     """bigbird_pegasus"""
							__lowercase:		Any				   =     ["""past_key_values"""]
							__lowercase:		Optional[Any]				   =     {
							    """num_attention_heads""": """encoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """attention_probs_dropout_prob""": """attention_dropout""",
							}



							def __init__(					self				:   Optional[int]		,							UpperCAmelCase_				:   List[Any]=96_103		,							UpperCAmelCase_				:   List[str]=4_096		,							UpperCAmelCase_				:   Optional[Any]=16		,							UpperCAmelCase_				:   str=4_096		,							UpperCAmelCase_				:   Optional[int]=16		,							UpperCAmelCase_				:   List[Any]=16		,							UpperCAmelCase_				:   Optional[int]=4_096		,							UpperCAmelCase_				:   List[str]=16		,							UpperCAmelCase_				:   Tuple=0.0		,							UpperCAmelCase_				:   Union[str, Any]=0.0		,							UpperCAmelCase_				:   str=True		,							UpperCAmelCase_				:   Dict=True		,							UpperCAmelCase_				:   List[str]="gelu_new"		,							UpperCAmelCase_				:   Tuple=1_024		,							UpperCAmelCase_				:   int=0.1		,							UpperCAmelCase_				:   Union[str, Any]=0.0		,							UpperCAmelCase_				:   str=0.0		,							UpperCAmelCase_				:   str=0.02		,							UpperCAmelCase_				:   Tuple=2		,							UpperCAmelCase_				:   int=0.0		,							UpperCAmelCase_				:   str=True		,							UpperCAmelCase_				:   Any=0		,							UpperCAmelCase_				:   Dict=2		,							UpperCAmelCase_				:   List[str]=1		,							UpperCAmelCase_				:   Optional[int]="block_sparse"		,							UpperCAmelCase_				:   Tuple=64		,							UpperCAmelCase_				:   List[str]=3		,							UpperCAmelCase_				:   Optional[int]=False		,							**UpperCAmelCase_				:   str		,							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		vocab_size
														snake_case_     							=		max_position_embeddings
														snake_case_     							=		d_model
														snake_case_     							=		encoder_ffn_dim
														snake_case_     							=		encoder_layers
														snake_case_     							=		encoder_attention_heads
														snake_case_     							=		decoder_ffn_dim
														snake_case_     							=		decoder_layers
														snake_case_     							=		decoder_attention_heads
														snake_case_     							=		dropout
														snake_case_     							=		attention_dropout
														snake_case_     							=		activation_dropout
														snake_case_     							=		activation_function
														snake_case_     							=		init_std
														snake_case_     							=		encoder_layerdrop
														snake_case_     							=		decoder_layerdrop
														snake_case_     							=		classifier_dropout
														snake_case_     							=		use_cache
														snake_case_     							=		encoder_layers
														snake_case_     							=		scale_embedding  # scale factor will be sqrt(d_model) if True

														# extra config
														snake_case_     							=		attention_type
														snake_case_     							=		block_size
														snake_case_     							=		num_random_blocks
														snake_case_     							=		use_bias

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





class   __A  (snake_case__):





							'''simple docstring'''

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



														"""simple docstring"""



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

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

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



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)

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

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

																					snake_case_     							=		torch.cat(
																					    [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCAmelCase_		,							UpperCAmelCase_							)]		,							dim=1							)

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

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



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)

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

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



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		compute_effective_axis_dimension(
														    UpperCAmelCase_		,							fixed_dimension=OnnxConfig.default_fixed_batch		,							num_token_to_add=0							)

														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														snake_case_     							=		tokenizer.num_special_tokens_to_add(UpperCAmelCase_							)
														snake_case_     							=		compute_effective_axis_dimension(
														    UpperCAmelCase_		,							fixed_dimension=OnnxConfig.default_fixed_sequence		,							num_token_to_add=UpperCAmelCase_							)

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



							def 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



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

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

														return common_inputs



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Any							)       ->Tuple:



														"""simple docstring"""



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



