import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

__a  :					List[Any]          =  logging.getLogger()
__a  :					str          =  torch.cuda.is_available()
__a  :					Tuple          =  {
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 4_2,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				__lowercase			      =							'''\n'''.join(lowercase							)
				Path(lowercase							).open('''w'''							).writelines(lowercase							)


__a  :					Union[str, Any]          =  [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__a  :					Any          =  ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__a  :					Optional[int]          =  """patrickvonplaten/t5-tiny-random"""
__a  :					Optional[Any]          =  """sshleifer/t5-tinier-random"""
__a  :					Optional[Any]          =  """sshleifer/bart-tiny-random"""
__a  :					str          =  """sshleifer/tiny-mbart"""
__a  :					Optional[Any]          =  """sshleifer/tiny-marian-en-de"""
__a  :					Dict          =  """stas/tiny-wmt19-en-de"""


__a  :					str          =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(lowercase							,      F"{split}.source"							)							,      lowercase							)
								_dump_articles(os.path.join(lowercase							,      F"{split}.target"							)							,      lowercase							)
				return tmp_dir




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls						)					-> Dict:

							'''simple docstring'''


							logging.disable(logging.CRITICAL						)  # remove noisy download output from tracebacks
							return cls





			@slow
			@require_torch_gpu
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							__lowercase			      =							list_models()
							__lowercase			      =							'''sshleifer'''
							__lowercase			      =							[x.modelId for x in model_list if x.modelId.startswith(lowerCAmelCase__						)]
							__lowercase			      =							['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
							__lowercase			      =							[]
							for m in model_ids:
											if m in allowed_to_be_broken:
															continue
											try:
															AutoConfig.from_pretrained(lowerCAmelCase__						)
											except Exception:
															failures.append(lowerCAmelCase__						)
							assert not failures, F"The following models could not be loaded through AutoConfig: {failures}"





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
							self._test_distiller_cli(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							{
							    '''student_encoder_layers''': 2,
							    '''student_decoder_layers''': 1,
							    '''max_epochs''': 4,
							    '''val_check_interval''': 0.25,
							    '''alpha_hid''': 2.0,
							    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
							}
							__lowercase			      =							self._test_distiller_cli(lowerCAmelCase__				,       check_contents=lowerCAmelCase__						)

							__lowercase			      =							list(Path(model.output_dir						).glob('''*.ckpt'''						)						)
							self.assertEqual(1				,       len(lowerCAmelCase__						)						)
							__lowercase			      =							list(Path(model.output_dir						).glob('''**/*.bin'''						)						)
							self.assertEqual(len(lowerCAmelCase__						)				,       2						)
							__lowercase			      =							lmap(str.strip				,       Path(model.hparams.data_dir						).joinpath('''test.source'''						).open().readlines()						)
							__lowercase			      =							tempfile.mktemp()  # XXX: not being cleaned up
							generate_summaries_or_translations(lowerCAmelCase__				,       lowerCAmelCase__				,       str(model.output_dir / '''best_tfmr'''						)						)
							self.assertTrue(Path(lowerCAmelCase__						).exists()						)

							__lowercase			      =							self.get_auto_remove_tmp_dir()
							convert_pl_to_hf(ckpts[0]				,       transformer_ckpts[0].parent				,       lowerCAmelCase__						)
							assert os.path.exists(os.path.join(lowerCAmelCase__				,       '''pytorch_model.bin'''						)						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__						)
							__lowercase      ,					__lowercase			      =							model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
							__lowercase			      =							torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]				,       dtype=torch.long				,       device=model.device						)
							__lowercase			      =							target_ids[:, :-1].contiguous()  # Why this line?
							__lowercase			      =							target_ids[:, 1:].clone()  # why clone?
							__lowercase			      =							model(
							    lowerCAmelCase__				,       attention_mask=lowerCAmelCase__				,       decoder_input_ids=lowerCAmelCase__				,       labels=lowerCAmelCase__				,       use_cache=lowerCAmelCase__						).loss

							__lowercase			      =							model(lowerCAmelCase__				,       attention_mask=lowerCAmelCase__				,       decoder_input_ids=lowerCAmelCase__				,       use_cache=lowerCAmelCase__						).logits

							__lowercase			      =							nn.functional.log_softmax(lowerCAmelCase__				,       dim=-1						)
							__lowercase      ,					__lowercase			      =							label_smoothed_nll_loss(
							    lowerCAmelCase__				,       lowerCAmelCase__				,       0.1				,       ignore_index=model.config.pad_token_id						)
							with self.assertRaises(lowerCAmelCase__						):
											# TODO: understand why this breaks
											self.assertEqual(lowerCAmelCase__				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							{
							    '''student_encoder_layers''': 2,
							    '''student_decoder_layers''': 1,
							    '''num_train_epochs''': 4,
							    '''val_check_interval''': 0.25,
							    '''alpha_hid''': 2.0,
							    '''task''': '''translation''',
							    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
							    '''tokenizer_name''': MBART_TINY,
							    '''teacher''': MBART_TINY,
							    '''src_lang''': '''en_XX''',
							    '''tgt_lang''': '''ro_RO''',
							}
							__lowercase			      =							self._test_distiller_cli(lowerCAmelCase__				,       check_contents=lowerCAmelCase__						)
							assert model.model.config.model_type == "mbart"

							__lowercase			      =							list(Path(model.output_dir						).glob('''*.ckpt'''						)						)
							self.assertEqual(1				,       len(lowerCAmelCase__						)						)
							__lowercase			      =							list(Path(model.output_dir						).glob('''**/*.bin'''						)						)
							__lowercase			      =							list(Path(model.output_dir						).glob('''best_tfmr/*'''						)						)
							assert len(lowerCAmelCase__						) > 2
							self.assertEqual(len(lowerCAmelCase__						)				,       2						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							{
							    '''student_encoder_layers''': 1,
							    '''student_decoder_layers''': 1,
							    '''alpha_hid''': 2.0,
							    '''teacher''': T5_TINY,
							    '''model_name_or_path''': T5_TINY,
							    '''tokenizer_name''': T5_TINY,
							}
							self._test_distiller_cli(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							__lowercase			      =							{
							    '''teacher''': T5_TINY,
							    '''student''': T5_TINIER,
							    '''model_name_or_path''': T5_TINIER,
							    '''tokenizer_name''': T5_TINIER,
							}
							self._test_distiller_cli(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=True						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							{
							    '''label_smoothing''': 0.0,
							    '''early_stopping_patience''': -1,
							    '''train_batch_size''': 1,
							    '''eval_batch_size''': 2,
							    '''max_epochs''': 2,
							    '''alpha_mlm''': 0.2,
							    '''alpha_ce''': 0.8,
							    '''do_predict''': True,
							    '''model_name_or_path''': '''sshleifer/tinier_bart''',
							    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
							    '''val_check_interval''': 0.5,
							}
							default_updates.update(lowerCAmelCase__						)
							__lowercase			      =							CHEAP_ARGS.copy()
							__lowercase			      =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
							__lowercase			      =							self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=lowerCAmelCase__				,       output_dir=lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							distill_main(argparse.Namespace(**lowerCAmelCase__						)						)
							if not check_contents:
											return model
							__lowercase			      =							os.listdir(lowerCAmelCase__						)
							__lowercase			      =							{os.path.basename(lowerCAmelCase__						) for p in contents}
							__lowercase			      =							[p for p in contents if p.endswith('''ckpt'''						)]
							assert len(lowerCAmelCase__						) > 0

							self.assertIn('''test_generations.txt'''				,       lowerCAmelCase__						)
							self.assertIn('''test_results.txt'''				,       lowerCAmelCase__						)

							__lowercase			      =							load_json(model.metrics_save_path						)
							__lowercase			      =							metrics['''val'''][-1]
							self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']				,       0.01						)
							self.assertGreaterEqual(1.0				,       last_step_stats['''val_avg_gen_time''']						)
							self.assertIsInstance(last_step_stats[F"val_avg_{model.val_metric}"]				,       lowerCAmelCase__						)
							__lowercase			      =							int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1						)
							self.assertEqual(len(metrics['''val''']						)				,       lowerCAmelCase__						)
							self.assertEqual(len(metrics['''test''']						)				,       1						)
							return model




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			@parameterized.expand(
			    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]				,       )
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							__lowercase			      =							CHEAP_ARGS.copy()
							__lowercase			      =							'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
							__lowercase			      =							0.1 if task == '''translation''' else 0

							__lowercase			      =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
							__lowercase			      =							self.get_auto_remove_tmp_dir()
							args_d.update(
							    data_dir=lowerCAmelCase__				,       model_name_or_path=lowerCAmelCase__				,       tokenizer_name=lowerCAmelCase__				,       train_batch_size=2				,       eval_batch_size=2				,       output_dir=lowerCAmelCase__				,       do_predict=lowerCAmelCase__				,       task=lowerCAmelCase__				,       src_lang='''en_XX'''				,       tgt_lang='''ro_RO'''				,       freeze_encoder=lowerCAmelCase__				,       freeze_embeds=lowerCAmelCase__				,       )
							assert "n_train" in args_d
							__lowercase			      =							argparse.Namespace(**lowerCAmelCase__						)
							__lowercase			      =							main(lowerCAmelCase__						)

							__lowercase			      =							module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
											__lowercase			      =							module.model.lm_head
											assert not lm_head.weight.requires_grad
											assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
											__lowercase			      =							module.model.model
											__lowercase			      =							fsmt.decoder.embed_positions
											assert not embed_pos.weight.requires_grad
											assert not fsmt.decoder.embed_tokens.weight.requires_grad
											# check that embeds are not the same
											assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
							else:
											__lowercase			      =							module.model.model
											__lowercase			      =							bart.decoder.embed_positions
											assert not embed_pos.weight.requires_grad
											assert not bart.shared.weight.requires_grad
											# check that embeds are the same
											assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
											assert bart.decoder.embed_tokens == bart.shared

							__lowercase			      =							load_json(module.output_dir / '''text_batch.json'''						)
							assert isinstance(lowerCAmelCase__				,       lowerCAmelCase__						)
							assert len(lowerCAmelCase__						) >= 4





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							CHEAP_ARGS.copy()

							__lowercase			      =							'''summarization'''
							__lowercase			      =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

							args_d.update(
							    data_dir=lowerCAmelCase__				,       tokenizer_name=lowerCAmelCase__				,       train_batch_size=2				,       eval_batch_size=2				,       do_predict=lowerCAmelCase__				,       task=lowerCAmelCase__				,       src_lang='''en_XX'''				,       tgt_lang='''ro_RO'''				,       freeze_encoder=lowerCAmelCase__				,       freeze_embeds=lowerCAmelCase__				,       )

							# test models whose config includes the extra_model_args
							__lowercase			      =							BART_TINY
							__lowercase			      =							self.get_auto_remove_tmp_dir()
							__lowercase			      =							args_d.copy()
							args_da.update(
							    model_name_or_path=lowerCAmelCase__				,       output_dir=lowerCAmelCase__				,       )
							__lowercase			      =							('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
							for p in extra_model_params:
											__lowercase			      =							0.5
							__lowercase			      =							argparse.Namespace(**lowerCAmelCase__						)
							__lowercase			      =							main(lowerCAmelCase__						)
							for p in extra_model_params:
											assert getattr(model.config				,       lowerCAmelCase__						) == 0.5, F"failed to override the model config for param {p}"

							# test models whose config doesn't include the extra_model_args
							__lowercase			      =							T5_TINY
							__lowercase			      =							self.get_auto_remove_tmp_dir()
							__lowercase			      =							args_d.copy()
							args_da.update(
							    model_name_or_path=lowerCAmelCase__				,       output_dir=lowerCAmelCase__				,       )
							__lowercase			      =							'''encoder_layerdrop'''
							__lowercase			      =							0.5
							__lowercase			      =							argparse.Namespace(**lowerCAmelCase__						)
							with pytest.raises(lowerCAmelCase__						) as excinfo:
											__lowercase			      =							main(lowerCAmelCase__						)
							assert str(excinfo.value						) == F"model config doesn't have a `{unsupported_param}` attribute"





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							CHEAP_ARGS.copy()

							__lowercase			      =							'''summarization'''
							__lowercase			      =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

							__lowercase			      =							BART_TINY
							__lowercase			      =							self.get_auto_remove_tmp_dir()

							args_d.update(
							    data_dir=lowerCAmelCase__				,       model_name_or_path=lowerCAmelCase__				,       output_dir=lowerCAmelCase__				,       tokenizer_name=lowerCAmelCase__				,       train_batch_size=2				,       eval_batch_size=2				,       do_predict=lowerCAmelCase__				,       task=lowerCAmelCase__				,       src_lang='''en_XX'''				,       tgt_lang='''ro_RO'''				,       freeze_encoder=lowerCAmelCase__				,       freeze_embeds=lowerCAmelCase__				,       )

							# emulate finetune.py
							__lowercase			      =							argparse.ArgumentParser()
							__lowercase			      =							pl.Trainer.add_argparse_args(lowerCAmelCase__						)
							__lowercase			      =							SummarizationModule.add_model_specific_args(lowerCAmelCase__				,       os.getcwd()						)
							__lowercase			      =							{'''--help''': True}

							# --help test
							with pytest.raises(lowerCAmelCase__						) as excinfo:
											with CaptureStdout() as cs:
															__lowercase			      =							parser.parse_args(lowerCAmelCase__						)
											assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							__lowercase			      =							lightning_base.arg_to_scheduler_metavar
							assert expected in cs.out, "--help is expected to list the supported schedulers"

							# --lr_scheduler=non_existing_scheduler test
							__lowercase			      =							'''non_existing_scheduler'''
							__lowercase			      =							{F"--lr_scheduler={unsupported_param}"}
							with pytest.raises(lowerCAmelCase__						) as excinfo:
											with CaptureStderr() as cs:
															__lowercase			      =							parser.parse_args(lowerCAmelCase__						)
											assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							__lowercase			      =							F"invalid choice: '{unsupported_param}'"
							assert expected in cs.err, F"should have bailed on invalid choice of scheduler {unsupported_param}"

							# --lr_scheduler=existing_scheduler test
							__lowercase			      =							'''cosine'''
							__lowercase			      =							args_d.copy()
							__lowercase			      =							supported_param
							__lowercase			      =							argparse.Namespace(**lowerCAmelCase__						)
							__lowercase			      =							main(lowerCAmelCase__						)
							assert (
							    getattr(model.hparams				,       '''lr_scheduler'''						) == supported_param
							), F"lr_scheduler={supported_param} shouldn't fail"
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__a  :					int          =  logging.get_logger(__name__)

__a  :					Optional[Any]          =  {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''layoutlm'''





			def __init__(       self				,       lowerCAmelCase__=3_05_22				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=12				,       lowerCAmelCase__=12				,       lowerCAmelCase__=30_72				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=2				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=1E-12				,       lowerCAmelCase__=0				,       lowerCAmelCase__="absolute"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=10_24				,       **lowerCAmelCase__				,       )					-> Tuple:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							vocab_size
							__lowercase			      =							hidden_size
							__lowercase			      =							num_hidden_layers
							__lowercase			      =							num_attention_heads
							__lowercase			      =							hidden_act
							__lowercase			      =							intermediate_size
							__lowercase			      =							hidden_dropout_prob
							__lowercase			      =							attention_probs_dropout_prob
							__lowercase			      =							max_position_embeddings
							__lowercase			      =							type_vocab_size
							__lowercase			      =							initializer_range
							__lowercase			      =							layer_norm_eps
							__lowercase			      =							position_embedding_type
							__lowercase			      =							use_cache
							__lowercase			      =							max_ad_position_embeddings




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = "default"				,       lowerCAmelCase__ = None				,       )					-> str:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__				,       task=lowerCAmelCase__				,       patching_specs=lowerCAmelCase__						)
							__lowercase			      =							config.max_ad_position_embeddings - 1





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

							'''simple docstring'''


							return OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
							        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
							        ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
							    ]						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							super().generate_dummy_inputs(
							    lowerCAmelCase__				,       batch_size=lowerCAmelCase__				,       seq_length=lowerCAmelCase__				,       is_pair=lowerCAmelCase__				,       framework=lowerCAmelCase__						)

							# Generate a dummy bbox
							__lowercase			      =							[48, 84, 73, 1_28]

							if not framework == TensorType.PYTORCH:
											raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''						)

							if not is_torch_available():
											raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''						)
							import torch

							__lowercase      ,					__lowercase			      =							input_dict['''input_ids'''].shape
							__lowercase			      =							torch.tensor([*[box] * seq_length]						).tile(lowerCAmelCase__				,       1				,       1						)
							return input_dict