from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__UpperCamelCase	: Union[str, Any]    		=  logging.get_logger(__name__)


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset

					from transformers import Trainer





					class       lowercase__		(     UpperCamelCase_):


											def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       int = 101		):





																	'''simple docstring'''





																	SCREAMING_SNAKE_CASE							:					Dict           =							length


											def __len__( self			:       Dict		):





																	'''simple docstring'''





																	return self.length



											def __getitem__( self			:       int					,	UpperCamelCase__			:       str		):





																	'''simple docstring'''





																	return i







					class       lowercase__		:


											def __call__( self			:       Dict					,	UpperCamelCase__			:       List[Any]		):





																	'''simple docstring'''





																	return {"input_ids": torch.tensor(UpperCamelCase__		), "labels": torch.tensor(UpperCamelCase__		)}







					class       lowercase__		(     nn.Module):


											def __init__( self			:       List[str]		):





																	'''simple docstring'''





																	super().__init__()
																	# Add some (unused) params otherwise DDP will complain.
																	SCREAMING_SNAKE_CASE							:					Dict           =							nn.Linear(120					,	80		)



											def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Any=None		):





																	'''simple docstring'''





																	if labels is not None:
																							return torch.tensor(0.0					,	device=input_ids.device		), input_ids
																	else:
																							return input_ids





class       lowercase__		(     UpperCamelCase_):


						@require_torch_neuroncore
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							f"""--output_dir {output_dir}""".split()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							['''torchrun'''] + distributed_args + args
												execute_subprocess_async(UpperCamelCase__					,	env=self.get_env()		)
												# successful return here == success - any errors would have caused an error in the sub-call







class       lowercase__		(     UpperCamelCase_):


						@require_torch_multi_gpu
						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
												SCREAMING_SNAKE_CASE							:					Any           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					List[Any]           =							f"""--output_dir {output_dir}""".split()
												SCREAMING_SNAKE_CASE							:					str           =							['''torchrun'''] + distributed_args + args
												execute_subprocess_async(UpperCamelCase__					,	env=self.get_env()		)
												# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
					# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
					#
					# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

					__UpperCamelCase	: Any    		=  HfArgumentParser((TrainingArguments,))
					__UpperCamelCase	: Tuple    		=  parser.parse_args_into_dataclasses()[0]

					logger.warning(
					    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
					    f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
					)

					# Essentially, what we want to verify in the distributed case is that we get all samples back,
					# in the right order. (this is crucial for prediction for instance)
					for dataset_length in [101, 40, 7]:
										__UpperCamelCase	: List[str]    		=  DummyDataset(dataset_length)
										def    A			(					_lowercase		):
																SCREAMING_SNAKE_CASE							:					Optional[int]           =							list(range(len(_lowercase		)		)		)
																SCREAMING_SNAKE_CASE							:					List[str]           =							p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																if not success and training_args.local_rank == 0:
																						logger.warning(
																						    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
																						    f"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""		)
																return {"success": success}

										__UpperCamelCase	: Any    		=  Trainer(
										    model=DummyModel(),
										    args=training_args,
										    data_collator=DummyDataCollator(),
										    eval_dataset=dataset,
										    compute_metrics=compute_metrics,
										)
										__UpperCamelCase	: str    		=  trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										__UpperCamelCase	: Tuple    		=  trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)

										__UpperCamelCase	: Any    		=  2

										__UpperCamelCase	: Any    		=  trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										__UpperCamelCase	: Optional[int]    		=  trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)



										__UpperCamelCase	: List[str]    		=  None






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

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


__UpperCamelCase	: str    		=  logging.get_logger(__name__)

__UpperCamelCase	: str    		=  {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""layoutlmv3"""


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]=5_0265					,	UpperCamelCase__			:       List[Any]=768					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Tuple=3072					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       List[Any]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Any=512					,	UpperCamelCase__			:       List[Any]=2					,	UpperCamelCase__			:       List[Any]=0.02					,	UpperCamelCase__			:       Optional[Any]=1E-5					,	UpperCamelCase__			:       List[str]=1					,	UpperCamelCase__			:       int=0					,	UpperCamelCase__			:       str=2					,	UpperCamelCase__			:       List[str]=1024					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       Tuple=True					,	UpperCamelCase__			:       Optional[int]=32					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Optional[Any]=64					,	UpperCamelCase__			:       Dict=256					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       Dict=True					,	UpperCamelCase__			:       Dict=224					,	UpperCamelCase__			:       Optional[Any]=3					,	UpperCamelCase__			:       Any=16					,	UpperCamelCase__			:       Any=None					,	**UpperCamelCase__			:       Optional[Any]					,	):





												'''simple docstring'''





												super().__init__(
												    vocab_size=UpperCamelCase__					,	hidden_size=UpperCamelCase__					,	num_hidden_layers=UpperCamelCase__					,	num_attention_heads=UpperCamelCase__					,	intermediate_size=UpperCamelCase__					,	hidden_act=UpperCamelCase__					,	hidden_dropout_prob=UpperCamelCase__					,	attention_probs_dropout_prob=UpperCamelCase__					,	max_position_embeddings=UpperCamelCase__					,	type_vocab_size=UpperCamelCase__					,	initializer_range=UpperCamelCase__					,	layer_norm_eps=UpperCamelCase__					,	pad_token_id=UpperCamelCase__					,	bos_token_id=UpperCamelCase__					,	eos_token_id=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_ad_position_embeddings
												SCREAMING_SNAKE_CASE							:					List[Any]           =							coordinate_size
												SCREAMING_SNAKE_CASE							:					List[str]           =							shape_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							has_relative_attention_bias
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rel_pos_bins
												SCREAMING_SNAKE_CASE							:					str           =							max_rel_pos
												SCREAMING_SNAKE_CASE							:					Any           =							has_spatial_attention_bias
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rel_ad_pos_bins
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_rel_ad_pos
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							text_embed
												SCREAMING_SNAKE_CASE							:					List[str]           =							visual_embed
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_size
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							num_channels
												SCREAMING_SNAKE_CASE							:					List[Any]           =							patch_size
												SCREAMING_SNAKE_CASE							:					List[Any]           =							classifier_dropout






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	version.parse("""1.12""")


						@property
						def  __A ( self			:       str		):





												'''simple docstring'''





												if self.task in ["question-answering", "sequence-classification"]:
																		return OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
																		    ]		)
												else:
																		return OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
																		    ]		)


						@property
						def  __A ( self			:       int		):





												'''simple docstring'''





												return 1E-5


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       "ProcessorMixin"					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	):





												'''simple docstring'''





												setattr(processor.image_processor					,	'''apply_ocr'''					,	UpperCamelCase__		)

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					Any           =							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
												SCREAMING_SNAKE_CASE							:					List[Any]           =							processor.tokenizer.num_special_tokens_to_add(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							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
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[[''' '''.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												SCREAMING_SNAKE_CASE							:					Any           =							[[[48, 84, 73, 128]]] * batch_size

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self._generate_dummy_images(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict(
												    processor(
												        UpperCamelCase__					,	text=UpperCamelCase__					,	boxes=UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	)		)

												return inputs





