




import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   int:
   snake_case      :				int				=       filter(lambda lowercase     : p.requires_grad						,model.parameters()     )
   snake_case      :				List[str]				=       sum([np.prod(p.size()     ) for p in model_parameters]     )
   return params


lowerCamelCase       :       str        =    logging.getLogger(__name__)




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase     )  ->   Dict:



   if metric == "rouge2":
      snake_case      :				Tuple				=       """{val_avg_rouge2:.4f}-{step_count}"""
   elif metric == "bleu":
      snake_case      :				List[Any]				=       """{val_avg_bleu:.4f}-{step_count}"""
   elif metric == "em":
      snake_case      :				Tuple				=       """{val_avg_em:.4f}-{step_count}"""
   elif metric == "loss":
      snake_case      :				Dict				=       """{val_avg_loss:.4f}-{step_count}"""
   else:
      raise NotImplementedError(
          f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
          """ function."""     )

   snake_case      :				List[str]				=       ModelCheckpoint(
       dirpath=lowercase						,filename=lowercase						,monitor=f"""val_{metric}"""						,mode="""max"""						,save_top_k=1						,every_n_epochs=1						,)

   return checkpoint_callback




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase     )  ->   Optional[int]:
   return EarlyStopping(
       monitor=f"""val_{metric}"""						,mode="""min""" if """loss""" in metric else """max"""						,patience=lowercase						,verbose=lowercase						,)




class 						__lowercase		(pl.Callback							):




      """simple docstring"""





      def 	UpperCAmelCase      (      self				,      A				,      A	)		->				List[Any]:
         snake_case      :				Optional[int]				=       {f"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
         pl_module.logger.log_metrics(__lowerCamelCase	)

      @rank_zero_only
      def 	UpperCAmelCase      (      self				,      A				,      A				,      A				,      A=True	)		->				Optional[Any]:
         logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""	)
         snake_case      :				str				=       trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}	)
         # Log results
         snake_case      :				Union[str, Any]				=       Path(pl_module.hparams.output_dir	)
         if type_path == "test":
            snake_case      :				Dict				=       od / """test_results.txt"""
            snake_case      :				str				=       od / """test_generations.txt"""
         else:
            # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
            # If people want this it will be easy enough to add back.
            snake_case      :				Any				=       od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
            snake_case      :				List[str]				=       od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
            results_file.parent.mkdir(exist_ok=__lowerCamelCase	)
            generations_file.parent.mkdir(exist_ok=__lowerCamelCase	)
         with open(__lowerCamelCase				,      """a+"""	) as writer:
            for key in sorted(__lowerCamelCase	):
               if key in ["log", "progress_bar", "preds"]:
                  continue
               snake_case      :				int				=       metrics[key]
               if isinstance(__lowerCamelCase				,      torch.Tensor	):
                  snake_case      :				Any				=       val.item()
               snake_case      :				Union[str, Any]				=       f"""{key}: {val:.6f}\n"""
               writer.write(__lowerCamelCase	)

         if not save_generations:
            return

         if "preds" in metrics:
            snake_case      :				Any				=       """\n""".join(metrics["""preds"""]	)
            generations_file.open("""w+"""	).write(__lowerCamelCase	)

      @rank_zero_only
      def 	UpperCAmelCase      (      self				,      A				,      A	)		->				Optional[int]:
         try:
            snake_case      :				Union[str, Any]				=       pl_module.model.model.num_parameters()
         except AttributeError:
            snake_case      :				int				=       pl_module.model.num_parameters()

         snake_case      :				Union[str, Any]				=       count_trainable_parameters(__lowerCamelCase	)
         # mp stands for million parameters
         trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6}	)

      @rank_zero_only
      def 	UpperCAmelCase      (      self				,      A				,      A	)		->				Union[str, Any]:
         save_json(pl_module.metrics				,      pl_module.metrics_save_path	)
         return self._write_logs(__lowerCamelCase				,      __lowerCamelCase				,      """test"""	)

      @rank_zero_only
      def 	UpperCAmelCase      (      self				,      A				,      A	)		->				Union[str, Any]:
         save_json(pl_module.metrics				,      pl_module.metrics_save_path	)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")













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_ :						Dict      =      logging.get_logger(__name__)

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

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Optional[int]     	=  """layoutlmv3"""





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Optional[Any]=50_265 ,  __lowerCamelCase				:					Dict=768 ,  __lowerCamelCase				:					Any=12 ,  __lowerCamelCase				:					int=12 ,  __lowerCamelCase				:					str=3_072 ,  __lowerCamelCase				:					List[Any]="gelu" ,  __lowerCamelCase				:					List[str]=0.1 ,  __lowerCamelCase				:					Optional[int]=0.1 ,  __lowerCamelCase				:					Optional[Any]=512 ,  __lowerCamelCase				:					Optional[int]=2 ,  __lowerCamelCase				:					Union[str, Any]=0.02 ,  __lowerCamelCase				:					Union[str, Any]=1E-5 ,  __lowerCamelCase				:					Any=1 ,  __lowerCamelCase				:					Optional[int]=0 ,  __lowerCamelCase				:					List[Any]=2 ,  __lowerCamelCase				:					Dict=1_024 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=128 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					str=32 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=64 ,  __lowerCamelCase				:					List[str]=256 ,  __lowerCamelCase				:					Dict=True ,  __lowerCamelCase				:					List[Any]=True ,  __lowerCamelCase				:					Tuple=True ,  __lowerCamelCase				:					Tuple=224 ,  __lowerCamelCase				:					Tuple=3 ,  __lowerCamelCase				:					Dict=16 ,  __lowerCamelCase				:					Union[str, Any]=None ,  **__lowerCamelCase				:					Optional[Any] ,  ):
		super().__init__(
		    vocab_size=__lowerCamelCase ,  hidden_size=__lowerCamelCase ,  num_hidden_layers=__lowerCamelCase ,  num_attention_heads=__lowerCamelCase ,  intermediate_size=__lowerCamelCase ,  hidden_act=__lowerCamelCase ,  hidden_dropout_prob=__lowerCamelCase ,  attention_probs_dropout_prob=__lowerCamelCase ,  max_position_embeddings=__lowerCamelCase ,  type_vocab_size=__lowerCamelCase ,  initializer_range=__lowerCamelCase ,  layer_norm_eps=__lowerCamelCase ,  pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :int          =	max_ad_position_embeddings
		UpperCamelCase     :Tuple          =	coordinate_size
		UpperCamelCase     :List[Any]          =	shape_size
		UpperCamelCase     :Union[str, Any]          =	has_relative_attention_bias
		UpperCamelCase     :Any          =	rel_pos_bins
		UpperCamelCase     :Optional[Any]          =	max_rel_pos
		UpperCamelCase     :str          =	has_spatial_attention_bias
		UpperCamelCase     :Tuple          =	rel_ad_pos_bins
		UpperCamelCase     :Optional[int]          =	max_rel_ad_pos
		UpperCamelCase     :Tuple          =	text_embed
		UpperCamelCase     :str          =	visual_embed
		UpperCamelCase     :Optional[Any]          =	input_size
		UpperCamelCase     :str          =	num_channels
		UpperCamelCase     :List[Any]          =	patch_size
		UpperCamelCase     :Optional[Any]          =	classifier_dropout

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     int     	=  version.parse("""1.12"""      )





	@property
	def 						_A (		self				:					Optional[int]	):
		# The order of inputs is different for question answering and sequence classification
		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				:					str	):
		return 1E-5





	@property
	def 						_A (		self				:					Dict	):
		return 12





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					"ProcessorMixin" ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional["TensorType"] = None ,  __lowerCamelCase				:					int = 3 ,  __lowerCamelCase				:					int = 40 ,  __lowerCamelCase				:					int = 40 ,  ):
		setattr(processor.image_processor ,  """apply_ocr""" ,  __lowerCamelCase	)

		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		UpperCamelCase     :Optional[Any]          =	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
		UpperCamelCase     :Optional[int]          =	processor.tokenizer.num_special_tokens_to_add(__lowerCamelCase	)
		UpperCamelCase     :int          =	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
		UpperCamelCase     :Any          =	[[""" """.join([processor.tokenizer.unk_token]	) * seq_length]] * batch_size

		# Generate dummy bounding boxes
		UpperCamelCase     :Optional[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)
		UpperCamelCase     :List[str]          =	self._generate_dummy_images(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)

		UpperCamelCase     :Any          =	dict(
		    processor(
		        __lowerCamelCase ,  text=__lowerCamelCase ,  boxes=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  )	)

		return inputs
