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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__				:      Tuple    		=     logging.get_logger(__name__)

a__				:      List[Any]    		=     {
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}






class     a_       (			snake_case__							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Union[str, Any]						=  'm2m_100'
       __SCREAMING_SNAKE_CASE       :					Union[str, Any]						=  ['past_key_values']
       __SCREAMING_SNAKE_CASE       :					Tuple						=  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






       def __init__(		self				,       _lowerCamelCase=12_8112				,       _lowerCamelCase=1024				,       _lowerCamelCase=12				,       _lowerCamelCase=4096				,       _lowerCamelCase=16				,       _lowerCamelCase=12				,       _lowerCamelCase=4096				,       _lowerCamelCase=16				,       _lowerCamelCase=0.0_5				,       _lowerCamelCase=0.0_5				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase="relu"				,       _lowerCamelCase=1024				,       _lowerCamelCase=0.1				,       _lowerCamelCase=0.1				,       _lowerCamelCase=0.0				,       _lowerCamelCase=0.0_2				,       _lowerCamelCase=2				,       _lowerCamelCase=True				,       _lowerCamelCase=1				,       _lowerCamelCase=0				,       _lowerCamelCase=2				,       **_lowerCamelCase				,       )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Dict				      = vocab_size
           SCREAMING_SNAKE_CASE				:				List[Any]				      = max_position_embeddings
           SCREAMING_SNAKE_CASE				:				Tuple				      = d_model
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = encoder_ffn_dim
           SCREAMING_SNAKE_CASE				:				str				      = encoder_layers
           SCREAMING_SNAKE_CASE				:				Dict				      = encoder_attention_heads
           SCREAMING_SNAKE_CASE				:				int				      = decoder_ffn_dim
           SCREAMING_SNAKE_CASE				:				List[str]				      = decoder_layers
           SCREAMING_SNAKE_CASE				:				List[Any]				      = decoder_attention_heads
           SCREAMING_SNAKE_CASE				:				str				      = dropout
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = attention_dropout
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = activation_dropout
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = activation_function
           SCREAMING_SNAKE_CASE				:				Tuple				      = init_std
           SCREAMING_SNAKE_CASE				:				str				      = encoder_layerdrop
           SCREAMING_SNAKE_CASE				:				List[str]				      = decoder_layerdrop
           SCREAMING_SNAKE_CASE				:				List[str]				      = use_cache
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = encoder_layers
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = scale_embedding  # scale factor will be sqrt(d_model) if True

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






class     a_       (			snake_case__							):



       """simple docstring"""






       @property
       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				int				      = OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]   )

           if self.use_past:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {0: '''batch'''}
               SCREAMING_SNAKE_CASE				:				Tuple				      = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
           else:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {0: '''batch''', 1: '''decoder_sequence'''}
               SCREAMING_SNAKE_CASE				:				Any				      = {0: '''batch''', 1: '''decoder_sequence'''}

           if self.use_past:
               self.fill_with_past_key_values_(lowercase_				,       direction='''inputs'''   )
           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->Union[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
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = compute_effective_axis_dimension(
               lowercase_				,       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				:				Tuple				      = tokenizer.num_special_tokens_to_add(lowercase_   )
           SCREAMING_SNAKE_CASE				:				str				      = compute_effective_axis_dimension(
               lowercase_				,       fixed_dimension=OnnxConfig.default_fixed_sequence				,       num_token_to_add=lowercase_   )

           # Generate dummy inputs according to compute batch and sequence
           SCREAMING_SNAKE_CASE				:				List[Any]				      = [''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
           SCREAMING_SNAKE_CASE				:				List[Any]				      = dict(tokenizer(lowercase_				,       return_tensors=lowercase_   )   )
           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_   )

           # Generate decoder inputs
           SCREAMING_SNAKE_CASE				:				List[Any]				      = seq_length if not self.use_past else 1
           SCREAMING_SNAKE_CASE				:				List[str]				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dict(**lowercase_				,       **lowercase_   )

           if self.use_past:
               if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
               else:
                   import torch
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = common_inputs['''input_ids'''].shape
               SCREAMING_SNAKE_CASE				:				Any				      = common_inputs['''decoder_input_ids'''].shape[1]
               SCREAMING_SNAKE_CASE				:				List[str]				      = self.num_attention_heads
               SCREAMING_SNAKE_CASE				:				Tuple				      = (
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               SCREAMING_SNAKE_CASE				:				int				      = decoder_seq_length + 3
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = (
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               SCREAMING_SNAKE_CASE				:				Dict				      = torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_				,       lowercase_   )]				,       dim=1   )

               SCREAMING_SNAKE_CASE				:				int				      = []
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               SCREAMING_SNAKE_CASE				:				Tuple				      = self.num_layers
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = min(lowercase_				,       lowercase_   )
               SCREAMING_SNAKE_CASE				:				Any				      = max(lowercase_				,       lowercase_   ) - min_num_layers
               SCREAMING_SNAKE_CASE				:				Tuple				      = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

               for _ in range(lowercase_   ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(lowercase_   ),
                           torch.zeros(lowercase_   ),
                           torch.zeros(lowercase_   ),
                           torch.zeros(lowercase_   ),
                       )   )
               # TODO: test this.
               SCREAMING_SNAKE_CASE				:				List[str]				      = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
               for _ in range(lowercase_				,       lowercase_   ):
                   common_inputs["past_key_values"].append((torch.zeros(lowercase_   ), torch.zeros(lowercase_   ))   )
           return common_inputs
       __SCREAMING_SNAKE_CASE       :					List[str]						=  _generate_dummy_inputs_for_default_and_seqaseq_lm



"""simple docstring"""





import argparse
import json
import re
import string
import sys

import numpy as np


_SCREAMING_SNAKE_CASE							:	str										=			0.5



def 							_lowerCAmelCase   (    UpperCAmelCase  :				str   ,				UpperCAmelCase  :				Optional[int]					):


					'''simple docstring'''






					UpperCamelCase__		:     Optional[Any]								=['''.''', ''',''', ''';''', ''':''']

					for token in remove_tokens:
										UpperCamelCase__		:     Optional[int]								=ground_truth.replace(UpperCAmelCase   ,				''''''					)
										UpperCamelCase__		:     List[Any]								=prediction.replace(UpperCAmelCase   ,				''''''					)

					UpperCamelCase__	,      UpperCamelCase__		:     List[Any]								=ground_truth.lower(), prediction.lower()
					UpperCamelCase__	,      UpperCamelCase__		:     Dict								=ground_truth.replace('''/'''   ,				''' '''					), prediction.replace('''/'''   ,				''' '''					)
					UpperCamelCase__	,      UpperCamelCase__		:     Any								=set(ground_truth.split(''' '''					)					), set(prediction.split(''' '''					)					)

					UpperCamelCase__		:     Optional[Any]								=ground_truth.intersection(UpperCAmelCase					)
					UpperCamelCase__		:     Optional[Any]								=ground_truth.union(UpperCAmelCase					)
					UpperCamelCase__		:     str								=len(UpperCAmelCase					) / len(UpperCAmelCase					)
					return jaccard



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]					):


					'''simple docstring'''






					def remove_articles(UpperCAmelCase  :				int					):
										return re.sub(r'''\b(a|an|the)\b'''   ,				''' '''   ,				UpperCAmelCase					)

					def white_space_fix(UpperCAmelCase  :				Union[str, Any]					):
										return " ".join(text.split()					)

					def remove_punc(UpperCAmelCase  :				Optional[int]					):
										UpperCamelCase__		:     Dict								=set(string.punctuation					)
										return "".join(ch for ch in text if ch not in exclude					)

					def lower(UpperCAmelCase  :				Optional[Any]					):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase					)					)					)					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				str   ,				UpperCAmelCase  :				Any					):


					'''simple docstring'''






					UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__		:     Optional[Any]								=0, 0, 0

					UpperCamelCase__		:     int								='''Parties''' in qa_id

					# first check if ground truth is empty
					if len(UpperCAmelCase					) == 0:
										if len(UpperCAmelCase					) > 0:
															fp += len(UpperCAmelCase					)  # false positive for each one
					else:
										for ground_truth in ground_truths:
															assert len(UpperCAmelCase					) > 0
															# check if there is a match
															UpperCamelCase__		:     Union[str, Any]								=False
															for pred in predictions:
																				if substr_ok:
																									UpperCamelCase__		:     List[str]								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH or ground_truth in pred
																				else:
																									UpperCamelCase__		:     Union[str, Any]								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH
																				if is_match:
																									UpperCamelCase__		:     Dict								=True

															if match_found:
																				tp += 1
															else:
																				fn += 1

        # now also get any fps by looping through preds
										for pred in predictions:
															# Check if there's a match. if so, don't count (don't want to double count based on the above)
															# but if there's no match, then this is a false positive.
															# (Note: we get the true positives in the above loop instead of this loop so that we don't double count
															# multiple predictions that are matched with the same answer.)
															UpperCamelCase__		:     Any								=False
															for ground_truth in ground_truths:
																				assert len(UpperCAmelCase					) > 0
																				if substr_ok:
																									UpperCamelCase__		:     Union[str, Any]								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH or ground_truth in pred
																				else:
																									UpperCamelCase__		:     Dict								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH
																				if is_match:
																									UpperCamelCase__		:     str								=True

															if not match_found:
																				fp += 1

					UpperCamelCase__		:     Union[str, Any]								=tp / (tp + fp) if tp + fp > 0 else np.nan
					UpperCamelCase__		:     Optional[int]								=tp / (tp + fn) if tp + fn > 0 else np.nan

					return precision, recall



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[Any]					):


					'''simple docstring'''






					UpperCamelCase__		:     Optional[Any]								=precisions[::-1]
					for i in range(1   ,				len(UpperCAmelCase					)					):
										UpperCamelCase__		:     int								=max(precision_best[i - 1]   ,				precision_best[i]					)
					UpperCamelCase__		:     str								=precision_best[::-1]
					return precisions



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				Any					):


					'''simple docstring'''






					UpperCamelCase__		:     int								=process_precisions(UpperCAmelCase					)
					UpperCamelCase__		:     str								=np.trapz(UpperCAmelCase   ,				UpperCAmelCase					)
					if np.isnan(UpperCAmelCase					):
										return 0
					return aupr



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				int   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     List[Any]								=process_precisions(UpperCAmelCase					)
					UpperCamelCase__		:     Optional[int]								=0
					for prec, recall in zip(UpperCAmelCase   ,				UpperCAmelCase					):
										if recall >= recall_thresh:
															UpperCamelCase__		:     Any								=prec
															break
					return prec_at_recall



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Any   ,				UpperCAmelCase  :				Dict					):


					'''simple docstring'''






					return normalize_answer(UpperCAmelCase					) == normalize_answer(UpperCAmelCase					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     List[str]								=0
					for pred in predictions:
										for ground_truth in ground_truths:
															UpperCamelCase__		:     str								=metric_fn(UpperCAmelCase   ,				UpperCAmelCase					)
															if score == 1:  # break the loop when one prediction matches the ground truth
																				break
										if score == 1:
															break
					return score



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[int]   ,				UpperCAmelCase  :				str					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=0
					UpperCamelCase__		:     List[Any]								=[]
					UpperCamelCase__		:     Optional[Any]								=[]
					for article in dataset:
										for paragraph in article["paragraphs"]:
															for qa in paragraph["qas"]:
																				total += 1
																				if qa["id"] not in predictions:
																									UpperCamelCase__		:     Union[str, Any]								='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																									print(UpperCAmelCase   ,				file=sys.stderr					)
																									continue
																				UpperCamelCase__		:     Union[str, Any]								=[x['''text'''] for x in qa['''answers''']]
																				UpperCamelCase__		:     Any								=predictions[qa['''id''']]
																				UpperCamelCase__	,      UpperCamelCase__		:     int								=compute_precision_recall(UpperCAmelCase   ,				UpperCAmelCase   ,				qa['''id''']					)

																				precisions.append(UpperCAmelCase					)
																				recalls.append(UpperCAmelCase					)

																				if precision == 0 and recall == 0:
																									fa += 0
																				else:
																									fa += 2 * (precision * recall) / (precision + recall)

																				exact_match += metric_max_over_ground_truths(UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase					)

					UpperCamelCase__		:     Union[str, Any]								=[x for _, x in sorted(zip(UpperCAmelCase   ,				UpperCAmelCase					)					)]
					recalls.sort()

					UpperCamelCase__		:     Optional[int]								=100.0 * fa / total
					UpperCamelCase__		:     List[Any]								=100.0 * exact_match / total
					UpperCamelCase__		:     Dict								=get_aupr(UpperCAmelCase   ,				UpperCAmelCase					)

					UpperCamelCase__		:     Optional[Any]								=get_prec_at_recall(UpperCAmelCase   ,				UpperCAmelCase   ,				recall_thresh=0.9					)
					UpperCamelCase__		:     Dict								=get_prec_at_recall(UpperCAmelCase   ,				UpperCAmelCase   ,				recall_thresh=0.8					)

					return {
					    "exact_match": exact_match,
					    "f1": fa,
					    "aupr": aupr,
					    "prec_at_80_recall": prec_at_aa_recall,
					    "prec_at_90_recall": prec_at_aa_recall,
					}


if __name__ == "__main__":
				_SCREAMING_SNAKE_CASE							:	str										=			argparse.ArgumentParser(description="""Evaluation for CUAD""")
				parser.add_argument("""dataset_file""", help="""Dataset file""")
				parser.add_argument("""prediction_file""", help="""Prediction File""")
				_SCREAMING_SNAKE_CASE							:	Dict										=			parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								_SCREAMING_SNAKE_CASE							:	str										=			json.load(dataset_file)
								_SCREAMING_SNAKE_CASE							:	Tuple										=			dataset_json["""data"""]
				with open(args.prediction_file) as prediction_file:
								_SCREAMING_SNAKE_CASE							:	Dict										=			json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))


