


"""simple docstring"""







import datasets

from .evaluate import evaluate


a__    :							Union[str, Any]            =					"""\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

a__    :							List[str]            =					"""
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

a__    :							Optional[int]            =					"""
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,  _KWARGS_DESCRIPTION)
class 			UpperCamelCase_     (			datasets.Metric):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     str:
						return datasets.MetricInfo(
						    description=_DESCRIPTION      ,      citation=_CITATION      ,      inputs_description=_KWARGS_DESCRIPTION      ,      features=datasets.Features(
						        {
						            "predictions": {
						                "id": datasets.Value("string"       ),
						                "prediction_text": datasets.features.Sequence(datasets.Value("string"       )       ),
						            },
						            "references": {
						                "id": datasets.Value("string"       ),
						                "answers": datasets.features.Sequence(
						                    {
						                        "text": datasets.Value("string"       ),
						                        "answer_start": datasets.Value("int32"       ),
						                    }       ),
						            },
						        }       )      ,      codebase_urls=["https://www.atticusprojectai.org/cuad"]      ,      reference_urls=["https://www.atticusprojectai.org/cuad"]      ,      )




	def   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    str       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
						__SCREAMING_SNAKE_CASE						       =    [
						    {
						        "paragraphs": [
						            {
						                "qas": [
						                    {
						                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
						                        "id": ref["id"],
						                    }
						                    for ref in references
						                ]
						            }
						        ]
						    }
						]
						__SCREAMING_SNAKE_CASE						       =    evaluate(dataset=__lowerCAmelCase      ,      predictions=__lowerCAmelCase       )
						return score



"""simple docstring"""







import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
a__    :							Optional[Any]            =					{
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    list(s_dict.keys()       )
					for key in keys:
										__SCREAMING_SNAKE_CASE						       =    R".*/layers_(\d+)"
										__SCREAMING_SNAKE_CASE						       =    key
										if re.match(lowerCAmelCase_  ,						lowerCAmelCase_       ):
															__SCREAMING_SNAKE_CASE						       =    re.sub(R"layers_(\d+)"  ,						R"block/\1/layer"  ,						lowerCAmelCase_       )

										__SCREAMING_SNAKE_CASE						       =    R"(encoder|decoder)\/"

										if re.match(lowerCAmelCase_  ,						lowerCAmelCase_       ):
															__SCREAMING_SNAKE_CASE						       =    re.match(lowerCAmelCase_  ,						lowerCAmelCase_       ).groups()
															if groups[0] == "encoder":
																				__SCREAMING_SNAKE_CASE						       =    re.sub(R"/mlp/"  ,						R"/1/mlp/"  ,						lowerCAmelCase_       )
																				__SCREAMING_SNAKE_CASE						       =    re.sub(R"/pre_mlp_layer_norm/"  ,						R"/1/layer_norm/"  ,						lowerCAmelCase_       )

															elif groups[0] == "decoder":
																				__SCREAMING_SNAKE_CASE						       =    re.sub(R"/mlp/"  ,						R"/2/mlp/"  ,						lowerCAmelCase_       )
																				__SCREAMING_SNAKE_CASE						       =    re.sub(R"/pre_mlp_layer_norm/"  ,						R"/2/layer_norm/"  ,						lowerCAmelCase_       )

        # 2. Convert other classic mappings
										for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
															if old_key in new_key:
																				__SCREAMING_SNAKE_CASE						       =    new_key.replace(lowerCAmelCase_  ,						lowerCAmelCase_       )

										print(f"""{key} -> {new_key}"""       )
										__SCREAMING_SNAKE_CASE						       =    s_dict.pop(lowerCAmelCase_       )

					if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
										__SCREAMING_SNAKE_CASE						       =    s_dict[
										    "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
										].T
					if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
										__SCREAMING_SNAKE_CASE						       =    s_dict[
										    "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
										].T

					# 3. Take extra care of the EXPERTS layer
					for key in list(s_dict.keys()       ):
										if "expert" in key:
															__SCREAMING_SNAKE_CASE						       =    s_dict[key].shape[0]
															__SCREAMING_SNAKE_CASE						       =    s_dict[key]
															for idx in range(lowerCAmelCase_       ):
																				__SCREAMING_SNAKE_CASE						       =    expert_weihts[idx]
																				print(f"""{key} -> {key.replace('expert/'  ,						'nested fstring'       )}"""       )

															s_dict.pop(lowerCAmelCase_       )

					return s_dict


a__    :							List[Any]            =					{
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					import regex as re

					with open(lowerCAmelCase_  ,						"r"       ) as f:
										__SCREAMING_SNAKE_CASE						       =    f.read()

					__SCREAMING_SNAKE_CASE						       =    re.findall(R"(.*) = ([0-9.]*)"  ,						lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    {}
					for param, value in regex_match:
										if param in GIN_TO_CONFIG_MAPPING and value != "":
															__SCREAMING_SNAKE_CASE						       =    float(lowerCAmelCase_       ) if "." in value else int(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    re.findall(R"(.*activations) = \(\'(.*)\',\)"  ,						lowerCAmelCase_       )[0]
					__SCREAMING_SNAKE_CASE						       =    str(activation[1]       )

					__SCREAMING_SNAKE_CASE						       =    num_experts
					__SCREAMING_SNAKE_CASE						       =    SwitchTransformersConfig(**lowerCAmelCase_       )
					return config




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_="./"  ,						lowerCAmelCase_=8       ):




					'''simple docstring'''


					print(f"""Loading flax weights from : {flax_checkpoint_path}"""       )
					__SCREAMING_SNAKE_CASE						       =    checkpoints.load_tax_checkpoint(lowerCAmelCase_       )

					if gin_file is not None:
										__SCREAMING_SNAKE_CASE						       =    convert_gin_to_config(lowerCAmelCase_  ,						lowerCAmelCase_       )
					else:
										__SCREAMING_SNAKE_CASE						       =    SwitchTransformersConfig.from_pretrained(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    SwitchTransformersForConditionalGeneration(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    flax_params["target"]
					__SCREAMING_SNAKE_CASE						       =    flatten_dict(lowerCAmelCase_  ,						sep="/"       )
					__SCREAMING_SNAKE_CASE						       =    rename_keys(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    unflatten_dict(lowerCAmelCase_  ,						sep="/"       )

					# Load the flax params in the PT model
					load_flax_weights_in_pytorch_model(lowerCAmelCase_  ,						lowerCAmelCase_       )

					print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
					pt_model.save_pretrained(lowerCAmelCase_       )


if __name__ == "__main__":
		a__    :							List[Any]            =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--switch_t5x_checkpoint_path''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
		        ''' model architecture. If not provided, a `gin_file` has to be provided.'''
		    ),
		)
		parser.add_argument(
		    '''--gin_file''',
		    default=None,
		    type=str,
		    required=False,
		    help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
		)
		parser.add_argument(
		    '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
		)
		parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
		a__    :							Tuple            =					parser.parse_args()
		convert_flax_checkpoint_to_pytorch(
		    args.switch_tax_checkpoint_path,
		    args.config_name,
		    args.gin_file,
		    args.pytorch_dump_folder_path,
		    args.num_experts,
		)
