import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					def get_masked_lm_array(__snake_case						):
										_UpperCamelCase			     =     f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)

										if "kernel" in name:
															_UpperCamelCase			     =     array.transpose()

										return torch.from_numpy(__snake_case						)

					def get_encoder_array(__snake_case						):
										_UpperCamelCase			     =     f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)

										if "kernel" in name:
															_UpperCamelCase			     =     array.transpose()

										return torch.from_numpy(__snake_case						)

					def get_encoder_layer_array(__snake_case  ,     __snake_case						):
										_UpperCamelCase			     =     f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)

										if "kernel" in name:
															_UpperCamelCase			     =     array.transpose()

										return torch.from_numpy(__snake_case						)

					def get_encoder_attention_layer_array(__snake_case  ,     __snake_case  ,     __snake_case						):
										_UpperCamelCase			     =     f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)
										_UpperCamelCase			     =     array.reshape(__snake_case						)

										if "kernel" in name:
															_UpperCamelCase			     =     array.transpose()

										return torch.from_numpy(__snake_case						)

					print(f"""Loading model based on config from {config_path}..."""						)
					_UpperCamelCase			     =     BertConfig.from_json_file(__snake_case						)
					_UpperCamelCase			     =     BertForMaskedLM(__snake_case						)

					# Layers
					for layer_index in range(0  ,     config.num_hidden_layers						):
										_UpperCamelCase			     =     model.bert.encoder.layer[layer_index]

										# Self-attention
										_UpperCamelCase			     =     layer.attention.self

										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_query_dense/kernel'''  ,     self_attn.query.weight.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_query_dense/bias'''  ,     self_attn.query.bias.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_key_dense/kernel'''  ,     self_attn.key.weight.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_key_dense/bias'''  ,     self_attn.key.bias.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_value_dense/kernel'''  ,     self_attn.value.weight.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_value_dense/bias'''  ,     self_attn.value.bias.data.shape						)

										# Self-attention Output
										_UpperCamelCase			     =     layer.attention.output

										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_output_dense/kernel'''  ,     self_output.dense.weight.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_output_dense/bias'''  ,     self_output.dense.bias.data.shape						)

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_attention_layer_norm/gamma'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_attention_layer_norm/beta'''						)

										# Intermediate
										_UpperCamelCase			     =     layer.intermediate

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_intermediate_dense/kernel'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_intermediate_dense/bias'''						)

										# Output
										_UpperCamelCase			     =     layer.output

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_dense/kernel'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_dense/bias'''						)

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_layer_norm/gamma'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_layer_norm/beta'''						)

					# Embeddings
					_UpperCamelCase			     =     get_encoder_array('''_position_embedding_layer/embeddings'''						)
					_UpperCamelCase			     =     get_encoder_array('''_type_embedding_layer/embeddings'''						)
					_UpperCamelCase			     =     get_encoder_array('''_embedding_norm_layer/gamma'''						)
					_UpperCamelCase			     =     get_encoder_array('''_embedding_norm_layer/beta'''						)

					# LM Head
					_UpperCamelCase			     =     model.cls.predictions.transform

					_UpperCamelCase			     =     get_masked_lm_array('''dense/kernel'''						)
					_UpperCamelCase			     =     get_masked_lm_array('''dense/bias'''						)

					_UpperCamelCase			     =     get_masked_lm_array('''layer_norm/gamma'''						)
					_UpperCamelCase			     =     get_masked_lm_array('''layer_norm/beta'''						)

					_UpperCamelCase			     =     get_masked_lm_array('''embedding_table'''						)

					# Pooling
					_UpperCamelCase			     =     BertPooler(config=__snake_case						)
					_UpperCamelCase			     =     get_encoder_array('''_pooler_layer/kernel'''						)
					_UpperCamelCase			     =     get_encoder_array('''_pooler_layer/bias'''						)

					# Export final model
					model.save_pretrained(__snake_case						)

					# Integration test - should load without any errors ;)
					_UpperCamelCase			     =     BertForMaskedLM.from_pretrained(__snake_case						)
					print(new_model.eval()						)

					print('''Model conversion was done sucessfully!'''						)


if __name__ == "__main__":
					_lowerCAmelCase       =		argparse.ArgumentParser()
					parser.add_argument(
					    "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path."
					)
					parser.add_argument(
					    "--bert_config_file",
					    type=str,
					    required=True,
					    help="The config json file corresponding to the BERT model. This specifies the model architecture.",
					)
					parser.add_argument(
					    "--pytorch_dump_path",
					    type=str,
					    required=True,
					    help="Path to the output PyTorch model.",
					)
					_lowerCAmelCase       =		parser.parse_args()
					convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







'''simple docstring'''






from sklearn.metrics import recall_score

import datasets


__UpperCamelCase					    =	"\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"


__UpperCamelCase					    =	"\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {'recall': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {'recall': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {'recall': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'recall': array([1., 0., 0.])}\n"


__UpperCamelCase					    =	"\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"




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

		def 	lowercase__     (   self					:   Optional[int]			)					->    Any:


					"""simple docstring"""

					return datasets.MetricInfo(
					    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
					        {
					            """predictions""": datasets.Sequence(datasets.Value("""int32"""			)			),
					            """references""": datasets.Sequence(datasets.Value("""int32"""			)			),
					        }
					        if self.config_name == """multilabel"""
					        else {
					            """predictions""": datasets.Value("""int32"""			),
					            """references""": datasets.Value("""int32"""			),
					        }			)  ,      reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]  ,      )

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   int  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Any=None  ,      __magic_name__					:   Optional[Any]=1  ,      __magic_name__					:   List[str]="binary"  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Dict="warn"  ,      )					->    Any:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     recall_score(
					    __magic_name__  ,      __magic_name__  ,      labels=__magic_name__  ,      pos_label=__magic_name__  ,      average=__magic_name__  ,      sample_weight=__magic_name__  ,      zero_division=__magic_name__  ,      )
					return {"recall": float(__magic_name__			) if score.size == 1 else score}


