





"""simple docstring"""



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 		__lowercase     (   snake_case_  :  str				,snake_case_  :  str				,snake_case_  :  str						)   ->Optional[Any]:



    '''simple docstring'''







    def get_masked_lm_array(snake_case_  :  str						):
        __A    :						int						 =     F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        __A    :						Tuple						 =     tf.train.load_variable(snake_case_				,snake_case_						)

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

        return torch.from_numpy(snake_case_						)

    def get_encoder_array(snake_case_  :  str						):
        __A    :						Any						 =     F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        __A    :						int						 =     tf.train.load_variable(snake_case_				,snake_case_						)

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

        return torch.from_numpy(snake_case_						)

    def get_encoder_layer_array(snake_case_  :  int				,snake_case_  :  str						):
        __A    :						List[str]						 =     F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        __A    :						Optional[int]						 =     tf.train.load_variable(snake_case_				,snake_case_						)

        if "kernel" in name:
            __A    :						Optional[int]						 =     array.transpose()

        return torch.from_numpy(snake_case_						)

    def get_encoder_attention_layer_array(snake_case_  :  int				,snake_case_  :  str				,snake_case_  :  List[Any]						):
        __A    :						Union[str, Any]						 =     F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        __A    :						int						 =     tf.train.load_variable(snake_case_				,snake_case_						)
        __A    :						str						 =     array.reshape(snake_case_						)

        if "kernel" in name:
            __A    :						Optional[Any]						 =     array.transpose()

        return torch.from_numpy(snake_case_						)

    print(F"""Loading model based on config from {config_path}..."""						)
    __A    :						Optional[int]						 =     BertConfig.from_json_file(snake_case_						)
    __A    :						List[str]						 =     BertForMaskedLM(snake_case_						)

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

        # Self-attention
        __A    :						BertSelfAttention						 =     layer.attention.self

        __A    :						List[str]						 =     get_encoder_attention_layer_array(
            snake_case_				,'''_query_dense/kernel'''				,self_attn.query.weight.data.shape						)
        __A    :						Union[str, Any]						 =     get_encoder_attention_layer_array(
            snake_case_				,'''_query_dense/bias'''				,self_attn.query.bias.data.shape						)
        __A    :						List[Any]						 =     get_encoder_attention_layer_array(
            snake_case_				,'''_key_dense/kernel'''				,self_attn.key.weight.data.shape						)
        __A    :						Dict						 =     get_encoder_attention_layer_array(
            snake_case_				,'''_key_dense/bias'''				,self_attn.key.bias.data.shape						)
        __A    :						Any						 =     get_encoder_attention_layer_array(
            snake_case_				,'''_value_dense/kernel'''				,self_attn.value.weight.data.shape						)
        __A    :						Optional[int]						 =     get_encoder_attention_layer_array(
            snake_case_				,'''_value_dense/bias'''				,self_attn.value.bias.data.shape						)

        # Self-attention Output
        __A    :						BertSelfOutput						 =     layer.attention.output

        __A    :						List[Any]						 =     get_encoder_attention_layer_array(
            snake_case_				,'''_output_dense/kernel'''				,self_output.dense.weight.data.shape						)
        __A    :						Any						 =     get_encoder_attention_layer_array(
            snake_case_				,'''_output_dense/bias'''				,self_output.dense.bias.data.shape						)

        __A    :						Tuple						 =     get_encoder_layer_array(snake_case_				,'''_attention_layer_norm/gamma'''						)
        __A    :						List[str]						 =     get_encoder_layer_array(snake_case_				,'''_attention_layer_norm/beta'''						)

        # Intermediate
        __A    :						BertIntermediate						 =     layer.intermediate

        __A    :						Any						 =     get_encoder_layer_array(snake_case_				,'''_intermediate_dense/kernel'''						)
        __A    :						int						 =     get_encoder_layer_array(snake_case_				,'''_intermediate_dense/bias'''						)

        # Output
        __A    :						BertOutput						 =     layer.output

        __A    :						List[Any]						 =     get_encoder_layer_array(snake_case_				,'''_output_dense/kernel'''						)
        __A    :						Dict						 =     get_encoder_layer_array(snake_case_				,'''_output_dense/bias'''						)

        __A    :						Tuple						 =     get_encoder_layer_array(snake_case_				,'''_output_layer_norm/gamma'''						)
        __A    :						List[str]						 =     get_encoder_layer_array(snake_case_				,'''_output_layer_norm/beta'''						)

    # Embeddings
    __A    :						Optional[int]						 =     get_encoder_array('''_position_embedding_layer/embeddings'''						)
    __A    :						str						 =     get_encoder_array('''_type_embedding_layer/embeddings'''						)
    __A    :						int						 =     get_encoder_array('''_embedding_norm_layer/gamma'''						)
    __A    :						Optional[Any]						 =     get_encoder_array('''_embedding_norm_layer/beta'''						)

    # LM Head
    __A    :						str						 =     model.cls.predictions.transform

    __A    :						List[Any]						 =     get_masked_lm_array('''dense/kernel'''						)
    __A    :						int						 =     get_masked_lm_array('''dense/bias'''						)

    __A    :						Optional[int]						 =     get_masked_lm_array('''layer_norm/gamma'''						)
    __A    :						Optional[Any]						 =     get_masked_lm_array('''layer_norm/beta'''						)

    __A    :						Tuple						 =     get_masked_lm_array('''embedding_table'''						)

    # Pooling
    __A    :						Tuple						 =     BertPooler(config=snake_case_						)
    __A    :						BertPooler						 =     get_encoder_array('''_pooler_layer/kernel'''						)
    __A    :						BertPooler						 =     get_encoder_array('''_pooler_layer/bias'''						)

    # Export final model
    model.save_pretrained(snake_case_						)

    # Integration test - should load without any errors ;)
    __A    :						Dict						 =     BertForMaskedLM.from_pretrained(snake_case_						)
    print(new_model.eval()						)

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


if __name__ == "__main__":
       a_ =			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.""",
       )
       a_ =			parser.parse_args()
       convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """facebook/bart-large-mnli"""
 _lowerCamelCase        =     (
     """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
     """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
     """It returns the most likely label in the list of provided `labels` for the input text."""
 )
 _lowerCamelCase        =     """text_classifier"""
 _lowerCamelCase        =     AutoTokenizer
 _lowerCamelCase        =     AutoModelForSequenceClassification

 _lowerCamelCase        =     ["""text""", ["""text"""]]
 _lowerCamelCase        =     ["""text"""]

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().setup()
     __A    :						List[str]						 =     self.model.config
     __A    :						int						 =     -1
     for idx, label in config.idalabel.items():
         if label.lower().startswith('''entail'''  ):
             __A    :						List[str]						 =     int(__lowerCamelCase  )
     if self.entailment_id == -1:
         raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.'''  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     labels
     return self.pre_processor(
         [text] * len(__lowerCamelCase  )					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='''pt'''					,						padding='''max_length'''					,						)







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     outputs.logits
     __A    :						List[str]						 =     torch.argmax(logits[:, 2]  ).item()
     return self._labels[label_id]




