


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE       :      List[Any]												= logging.get_logger(__name__)



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Tuple:
    _lowercase       :							Union[str, Any]      		=		os.path.abspath(lowerCamelCase_ )
    logger.info(F'''Converting TensorFlow checkpoint from {tf_path}''' )
    # Load weights from TF model
    _lowercase       :							Optional[Any]      		=		tf.train.list_variables(lowerCamelCase_ )
    _lowercase       :							Optional[Any]      		=		[]
    _lowercase       :							List[str]      		=		[]
    _lowercase       :							str      		=		[]
    for full_name, shape in init_vars:
        # logger.info(f"Loading TF weight {name} with shape {shape}")
        _lowercase       :							List[Any]      		=		full_name.split('/' )
        if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
            logger.info(F'''Skipping non-model layer {full_name}''' )
            continue
        if "optimizer" in full_name:
            logger.info(F'''Skipping optimization layer {full_name}''' )
            continue
        if name[0] == "model":
            # ignore initial 'model'
            _lowercase       :							Union[str, Any]      		=		name[1:]
        # figure out how many levels deep the name is
        _lowercase       :							str      		=		0
        for _name in name:
            if _name.startswith('layer_with_weights' ):
                depth += 1
            else:
                break
        layer_depth.append(lowerCamelCase_ )
        # read data
        _lowercase       :							Union[str, Any]      		=		tf.train.load_variable(lowerCamelCase_							, lowerCamelCase_ )
        names.append('/'.join(lowerCamelCase_ ) )
        arrays.append(lowerCamelCase_ )
    logger.info(F'''Read a total of {len(lowerCamelCase_ ):,} layers''' )

    # Sanity check
    if len(set(lowerCamelCase_ ) ) != 1:
        raise ValueError(F'''Found layer names with different depths (layer depth {list(set(lowerCamelCase_ ) )})''' )
    _lowercase       :							Any      		=		list(set(lowerCamelCase_ ) )[0]
    if layer_depth != 1:
        raise ValueError(
            'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
            ' heads.' )

    # convert layers
    logger.info('Converting weights...' )
    for full_name, array in zip(lowerCamelCase_							, lowerCamelCase_ ):
        _lowercase       :							Tuple      		=		full_name.split('/' )
        _lowercase       :							Optional[int]      		=		model
        _lowercase       :							str      		=		[]
        for i, m_name in enumerate(lowerCamelCase_ ):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith('layer_with_weights' ):
                _lowercase       :							Optional[Any]      		=		int(m_name.split('-' )[-1] )
                if layer_num <= 2:
                    # embedding layers
                    # layer_num 0: word_embeddings
                    # layer_num 1: position_embeddings
                    # layer_num 2: token_type_embeddings
                    continue
                elif layer_num == 3:
                    # embedding LayerNorm
                    trace.extend(['embeddings', 'LayerNorm'] )
                    _lowercase       :							Union[str, Any]      		=		getattr(lowerCamelCase_							, 'embeddings' )
                    _lowercase       :							Optional[int]      		=		getattr(lowerCamelCase_							, 'LayerNorm' )
                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                    # encoder layers
                    trace.extend(['encoder', 'layer', str(layer_num - 4 )] )
                    _lowercase       :							List[str]      		=		getattr(lowerCamelCase_							, 'encoder' )
                    _lowercase       :							Optional[int]      		=		getattr(lowerCamelCase_							, 'layer' )
                    _lowercase       :							Dict      		=		pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(['pooler', 'dense'] )
                    _lowercase       :							Optional[Any]      		=		getattr(lowerCamelCase_							, 'pooler' )
                    _lowercase       :							Tuple      		=		getattr(lowerCamelCase_							, 'dense' )
            elif m_name == "embeddings":
                trace.append('embeddings' )
                _lowercase       :							Dict      		=		getattr(lowerCamelCase_							, 'embeddings' )
                if layer_num == 0:
                    trace.append('word_embeddings' )
                    _lowercase       :							str      		=		getattr(lowerCamelCase_							, 'word_embeddings' )
                elif layer_num == 1:
                    trace.append('position_embeddings' )
                    _lowercase       :							Optional[Any]      		=		getattr(lowerCamelCase_							, 'position_embeddings' )
                elif layer_num == 2:
                    trace.append('token_type_embeddings' )
                    _lowercase       :							List[Any]      		=		getattr(lowerCamelCase_							, 'token_type_embeddings' )
                else:
                    raise ValueError(F'''Unknown embedding layer with name {full_name}''' )
                trace.append('weight' )
                _lowercase       :							List[Any]      		=		getattr(lowerCamelCase_							, 'weight' )
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(['attention', 'self'] )
                _lowercase       :							Tuple      		=		getattr(lowerCamelCase_							, 'attention' )
                _lowercase       :							Tuple      		=		getattr(lowerCamelCase_							, 'self' )
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(['attention', 'output', 'LayerNorm'] )
                _lowercase       :							Dict      		=		getattr(lowerCamelCase_							, 'attention' )
                _lowercase       :							int      		=		getattr(lowerCamelCase_							, 'output' )
                _lowercase       :							Dict      		=		getattr(lowerCamelCase_							, 'LayerNorm' )
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(['attention', 'output', 'dense'] )
                _lowercase       :							Union[str, Any]      		=		getattr(lowerCamelCase_							, 'attention' )
                _lowercase       :							Optional[Any]      		=		getattr(lowerCamelCase_							, 'output' )
                _lowercase       :							Dict      		=		getattr(lowerCamelCase_							, 'dense' )
            elif m_name == "_output_dense":
                # output dense
                trace.extend(['output', 'dense'] )
                _lowercase       :							Optional[Any]      		=		getattr(lowerCamelCase_							, 'output' )
                _lowercase       :							Any      		=		getattr(lowerCamelCase_							, 'dense' )
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(['output', 'LayerNorm'] )
                _lowercase       :							List[Any]      		=		getattr(lowerCamelCase_							, 'output' )
                _lowercase       :							Dict      		=		getattr(lowerCamelCase_							, 'LayerNorm' )
            elif m_name == "_key_dense":
                # attention key
                trace.append('key' )
                _lowercase       :							List[Any]      		=		getattr(lowerCamelCase_							, 'key' )
            elif m_name == "_query_dense":
                # attention query
                trace.append('query' )
                _lowercase       :							Optional[Any]      		=		getattr(lowerCamelCase_							, 'query' )
            elif m_name == "_value_dense":
                # attention value
                trace.append('value' )
                _lowercase       :							Any      		=		getattr(lowerCamelCase_							, 'value' )
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(['intermediate', 'dense'] )
                _lowercase       :							int      		=		getattr(lowerCamelCase_							, 'intermediate' )
                _lowercase       :							Optional[int]      		=		getattr(lowerCamelCase_							, 'dense' )
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append('output' )
                _lowercase       :							Optional[Any]      		=		getattr(lowerCamelCase_							, 'output' )
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append('bias' )
                _lowercase       :							Optional[Any]      		=		getattr(lowerCamelCase_							, 'bias' )
            elif m_name in ["kernel", "gamma"]:
                trace.append('weight' )
                _lowercase       :							Optional[int]      		=		getattr(lowerCamelCase_							, 'weight' )
            else:
                logger.warning(F'''Ignored {m_name}''' )
        # for certain layers reshape is necessary
        _lowercase       :							Any      		=		'.'.join(lowerCamelCase_ )
        if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'							, lowerCamelCase_ ) or re.match(
            R'(\S+)\.attention\.output\.dense\.weight'							, lowerCamelCase_ ):
            _lowercase       :							Any      		=		array.reshape(pointer.data.shape )
        if "kernel" in full_name:
            _lowercase       :							Union[str, Any]      		=		array.transpose()
        if pointer.shape == array.shape:
            _lowercase       :							Optional[int]      		=		torch.from_numpy(lowerCamelCase_ )
        else:
            raise ValueError(
                F'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
                F''' {array.shape}''' )
        logger.info(F'''Successfully set variable {full_name} to PyTorch layer {trace}''' )
    return model



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    # Instantiate model
    logger.info(F'''Loading model based on config from {config_path}...''' )
    _lowercase       :							Union[str, Any]      		=		BertConfig.from_json_file(lowerCamelCase_ )
    _lowercase       :							Union[str, Any]      		=		BertModel(lowerCamelCase_ )

    # Load weights from checkpoint
    logger.info(F'''Loading weights from checkpoint {tf_checkpoint_path}...''' )
    load_tfa_weights_in_bert(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )

    # Save pytorch-model
    logger.info(F'''Saving PyTorch model to {pytorch_dump_path}...''' )
    torch.save(model.state_dict()							, lowerCamelCase_ )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      str												= argparse.ArgumentParser()
 parser.add_argument(
     "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x 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 (must include filename).",
 )
 SCREAMING_SNAKE_CASE       :      str												= parser.parse_args()
 convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE       :      List[str]												= logging.get_logger(__name__)






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a   ):
       def __init__(					self,      *lowerCamelCase,      **lowerCamelCase)       ->   int:




           """simple docstring"""

           super().__init__(*lowerCamelCase,      **lowerCamelCase)
           requires_backends(self,      'vision')
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == 'tf'
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)
       def 			UpperCamelCase     (					self,      lowerCamelCase=None)       ->   int:




           """simple docstring"""

           _lowercase       :							Dict      		=		{}
           if top_k is not None:
               _lowercase       :							List[str]      		=		top_k
           return {}, {}, postprocess_params
       def __call__(					self,      lowerCamelCase,      **lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           return super().__call__(lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		load_image(lowerCamelCase)
           _lowercase       :							List[str]      		=		self.image_processor(images=lowerCamelCase,      return_tensors=self.framework)
           return model_inputs
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.model(**lowerCamelCase)
           return model_outputs




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=5)       ->   Dict:




           """simple docstring"""

           if top_k > self.model.config.num_labels:
               _lowercase       :							List[Any]      		=		self.model.config.num_labels

           if self.framework == "pt":
               _lowercase       :							int      		=		model_outputs.logits.softmax(-1)[0]
               _lowercase		,				_lowercase       :							Union[str, Any]      		=		probs.topk(lowerCamelCase)
           elif self.framework == "tf":
               _lowercase       :							int      		=		stable_softmax(model_outputs.logits,      axis=-1)[0]
               _lowercase       :							List[Any]      		=		tf.math.top_k(lowerCamelCase,      k=lowerCamelCase)
               _lowercase		,				_lowercase       :							Any      		=		topk.values.numpy(), topk.indices.numpy()
           else:
               raise ValueError(F'''Unsupported framework: {self.framework}''')

           _lowercase       :							str      		=		scores.tolist()
           _lowercase       :							str      		=		ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase,      lowerCamelCase)]


