
from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowercase__       =logging.get_logger(__name__)



class 		UpperCamelCase__  (       __lowercase    ):
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					List[Any]   ):
    if isinstance(snake_case_					,				snake_case_   ):
     __a :  str						=  [label.strip() for label in labels.split(''','''   ) if label.strip()]
    return labels







   def __call__(self :					int					,				snake_case_ :					Tuple					,				snake_case_ :					Dict					,				snake_case_ :					Optional[Any]   ):
    if len(snake_case_   ) == 0 or len(snake_case_   ) == 0:
     raise ValueError('''You must include at least one label and at least one sequence.'''   )
    if hypothesis_template.format(labels[0]   ) == hypothesis_template:
     raise ValueError(
         (
             '''The provided hypothesis_template "{}" was not able to be formatted with the target labels. '''
             '''Make sure the passed template includes formatting syntax such as {{}} where the label should go.'''
         ).format(snake_case_   )   )

    if isinstance(snake_case_					,				snake_case_   ):
     __a :  int						=  [sequences]

    __a :  Tuple						=  []
    for sequence in sequences:
     sequence_pairs.extend([[sequence, hypothesis_template.format(snake_case_   )] for label in labels]   )

    return sequence_pairs, sequences





@add_end_docstrings(__lowercase    )
class 		UpperCamelCase__  (       __lowercase    ):
   def __init__(self :					Optional[int]					,				snake_case_ :					int=ZeroShotClassificationArgumentHandler()					,				*snake_case_ :					Dict					,				**snake_case_ :					Any   ):
    __a :  Union[str, Any]						=  args_parser
    super().__init__(*snake_case_					,				**snake_case_   )
    if self.entailment_id == -1:
     logger.warning(
         '''Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '''
         '''-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.'''   )
   @property
   def       lowerCAmelCase			(self :					str   ):
    for label, ind in self.model.config.labelaid.items():
     if label.lower().startswith('''entail'''   ):
      return ind
    return -1
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					Optional[int]					,				snake_case_ :					Any=True					,				snake_case_ :					Optional[int]=True					,				snake_case_ :					Tuple=TruncationStrategy.ONLY_FIRST					,				**snake_case_ :					Union[str, Any]   ):
    __a :  Optional[int]						=  self.framework
    if self.tokenizer.pad_token is None:
     # Override for tokenizers not supporting padding
     logger.error(
         '''Tokenizer was not supporting padding necessary for zero-shot, attempting to use '''
         ''' `pad_token=eos_token`'''   )
     __a :  Tuple						=  self.tokenizer.eos_token
    try:
     __a :  Dict						=  self.tokenizer(
         snake_case_					,				add_special_tokens=snake_case_					,				return_tensors=snake_case_					,				padding=snake_case_					,				truncation=snake_case_					,				)
    except Exception as e:
     if "too short" in str(snake_case_   ):
      # tokenizers might yell that we want to truncate
      # to a value that is not even reached by the input.
      # In that case we don't want to truncate.
      # It seems there's not a really better way to catch that
      # exception.

      __a :  str						=  self.tokenizer(
          snake_case_					,				add_special_tokens=snake_case_					,				return_tensors=snake_case_					,				padding=snake_case_					,				truncation=TruncationStrategy.DO_NOT_TRUNCATE					,				)
     else:
      raise e

    return inputs
   def       lowerCAmelCase			(self :					Optional[Any]					,				**snake_case_ :					str   ):
    if kwargs.get('''multi_class'''					,				snake_case_   ) is not None:
     __a :  int						=  kwargs['''multi_class''']
     logger.warning(
         '''The `multi_class` argument has been deprecated and renamed to `multi_label`. '''
         '''`multi_class` will be removed in a future version of Transformers.'''   )
    __a :  List[Any]						=  {}
    if "candidate_labels" in kwargs:
     __a :  Optional[Any]						=  self._args_parser._parse_labels(kwargs['''candidate_labels''']   )
    if "hypothesis_template" in kwargs:
     __a :  int						=  kwargs['''hypothesis_template''']

    __a :  str						=  {}
    if "multi_label" in kwargs:
     __a :  Optional[Any]						=  kwargs['''multi_label''']
    return preprocess_params, {}, postprocess_params
   def __call__(self :					List[str]					,				snake_case_ :					Union[str, List[str]]					,				*snake_case_ :					Tuple					,				**snake_case_ :					Dict					,				):
    if len(snake_case_   ) == 0:
     pass
    elif len(snake_case_   ) == 1 and "candidate_labels" not in kwargs:
     __a :  Optional[int]						=  args[0]
    else:
     raise ValueError(f"Unable to understand extra arguments {args}"   )

    return super().__call__(snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Any=None					,				snake_case_ :					Union[str, Any]="This example is {}."   ):
    __a				,       __a :  List[str]						=  self._args_parser(snake_case_					,				snake_case_					,				snake_case_   )

    for i, (candidate_label, sequence_pair) in enumerate(zip(snake_case_					,				snake_case_   )   ):
     __a :  Dict						=  self._parse_and_tokenize([sequence_pair]   )

     yield {
         "candidate_label": candidate_label,
         "sequence": sequences[0],
         "is_last": i == len(snake_case_   ) - 1,
         **model_input,
     }
   def       lowerCAmelCase			(self :					List[str]					,				snake_case_ :					int   ):
    __a :  List[Any]						=  inputs['''candidate_label''']
    __a :  int						=  inputs['''sequence''']
    __a :  int						=  {k: inputs[k] for k in self.tokenizer.model_input_names}
    __a :  Any						=  self.model(**snake_case_   )

    __a :  Any						=  {
        '''candidate_label''': candidate_label,
        '''sequence''': sequence,
        '''is_last''': inputs['''is_last'''],
        **outputs,
    }
    return model_outputs







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Any					,				snake_case_ :					List[str]=False   ):
    __a :  Any						=  [outputs['''candidate_label'''] for outputs in model_outputs]
    __a :  List[str]						=  [outputs['''sequence'''] for outputs in model_outputs]
    __a :  int						=  np.concatenate([output['''logits'''].numpy() for output in model_outputs]   )
    __a :  Any						=  logits.shape[0]
    __a :  Union[str, Any]						=  len(snake_case_   )
    __a :  Union[str, Any]						=  N // n
    __a :  Union[str, Any]						=  logits.reshape((num_sequences, n, -1)   )

    if multi_label or len(snake_case_   ) == 1:
     # softmax over the entailment vs. contradiction dim for each label independently
     __a :  List[str]						=  self.entailment_id
     __a :  List[Any]						=  -1 if entailment_id == 0 else 0
     __a :  int						=  reshaped_outputs[..., [contradiction_id, entailment_id]]
     __a :  Any						=  np.exp(snake_case_   ) / np.exp(snake_case_   ).sum(-1					,				keepdims=snake_case_   )
     __a :  Union[str, Any]						=  scores[..., 1]
    else:
     # softmax the "entailment" logits over all candidate labels
     __a :  Union[str, Any]						=  reshaped_outputs[..., self.entailment_id]
     __a :  int						=  np.exp(snake_case_   ) / np.exp(snake_case_   ).sum(-1					,				keepdims=snake_case_   )

    __a :  Optional[Any]						=  list(reversed(scores[0].argsort()   )   )
    return {
        "sequence": sequences[0],
        "labels": [candidate_labels[i] for i in top_inds],
        "scores": scores[0, top_inds].tolist(),
    }


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()
lowercase__       =logging.get_logger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				Dict  ):
 __a :  List[str]						=  os.path.abspath(lowerCAmelCase__  )
 logger.info(f"Converting TensorFlow checkpoint from {tf_path}"  )
 # Load weights from TF model
 __a :  Tuple						=  tf.train.list_variables(lowerCAmelCase__  )
 __a :  Optional[Any]						=  []
 __a :  Union[str, Any]						=  []
 __a :  str						=  []
 for full_name, shape in init_vars:
  # logger.info(f"Loading TF weight {name} with shape {shape}")
  __a :  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'
   __a :  Any						=  name[1:]
  # figure out how many levels deep the name is
  __a :  List[Any]						=  0
  for _name in name:
   if _name.startswith('''layer_with_weights'''  ):
    depth += 1
   else:
    break
  layer_depth.append(lowerCAmelCase__  )
  # read data
  __a :  Tuple						=  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__  )  )})"  )
 __a :  int						=  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__  ):
  __a :  int						=  full_name.split('''/'''  )
  __a :  Tuple						=  model
  __a :  Dict						=  []
  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'''  ):
    __a :  Union[str, 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''']  )
     __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''embeddings'''  )
     __a :  List[str]						=  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  )]  )
     __a :  Dict						=  getattr(lowerCAmelCase__  ,   '''encoder'''  )
     __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''layer'''  )
     __a :  Any						=  pointer[layer_num - 4]
    elif layer_num == config.num_hidden_layers + 4:
     # pooler layer
     trace.extend(['''pooler''', '''dense''']  )
     __a :  Any						=  getattr(lowerCAmelCase__  ,   '''pooler'''  )
     __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "embeddings":
    trace.append('''embeddings'''  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''embeddings'''  )
    if layer_num == 0:
     trace.append('''word_embeddings'''  )
     __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''word_embeddings'''  )
    elif layer_num == 1:
     trace.append('''position_embeddings'''  )
     __a :  List[str]						=  getattr(lowerCAmelCase__  ,   '''position_embeddings'''  )
    elif layer_num == 2:
     trace.append('''token_type_embeddings'''  )
     __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''token_type_embeddings'''  )
    else:
     raise ValueError(f"Unknown embedding layer with name {full_name}"  )
    trace.append('''weight'''  )
    __a :  Tuple						=  getattr(lowerCAmelCase__  ,   '''weight'''  )
   elif m_name == "_attention_layer":
    # self-attention layer
    trace.extend(['''attention''', '''self''']  )
    __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''attention'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''self'''  )
   elif m_name == "_attention_layer_norm":
    # output attention norm
    trace.extend(['''attention''', '''output''', '''LayerNorm''']  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''attention'''  )
    __a :  List[Any]						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  List[Any]						=  getattr(lowerCAmelCase__  ,   '''LayerNorm'''  )
   elif m_name == "_attention_output_dense":
    # output attention dense
    trace.extend(['''attention''', '''output''', '''dense''']  )
    __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''attention'''  )
    __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  Any						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "_output_dense":
    # output dense
    trace.extend(['''output''', '''dense''']  )
    __a :  Tuple						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  str						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "_output_layer_norm":
    # output dense
    trace.extend(['''output''', '''LayerNorm''']  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  str						=  getattr(lowerCAmelCase__  ,   '''LayerNorm'''  )
   elif m_name == "_key_dense":
    # attention key
    trace.append('''key'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''key'''  )
   elif m_name == "_query_dense":
    # attention query
    trace.append('''query'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''query'''  )
   elif m_name == "_value_dense":
    # attention value
    trace.append('''value'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''value'''  )
   elif m_name == "_intermediate_dense":
    # attention intermediate dense
    trace.extend(['''intermediate''', '''dense''']  )
    __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''intermediate'''  )
    __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "_output_layer_norm":
    # output layer norm
    trace.append('''output'''  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''output'''  )
   # weights & biases
   elif m_name in ["bias", "beta"]:
    trace.append('''bias'''  )
    __a :  Dict						=  getattr(lowerCAmelCase__  ,   '''bias'''  )
   elif m_name in ["kernel", "gamma"]:
    trace.append('''weight'''  )
    __a :  List[Any]						=  getattr(lowerCAmelCase__  ,   '''weight'''  )
   else:
    logger.warning(f"Ignored {m_name}"  )
        # for certain layers reshape is necessary
  __a :  List[str]						=  '''.'''.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__  ):
   __a :  str						=  array.reshape(pointer.data.shape  )
  if "kernel" in full_name:
   __a :  Optional[Any]						=  array.transpose()
  if pointer.shape == array.shape:
   __a :  str						=  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__							:				Optional[Any]  ,   lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Optional[Any]  ):
 # Instantiate model
 logger.info(f"Loading model based on config from {config_path}..."  )
 __a :  Dict						=  BertConfig.from_json_file(lowerCAmelCase__  )
 __a :  int						=  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__":
      lowercase__       =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).',
      )
      lowercase__       =parser.parse_args()
      convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
