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()
a_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						int =					os.path.abspath(SCREAMING_SNAKE_CASE__	)
 logger.info(F"Converting TensorFlow checkpoint from {tf_path}"	)
 # Load weights from TF model
 UpperCamelCase_					:						str =					tf.train.list_variables(SCREAMING_SNAKE_CASE__	)
 UpperCamelCase_					:						Union[str, Any] =					[]
 UpperCamelCase_					:						List[str] =					[]
 UpperCamelCase_					:						str =					[]
 for full_name, shape in init_vars:
  # logger.info(f"Loading TF weight {name} with shape {shape}")
  UpperCamelCase_					:						Union[str, 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'
   UpperCamelCase_					:						Union[str, Any] =					name[1:]
  # figure out how many levels deep the name is
  UpperCamelCase_					:						str =					0
  for _name in name:
   if _name.startswith('layer_with_weights'	):
    depth += 1
   else:
    break
  layer_depth.append(SCREAMING_SNAKE_CASE__	)
  # read data
  UpperCamelCase_					:						List[Any] =					tf.train.load_variable(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	)
  names.append('/'.join(SCREAMING_SNAKE_CASE__	)	)
  arrays.append(SCREAMING_SNAKE_CASE__	)
 logger.info(F"Read a total of {len(SCREAMING_SNAKE_CASE__	):,} layers"	)

 # Sanity check
 if len(set(SCREAMING_SNAKE_CASE__	)	) != 1:
  raise ValueError(F"Found layer names with different depths (layer depth {list(set(SCREAMING_SNAKE_CASE__	)	)})"	)
 UpperCamelCase_					:						Dict =					list(set(SCREAMING_SNAKE_CASE__	)	)[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(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	):
  UpperCamelCase_					:						Union[str, Any] =					full_name.split('/'	)
  UpperCamelCase_					:						Tuple =					model
  UpperCamelCase_					:						Union[str, Any] =					[]
  for i, m_name in enumerate(SCREAMING_SNAKE_CASE__	):
   if m_name == ".ATTRIBUTES":
    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
    break
   if m_name.startswith('layer_with_weights'	):
    UpperCamelCase_					:						List[str] =					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']	)
     UpperCamelCase_					:						List[str] =					getattr(SCREAMING_SNAKE_CASE__  ,  'embeddings'	)
     UpperCamelCase_					:						Optional[int] =					getattr(SCREAMING_SNAKE_CASE__  ,  'LayerNorm'	)
    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
     # encoder layers
     trace.extend(['encoder', 'layer', str(layer_num - 4	)]	)
     UpperCamelCase_					:						Optional[int] =					getattr(SCREAMING_SNAKE_CASE__  ,  'encoder'	)
     UpperCamelCase_					:						int =					getattr(SCREAMING_SNAKE_CASE__  ,  'layer'	)
     UpperCamelCase_					:						List[Any] =					pointer[layer_num - 4]
    elif layer_num == config.num_hidden_layers + 4:
     # pooler layer
     trace.extend(['pooler', 'dense']	)
     UpperCamelCase_					:						Any =					getattr(SCREAMING_SNAKE_CASE__  ,  'pooler'	)
     UpperCamelCase_					:						Union[str, Any] =					getattr(SCREAMING_SNAKE_CASE__  ,  'dense'	)
   elif m_name == "embeddings":
    trace.append('embeddings'	)
    UpperCamelCase_					:						str =					getattr(SCREAMING_SNAKE_CASE__  ,  'embeddings'	)
    if layer_num == 0:
     trace.append('word_embeddings'	)
     UpperCamelCase_					:						Optional[Any] =					getattr(SCREAMING_SNAKE_CASE__  ,  'word_embeddings'	)
    elif layer_num == 1:
     trace.append('position_embeddings'	)
     UpperCamelCase_					:						Tuple =					getattr(SCREAMING_SNAKE_CASE__  ,  'position_embeddings'	)
    elif layer_num == 2:
     trace.append('token_type_embeddings'	)
     UpperCamelCase_					:						Any =					getattr(SCREAMING_SNAKE_CASE__  ,  'token_type_embeddings'	)
    else:
     raise ValueError(F"Unknown embedding layer with name {full_name}"	)
    trace.append('weight'	)
    UpperCamelCase_					:						str =					getattr(SCREAMING_SNAKE_CASE__  ,  'weight'	)
   elif m_name == "_attention_layer":
    # self-attention layer
    trace.extend(['attention', 'self']	)
    UpperCamelCase_					:						str =					getattr(SCREAMING_SNAKE_CASE__  ,  'attention'	)
    UpperCamelCase_					:						Tuple =					getattr(SCREAMING_SNAKE_CASE__  ,  'self'	)
   elif m_name == "_attention_layer_norm":
    # output attention norm
    trace.extend(['attention', 'output', 'LayerNorm']	)
    UpperCamelCase_					:						Dict =					getattr(SCREAMING_SNAKE_CASE__  ,  'attention'	)
    UpperCamelCase_					:						Tuple =					getattr(SCREAMING_SNAKE_CASE__  ,  'output'	)
    UpperCamelCase_					:						str =					getattr(SCREAMING_SNAKE_CASE__  ,  'LayerNorm'	)
   elif m_name == "_attention_output_dense":
    # output attention dense
    trace.extend(['attention', 'output', 'dense']	)
    UpperCamelCase_					:						List[str] =					getattr(SCREAMING_SNAKE_CASE__  ,  'attention'	)
    UpperCamelCase_					:						Tuple =					getattr(SCREAMING_SNAKE_CASE__  ,  'output'	)
    UpperCamelCase_					:						Dict =					getattr(SCREAMING_SNAKE_CASE__  ,  'dense'	)
   elif m_name == "_output_dense":
    # output dense
    trace.extend(['output', 'dense']	)
    UpperCamelCase_					:						Tuple =					getattr(SCREAMING_SNAKE_CASE__  ,  'output'	)
    UpperCamelCase_					:						Any =					getattr(SCREAMING_SNAKE_CASE__  ,  'dense'	)
   elif m_name == "_output_layer_norm":
    # output dense
    trace.extend(['output', 'LayerNorm']	)
    UpperCamelCase_					:						List[str] =					getattr(SCREAMING_SNAKE_CASE__  ,  'output'	)
    UpperCamelCase_					:						Optional[Any] =					getattr(SCREAMING_SNAKE_CASE__  ,  'LayerNorm'	)
   elif m_name == "_key_dense":
    # attention key
    trace.append('key'	)
    UpperCamelCase_					:						int =					getattr(SCREAMING_SNAKE_CASE__  ,  'key'	)
   elif m_name == "_query_dense":
    # attention query
    trace.append('query'	)
    UpperCamelCase_					:						Tuple =					getattr(SCREAMING_SNAKE_CASE__  ,  'query'	)
   elif m_name == "_value_dense":
    # attention value
    trace.append('value'	)
    UpperCamelCase_					:						Optional[int] =					getattr(SCREAMING_SNAKE_CASE__  ,  'value'	)
   elif m_name == "_intermediate_dense":
    # attention intermediate dense
    trace.extend(['intermediate', 'dense']	)
    UpperCamelCase_					:						Optional[Any] =					getattr(SCREAMING_SNAKE_CASE__  ,  'intermediate'	)
    UpperCamelCase_					:						Optional[Any] =					getattr(SCREAMING_SNAKE_CASE__  ,  'dense'	)
   elif m_name == "_output_layer_norm":
    # output layer norm
    trace.append('output'	)
    UpperCamelCase_					:						str =					getattr(SCREAMING_SNAKE_CASE__  ,  'output'	)
   # weights & biases
   elif m_name in ["bias", "beta"]:
    trace.append('bias'	)
    UpperCamelCase_					:						Optional[Any] =					getattr(SCREAMING_SNAKE_CASE__  ,  'bias'	)
   elif m_name in ["kernel", "gamma"]:
    trace.append('weight'	)
    UpperCamelCase_					:						Optional[Any] =					getattr(SCREAMING_SNAKE_CASE__  ,  'weight'	)
   else:
    logger.warning(F"Ignored {m_name}"	)
        # for certain layers reshape is necessary
  UpperCamelCase_					:						Any =					'.'.join(SCREAMING_SNAKE_CASE__	)
  if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'  ,  SCREAMING_SNAKE_CASE__	) or re.match(
      R'(\S+)\.attention\.output\.dense\.weight'  ,  SCREAMING_SNAKE_CASE__	):
   UpperCamelCase_					:						Optional[int] =					array.reshape(pointer.data.shape	)
  if "kernel" in full_name:
   UpperCamelCase_					:						List[Any] =					array.transpose()
  if pointer.shape == array.shape:
   UpperCamelCase_					:						List[str] =					torch.from_numpy(SCREAMING_SNAKE_CASE__	)
  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    __lowercase     (						lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int	):
 logger.info(F"Loading model based on config from {config_path}..."	)
 UpperCamelCase_					:						Union[str, Any] =					BertConfig.from_json_file(SCREAMING_SNAKE_CASE__	)
 UpperCamelCase_					:						Optional[int] =					BertModel(SCREAMING_SNAKE_CASE__	)

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

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


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






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							't5'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self :       Union[str, Any],lowercase_ :       int=3_2_1_2_8,lowercase_ :       int=5_1_2,lowercase_ :       List[str]=6_4,lowercase_ :       Tuple=2_0_4_8,lowercase_ :       Any=6,lowercase_ :       List[str]=None,lowercase_ :       Union[str, Any]=8,lowercase_ :       int=3_2,lowercase_ :       Dict=1_2_8,lowercase_ :       Optional[int]=0.1,lowercase_ :       List[str]=1E-6,lowercase_ :       Tuple=1.0,lowercase_ :       Any="relu",lowercase_ :       Union[str, Any]=True,lowercase_ :       Optional[Any]=True,lowercase_ :       int=0,lowercase_ :       str=1,**lowercase_ :       str,)->    Any:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   d_model
        A__          =   d_kv
        A__          =   d_ff
        A__          =   num_layers
        A__          =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A__          =   num_heads
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   feed_forward_proj
        A__          =   use_cache

        A__          =   self.feed_forward_proj.split('-'		)
        A__          =   act_info[-1]
        A__          =   act_info[0] == 'gated'

        if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 2:
            raise ValueError(
                F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                '\'gated-gelu\' or \'relu\''		)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            A__          =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,**lowercase_,)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    def 						snake_case__     (      self :       Tuple		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        A__          =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
            A__          =   'past_encoder_sequence + sequence'
            A__          =   {0: 'batch'}
            A__          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
            A__          =   {0: 'batch', 1: 'decoder_sequence'}
            A__          =   {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
            self.fill_with_past_key_values_(lowercase_,direction='inputs'		)

        return common_inputs


    @property
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        return 1_3

