





'''simple docstring'''

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__		:     Dict			=							logging.get_logger(__name__)







def    a__							(							lowercase   :	List[str],   lowercase   :	List[Any],   lowercase   :	Union[str, Any] )						->						Optional[Any]:





     """simple docstring"""

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

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





     """simple docstring"""

     logger.info(F"""Loading model based on config from {config_path}...""" )
     _UpperCamelCase							       =			BertConfig.from_json_file(lowercase )
     _UpperCamelCase							       =			BertModel(lowercase )

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

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


if __name__ == "__main__":
   lowercase__		:     Tuple			=							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__		:     Union[str, Any]			=							parser.parse_args()
   convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










'''simple docstring'''

import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase__		:     int			=							logging.get_logger(__name__)

lowercase__		:     Any			=							'T5Config'







def    a__							(							lowercase   :	jnp.array,   lowercase   :	int,   lowercase   :	int )						->						jnp.ndarray:





     """simple docstring"""

     _UpperCamelCase							       =			jnp.zeros_like(lowercase )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 0].set(lowercase )

     _UpperCamelCase							       =			jnp.where(shifted_input_ids == -100,   lowercase,   lowercase )
     return shifted_input_ids
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'mt5'
       _snake_case      :	Union[str, Any]									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Tuple									=       'mt5'
       _snake_case      :	int									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       'mt5'
       _snake_case      :	Optional[Any]									=       MTaConfig



