


'''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()
__UpperCAmelCase					       =							logging.get_logger(__name__)


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

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

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

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


if __name__ == "__main__":
		__UpperCAmelCase					       =							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).''',
		)
		__UpperCAmelCase					       =							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


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

							lowerCAmelCase__      =			jnp.where(shifted_input_ids == -100				,					A				,					A )
							return shifted_input_ids




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig