



'''simple docstring'''





import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig




def lowerCAmelCase  (__A  ,	__A  ,	__A=0):



     """simple docstring"""



     if name is None:
          _a      =  None
     else:
          _a      =  '''.''' * max(0  ,	spaces - 2) + '''# {:''' + str(50 - spaces) + '''s}'''
          _a      =  fmt.format(_A)

     # Print and recurse (if needed).
     if isinstance(_A  ,	_A):
          if msg is not None:
               print(_A)
          for k in val.keys():
               recursive_print(_A  ,	val[k]  ,	spaces + 2)
     elif isinstance(_A  ,	torch.Tensor):
          print(_A  ,	''':'''  ,	val.size())
     else:
          print(_A  ,	''':'''  ,	_A)




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          _a      =  (num_heads, hidden_size, num_splits) + input_shape[1:]
          _a      =  param.view(*_A)
          _a      =  param.transpose(0  ,	2)
          _a      =  param.transpose(1  ,	2).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          _a      =  (num_heads, num_splits, hidden_size) + input_shape[1:]
          _a      =  param.view(*_A)
          _a      =  param.transpose(0  ,	1).contiguous()
     _a      =  param.view(*_A)
     return param




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  {}

     # old versions did not store training args
     _a      =  input_state_dict.get('''args'''  ,	_A)
     if ds_args is not None:
          # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
          # from pprint import pprint
          # pprint(vars(ds_args))

          _a      =  ds_args.padded_vocab_size
          _a      =  ds_args.max_position_embeddings
          _a      =  ds_args.hidden_size
          _a      =  ds_args.num_layers
          _a      =  ds_args.num_attention_heads
          _a      =  ds_args.ffn_hidden_size
          # pprint(config)

     # The number of heads.
     _a      =  config.n_head
     # The hidden_size per head.
     _a      =  config.n_embd // config.n_head
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          _a      =  input_state_dict['''checkpoint_version''']
     else:
          _a      =  0.0

     # The model.
     _a      =  input_state_dict['''model''']
     # The language model.
     _a      =  model['''language_model''']
     # The embeddings.
     _a      =  lm['''embedding''']

     # The word embeddings.
     _a      =  embeddings['''word_embeddings''']['''weight''']
     # Truncate the embedding table to vocab_size rows.
     _a      =  word_embeddings[: config.vocab_size, :]
     _a      =  word_embeddings

     # The position embeddings.
     _a      =  embeddings['''position_embeddings''']['''weight''']
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     _a      =  pos_embeddings.size(0)
     if n_positions != config.n_positions:
          raise ValueError(
              F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''')
     # Store the position embeddings.
     _a      =  pos_embeddings

     # The transformer.
     _a      =  lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

     # The regex to extract layer names.
     _a      =  re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)''')

     # The simple map of names for "automated" rules.
     _a      =  {
         '''attention.dense''': '''.attn.c_proj.''',
         '''self_attention.dense''': '''.attn.c_proj.''',
         '''mlp.dense_h_to_4h''': '''.mlp.c_fc.''',
         '''mlp.dense_4h_to_h''': '''.mlp.c_proj.''',
     }

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          _a      =  layer_re.match(_A)

          # Stop if that's not a layer
          if m is None:
               break

          # The index of the layer.
          _a      =  int(m.group(1))
          # The name of the operation.
          _a      =  m.group(2)
          # Is it a weight or a bias?
          _a      =  m.group(3)

          # The name of the layer.
          _a      =  F'''transformer.h.{layer_idx}'''

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('''layernorm'''):
               _a      =  '''ln_1''' if op_name.startswith('''input''') else '''ln_2'''
               _a      =  val

          # Transpose the QKV matrix.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "weight":
               # Insert a tensor of 1x1xDxD bias.
               _a      =  torch.tril(torch.ones((n_positions, n_positions)  ,	dtype=torch.floataa)).view(
                   1  ,	1  ,	_A  ,	_A)
               _a      =  causal_mask

               # Insert a "dummy" tensor for masked_bias.
               _a      =  torch.tensor(-1e4  ,	dtype=torch.floataa)
               _a      =  masked_bias

               _a      =  fix_query_key_value_ordering(_A  ,	_A  ,	3  ,	_A  ,	_A)
               # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
               _a      =  out_val.transpose(0  ,	1).contiguous()
               # Store.
               _a      =  out_val

          # Transpose the bias.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "bias":
               _a      =  fix_query_key_value_ordering(_A  ,	_A  ,	3  ,	_A  ,	_A)
               # Store. No change of shape.
               _a      =  out_val

          # Transpose the weights.
          elif weight_or_bias == "weight":
               _a      =  megatron_to_transformers[op_name]
               _a      =  val.transpose(0  ,	1)

          # Copy the bias.
          elif weight_or_bias == "bias":
               _a      =  megatron_to_transformers[op_name]
               _a      =  val

    # DEBUG.
     assert config.n_layer == layer_idx + 1

     # The final layernorm.
     _a      =  transformer['''final_layernorm.weight''']
     _a      =  transformer['''final_layernorm.bias''']

     # For LM head, transformers' wants the matrix to weight embeddings.
     _a      =  word_embeddings

     # It should be done!
     return output_state_dict




def lowerCAmelCase  ():



     """simple docstring"""



     _a      =  argparse.ArgumentParser()
     parser.add_argument('''--print-checkpoint-structure'''  ,	action='''store_true''')
     parser.add_argument(
         '''path_to_checkpoint'''  ,	type=_A  ,	help='''Path to the checkpoint file (.zip archive or direct .pt file)'''  ,	)
     parser.add_argument(
         '''--config_file'''  ,	default=''''''  ,	type=_A  ,	help='''An optional config json file describing the pre-trained model.'''  ,	)
     _a      =  parser.parse_args()

     # Extract the basename.
     _a      =  os.path.dirname(args.path_to_checkpoint)

     # Load the model.
     # the .zip is very optional, let's keep it for backward compatibility
     print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''')
     if args.path_to_checkpoint.endswith('''.zip'''):
          with zipfile.ZipFile(args.path_to_checkpoint  ,	'''r''') as checkpoint:
               with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt''') as pytorch_dict:
                    _a      =  torch.load(_A  ,	map_location='''cpu''')
     else:
          _a      =  torch.load(args.path_to_checkpoint  ,	map_location='''cpu''')

     _a      =  input_state_dict.get('''args'''  ,	_A)

     # Read the config, or default to the model released by NVIDIA.
     if args.config_file == "":
          if ds_args is not None:
               if ds_args.bias_gelu_fusion:
                    _a      =  '''gelu_fast'''
               elif ds_args.openai_gelu:
                    _a      =  '''gelu_new'''
               else:
                    _a      =  '''gelu'''
          else:
               # in the very early days this used to be "gelu_new"
               _a      =  '''gelu_new'''

          # Spell out all parameters in case the defaults change.
          _a      =  GPTaConfig(
              vocab_size=50_257  ,	n_positions=1_024  ,	n_embd=1_024  ,	n_layer=24  ,	n_head=16  ,	n_inner=4_096  ,	activation_function=_A  ,	resid_pdrop=0.1  ,	embd_pdrop=0.1  ,	attn_pdrop=0.1  ,	layer_norm_epsilon=1e-5  ,	initializer_range=0.02  ,	summary_type='''cls_index'''  ,	summary_use_proj=_A  ,	summary_activation=_A  ,	summary_proj_to_labels=_A  ,	summary_first_dropout=0.1  ,	scale_attn_weights=_A  ,	use_cache=_A  ,	bos_token_id=50_256  ,	eos_token_id=50_256  ,	)
     else:
          _a      =  GPTaConfig.from_json_file(args.config_file)

     _a      =  ['''GPT2LMHeadModel''']

     # Convert.
     print('''Converting''')
     _a      =  convert_megatron_checkpoint(_A  ,	_A  ,	_A)

     # Print the structure of converted state dict.
     if args.print_checkpoint_structure:
          recursive_print(_A  ,	_A)

     # Add tokenizer class info to config
     # see https://github.com/huggingface/transformers/issues/13906)
     if ds_args is not None:
          _a      =  ds_args.tokenizer_type
          if tokenizer_type == "GPT2BPETokenizer":
               _a      =  '''gpt2'''
          elif tokenizer_type == "PretrainedFromHF":
               _a      =  ds_args.tokenizer_name_or_path
          else:
               raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''')
     else:
          _a      =  '''gpt2'''

     _a      =  AutoTokenizer.from_pretrained(_A)
     _a      =  type(_A).__name__
     _a      =  tokenizer_class

     # Store the config to file.
     print('''Saving config''')
     config.save_pretrained(_A)

     # Save tokenizer based on args
     print(F'''Adding {tokenizer_class} tokenizer files''')
     tokenizer.save_pretrained(_A)

     # Store the state_dict to file.
     _a      =  os.path.join(_A  ,	'''pytorch_model.bin''')
     print(F'''Saving checkpoint to \"{output_checkpoint_file}\"''')
     torch.save(_A  ,	_A)


####################################################################################################

if __name__ == "__main__":
  main()

####################################################################################################




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase          =  {
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ResNetForImageClassification''',
						    '''ResNetModel''',
						    '''ResNetPreTrainedModel''',
						    '''ResNetBackbone''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFResNetForImageClassification''',
						    '''TFResNetModel''',
						    '''TFResNetPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''FlaxResNetForImageClassification''',
						    '''FlaxResNetModel''',
						    '''FlaxResNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_resnet import (
												    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ResNetBackbone,
												    ResNetForImageClassification,
												    ResNetModel,
												    ResNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_resnet import (
												    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFResNetForImageClassification,
												    TFResNetModel,
												    TFResNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure)




