



"""simple docstring"""






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=0	)						->				Any:
  # Format the message.
  if name is None:
    _snake_case   	=      None
  else:
    _snake_case   	=      '''.''' * max(0  ,						spaces - 2	) + '''# {:''' + str(50 - spaces	) + '''s}'''
    _snake_case   	=      fmt.format(lowerCAmelCase_	)

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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Dict:
  # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
  # for compatibility with later versions of NVIDIA Megatron-LM.
  # The inverse operation is performed inside Megatron-LM to read checkpoints:
  # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
  # If param is the weight tensor of the self-attention block, the returned tensor
  # will have to be transposed one more time to be read by HuggingFace GPT2.
  _snake_case   	=      param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    _snake_case   	=      (num_heads, hidden_size, num_splits) + input_shape[1:]
    _snake_case   	=      param.view(*lowerCAmelCase_	)
    _snake_case   	=      param.transpose(0  ,						2	)
    _snake_case   	=      param.transpose(1  ,						2	).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    _snake_case   	=      (num_heads, num_splits, hidden_size) + input_shape[1:]
    _snake_case   	=      param.view(*lowerCAmelCase_	)
    _snake_case   	=      param.transpose(0  ,						1	).contiguous()
  _snake_case   	=      param.view(*lowerCAmelCase_	)
  return param




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Optional[int]:
  # The converted output model.
  _snake_case   	=      {}

  # old versions did not store training args
  _snake_case   	=      input_state_dict.get('''args'''  ,						lowerCAmelCase_	)
  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))

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

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

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

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

  # The position embeddings.
  _snake_case   	=      embeddings['''position_embeddings''']['''weight''']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  _snake_case   	=      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.
  _snake_case   	=      pos_embeddings

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

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

  # The simple map of names for "automated" rules.
  _snake_case   	=      {
      '''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.
    _snake_case   	=      layer_re.match(lowerCAmelCase_	)

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

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

    # The name of the layer.
    _snake_case   	=      f"""transformer.h.{layer_idx}"""

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('''layernorm'''	):
      _snake_case   	=      '''ln_1''' if op_name.startswith('''input'''	) else '''ln_2'''
      _snake_case   	=      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.
      _snake_case   	=      torch.tril(torch.ones((n_positions, n_positions)  ,						dtype=torch.floataa	)	).view(
          1  ,						1  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
      _snake_case   	=      causal_mask

      # Insert a "dummy" tensor for masked_bias.
      _snake_case   	=      torch.tensor(-1E4  ,						dtype=torch.floataa	)
      _snake_case   	=      masked_bias

      _snake_case   	=      fix_query_key_value_ordering(lowerCAmelCase_  ,						lowerCAmelCase_  ,						3  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
      # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
      _snake_case   	=      out_val.transpose(0  ,						1	).contiguous()
      # Store.
      _snake_case   	=      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":
      _snake_case   	=      fix_query_key_value_ordering(lowerCAmelCase_  ,						lowerCAmelCase_  ,						3  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
      # Store. No change of shape.
      _snake_case   	=      out_val

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

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

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

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

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

  # It should be done!
  return output_state_dict




def 			snake_case		(		)						->				Union[str, Any]:
  # Create the argument parser.
  _snake_case   	=      argparse.ArgumentParser()
  parser.add_argument('''--print-checkpoint-structure'''  ,						action='''store_true'''	)
  parser.add_argument(
      '''path_to_checkpoint'''  ,						type=lowerCAmelCase_  ,						help='''Path to the checkpoint file (.zip archive or direct .pt file)'''  ,						)
  parser.add_argument(
      '''--config_file'''  ,						default=''''''  ,						type=lowerCAmelCase_  ,						help='''An optional config json file describing the pre-trained model.'''  ,						)
  _snake_case   	=      parser.parse_args()

  # Extract the basename.
  _snake_case   	=      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:
        _snake_case   	=      torch.load(lowerCAmelCase_  ,						map_location='''cpu'''	)
  else:
    _snake_case   	=      torch.load(args.path_to_checkpoint  ,						map_location='''cpu'''	)

  _snake_case   	=      input_state_dict.get('''args'''  ,						lowerCAmelCase_	)

  # 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:
        _snake_case   	=      '''gelu_fast'''
      elif ds_args.openai_gelu:
        _snake_case   	=      '''gelu_new'''
      else:
        _snake_case   	=      '''gelu'''
    else:
      # in the very early days this used to be "gelu_new"
      _snake_case   	=      '''gelu_new'''

    # Spell out all parameters in case the defaults change.
    _snake_case   	=      GPTaConfig(
        vocab_size=50257  ,						n_positions=1024  ,						n_embd=1024  ,						n_layer=24  ,						n_head=16  ,						n_inner=4096  ,						activation_function=lowerCAmelCase_  ,						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=lowerCAmelCase_  ,						summary_activation=lowerCAmelCase_  ,						summary_proj_to_labels=lowerCAmelCase_  ,						summary_first_dropout=0.1  ,						scale_attn_weights=lowerCAmelCase_  ,						use_cache=lowerCAmelCase_  ,						bos_token_id=50256  ,						eos_token_id=50256  ,						)
  else:
    _snake_case   	=      GPTaConfig.from_json_file(args.config_file	)

  _snake_case   	=      ['''GPT2LMHeadModel''']

  # Convert.
  print('''Converting'''	)
  _snake_case   	=      convert_megatron_checkpoint(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

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

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

  _snake_case   	=      AutoTokenizer.from_pretrained(lowerCAmelCase_	)
  _snake_case   	=      type(lowerCAmelCase_	).__name__
  _snake_case   	=      tokenizer_class

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

  # Save tokenizer based on args
  print(f"""Adding {tokenizer_class} tokenizer files"""	)
  tokenizer.save_pretrained(lowerCAmelCase_	)

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


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

if __name__ == "__main__":
     main()

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










"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  '''openai/whisper-base'''
   A__		:	List[Any]           =  (
       '''This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '''
       '''transcribed text.'''
   )
   A__		:	str           =  '''transcriber'''
   A__		:	List[Any]           =  WhisperProcessor
   A__		:	Optional[int]           =  WhisperForConditionalGeneration

   A__		:	List[str]           =  ['''audio''']
   A__		:	Optional[int]           =  ['''text''']



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     return self.pre_processor(__lowerCamelCase							,       return_tensors='''pt'''				).input_features



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     return self.model.generate(inputs=__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.pre_processor.batch_decode(__lowerCamelCase							,       skip_special_tokens=__lowerCamelCase				)[0]





