



'''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 				lowerCamelCase__	(						_A			,      _A			,      _A=0    ):
  # Format the message.
  if name is None:
    a						: Tuple					       =					None
  else:
    a						: Dict					       =					'.' * max(0			,      spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
    a						: Tuple					       =					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    ):
  # 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.
  a						: str					       =					param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    a						: List[Any]					       =					(num_heads, hidden_size, num_splits) + input_shape[1:]
    a						: int					       =					param.view(*_A    )
    a						: List[str]					       =					param.transpose(0			,      2    )
    a						: Union[str, Any]					       =					param.transpose(1			,      2    ).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    a						: Union[str, Any]					       =					(num_heads, num_splits, hidden_size) + input_shape[1:]
    a						: List[str]					       =					param.view(*_A    )
    a						: Union[str, Any]					       =					param.transpose(0			,      1    ).contiguous()
  a						: List[Any]					       =					param.view(*_A    )
  return param
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  # The converted output model.
  a						: Optional[Any]					       =					{}

  # old versions did not store training args
  a						: Dict					       =					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						: Union[str, Any]					       =					ds_args.padded_vocab_size
    a						: str					       =					ds_args.max_position_embeddings
    a						: Dict					       =					ds_args.hidden_size
    a						: Union[str, Any]					       =					ds_args.num_layers
    a						: Dict					       =					ds_args.num_attention_heads
    a						: int					       =					ds_args.ffn_hidden_size
    # pprint(config)

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

  # The model.
  a						: Optional[int]					       =					input_state_dict['model']
  # The language model.
  a						: Optional[Any]					       =					model['language_model']
  # The embeddings.
  a						: List[str]					       =					lm['embedding']

  # The word embeddings.
  a						: List[Any]					       =					embeddings['word_embeddings']['weight']
  # Truncate the embedding table to vocab_size rows.
  a						: Dict					       =					word_embeddings[: config.vocab_size, :]
  a						: int					       =					word_embeddings

  # The position embeddings.
  a						: Tuple					       =					embeddings['position_embeddings']['weight']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  a						: List[str]					       =					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						: Optional[Any]					       =					pos_embeddings

  # The transformer.
  a						: Union[str, Any]					       =					lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

  # The simple map of names for "automated" rules.
  a						: Optional[Any]					       =					{
      '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						: Tuple					       =					layer_re.match(_A    )

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

    # The index of the layer.
    a						: Union[str, Any]					       =					int(m.group(1    )    )
    # The name of the operation.
    a						: Optional[int]					       =					m.group(2    )
    # Is it a weight or a bias?
    a						: Optional[int]					       =					m.group(3    )

    # The name of the layer.
    a						: Any					       =					f"""transformer.h.{layer_idx}"""

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'    ):
      a						: str					       =					'ln_1' if op_name.startswith('input'    ) else 'ln_2'
      a						: Tuple					       =					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						: Dict					       =					torch.tril(torch.ones((n_positions, n_positions)			,      dtype=torch.floataa    )    ).view(
          1			,      1			,      _A			,      _A    )
      a						: Optional[Any]					       =					causal_mask

      # Insert a "dummy" tensor for masked_bias.
      a						: List[Any]					       =					torch.tensor(-1E4			,      dtype=torch.floataa    )
      a						: List[str]					       =					masked_bias

      a						: Union[str, Any]					       =					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						: int					       =					out_val.transpose(0			,      1    ).contiguous()
      # Store.
      a						: int					       =					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						: str					       =					fix_query_key_value_ordering(_A			,      _A			,      3			,      _A			,      _A    )
      # Store. No change of shape.
      a						: List[str]					       =					out_val

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

    # Copy the bias.
    elif weight_or_bias == "bias":
      a						: Dict					       =					megatron_to_transformers[op_name]
      a						: Optional[Any]					       =					val

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

  # The final layernorm.
  a						: str					       =					transformer['final_layernorm.weight']
  a						: List[str]					       =					transformer['final_layernorm.bias']

  # For LM head, transformers' wants the matrix to weight embeddings.
  a						: Optional[int]					       =					word_embeddings

  # It should be done!
  return output_state_dict
def 				lowerCamelCase__	(						):
  # Create the argument parser.
  a						: Dict					       =					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						: Union[str, Any]					       =					parser.parse_args()

  # Extract the basename.
  a						: Optional[Any]					       =					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						: Union[str, Any]					       =					torch.load(_A			,      map_location='cpu'    )
  else:
    a						: Any					       =					torch.load(args.path_to_checkpoint			,      map_location='cpu'    )

  a						: List[Any]					       =					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						: int					       =					'gelu_fast'
      elif ds_args.openai_gelu:
        a						: Dict					       =					'gelu_new'
      else:
        a						: Any					       =					'gelu'
    else:
      # in the very early days this used to be "gelu_new"
      a						: Any					       =					'gelu_new'

    # Spell out all parameters in case the defaults change.
    a						: Tuple					       =					GPTaConfig(
        vocab_size=5_0257			,      n_positions=1024			,      n_embd=1024			,      n_layer=24			,      n_head=16			,      n_inner=4096			,      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=5_0256			,      eos_token_id=5_0256			,      )
  else:
    a						: str					       =					GPTaConfig.from_json_file(args.config_file    )

  a						: Any					       =					['GPT2LMHeadModel']

  # Convert.
  print('Converting'    )
  a						: Union[str, Any]					       =					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						: Union[str, Any]					       =					ds_args.tokenizer_type
    if tokenizer_type == "GPT2BPETokenizer":
      a						: Tuple					       =					'gpt2'
    elif tokenizer_type == "PretrainedFromHF":
      a						: List[str]					       =					ds_args.tokenizer_name_or_path
    else:
      raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}"""    )
  else:
    a						: Optional[Any]					       =					'gpt2'

  a						: Tuple					       =					AutoTokenizer.from_pretrained(_A    )
  a						: str					       =					type(_A    ).__name__
  a						: List[str]					       =					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						: Optional[int]					       =					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'''






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class 							a__:







    def __init__(				self			:  Optional[int]     ):
      a						: int					       =					''
      a						: List[str]					       =					''
      a						: int					       =					[]
      a						: Optional[Any]					       =					0
      a						: Optional[Any]					       =					2_56
      a						: int					       =					0
      a						: Optional[int]					       =					0
      a						: str					       =					0
      a						: int					       =					0







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  int     ):
      a						: Optional[Any]					       =					cva.imread(__snake_case						,      0     )
      a						: int					       =					copy.deepcopy(self.img     )
      a       ,     a       ,     a						: Optional[int]					       =					plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]						,      label='x'     )
      a						: str					       =					np.sum(__snake_case     )
      for i in range(len(__snake_case     )     ):
        a						: List[str]					       =					x[i] / self.k
        self.sk += prk
        a						: List[Any]					       =					(self.L - 1) * self.sk
        if self.rem != 0:
          a						: Union[str, Any]					       =					int(last % last     )
        a						: int					       =					int(last + 1 if self.rem >= 0.5 else last     )
        self.last_list.append(__snake_case     )
        a						: int					       =					int(np.ma.count(self.img     ) / self.img[1].size     )
        a						: Dict					       =					self.img[1].size
      for i in range(self.number_of_cols     ):
        for j in range(self.number_of_rows     ):
          a						: Tuple					       =					self.img[j][i]
          if num != self.last_list[num]:
            a						: Union[str, Any]					       =					self.last_list[num]
      cva.imwrite('output_data/output.jpg'						,      self.img     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]     )







    def       lowercase_	(				self			:  Any     ):
      cva.imshow('Output-Image'						,      self.img     )
      cva.imshow('Input-Image'						,      self.original_image     )
      cva.waitKey(50_00     )
      cva.destroyAllWindows()


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
     lowerCAmelCase: Optional[Any]							=						ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()