


'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


a_					:    Any          =    logging.get_logger(__name__)






def 	_A				(lowerCAmelCase__  :Tuple		)   ->  List[List[ImageInput]]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__				,						(list, tuple)		) and isinstance(videos[0]				,						(list, tuple)		) and is_valid_image(videos[0][0]		):
          return videos

     elif isinstance(lowerCAmelCase__				,						(list, tuple)		) and is_valid_image(videos[0]		):
          return [videos]

     elif is_valid_image(lowerCAmelCase__		):
          return [[videos]]

     raise ValueError(f'Could not make batched video from {videos}'		)






class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""pixel_values"""]
 def __init__( self      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = PILImageResampling.BILINEAR      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = True      ,				__magic_name__ = 1 / 2_55      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							None:
      super().__init__(**__magic_name__						)
      _a			      =							size if size is not None else {'shortest_edge': 2_24}
      _a			      =							get_size_dict(__magic_name__      ,				default_to_square=__magic_name__						)
      _a			      =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      _a			      =							get_size_dict(__magic_name__      ,				param_name='crop_size'						)

      _a			      =							do_resize
      _a			      =							size
      _a			      =							do_center_crop
      _a			      =							crop_size
      _a			      =							resample
      _a			      =							do_rescale
      _a			      =							rescale_factor
      _a			      =							do_normalize
      _a			      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _a			      =							image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = PILImageResampling.BILINEAR      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      _a			      =							get_size_dict(__magic_name__      ,				default_to_square=__magic_name__						)
      if "shortest_edge" in size:
           _a			      =							get_resize_output_image_size(__magic_name__      ,				size['shortest_edge']      ,				default_to_square=__magic_name__						)
      elif "height" in size and "width" in size:
           _a			      =							(size['height'], size['width'])
      else:
           raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
      return resize(__magic_name__      ,				size=__magic_name__      ,				resample=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      _a			      =							get_size_dict(__magic_name__						)
      if "height" not in size or "width" not in size:
           raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'						)
      return center_crop(__magic_name__      ,				size=(size['height'], size['width'])      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							Tuple:
      return rescale(__magic_name__      ,				scale=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      return normalize(__magic_name__      ,				mean=__magic_name__      ,				std=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = ChannelDimension.FIRST      ,				)					->							np.ndarray:
      if do_resize and size is None or resample is None:
           raise ValueError('Size and resample must be specified if do_resize is True.'						)

      if do_center_crop and crop_size is None:
           raise ValueError('Crop size must be specified if do_center_crop is True.'						)

      if do_rescale and rescale_factor is None:
           raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

      if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

      # All transformations expect numpy arrays.
      _a			      =							to_numpy_array(__magic_name__						)

      if do_resize:
           _a			      =							self.resize(image=__magic_name__      ,				size=__magic_name__      ,				resample=__magic_name__						)

      if do_center_crop:
           _a			      =							self.center_crop(__magic_name__      ,				size=__magic_name__						)

      if do_rescale:
           _a			      =							self.rescale(image=__magic_name__      ,				scale=__magic_name__						)

      if do_normalize:
           _a			      =							self.normalize(image=__magic_name__      ,				mean=__magic_name__      ,				std=__magic_name__						)

      _a			      =							to_channel_dimension_format(__magic_name__      ,				__magic_name__						)
      return image




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = ChannelDimension.FIRST      ,				**__magic_name__      ,				)					->							PIL.Image.Image:
      _a			      =							do_resize if do_resize is not None else self.do_resize
      _a			      =							resample if resample is not None else self.resample
      _a			      =							do_center_crop if do_center_crop is not None else self.do_center_crop
      _a			      =							do_rescale if do_rescale is not None else self.do_rescale
      _a			      =							rescale_factor if rescale_factor is not None else self.rescale_factor
      _a			      =							do_normalize if do_normalize is not None else self.do_normalize
      _a			      =							image_mean if image_mean is not None else self.image_mean
      _a			      =							image_std if image_std is not None else self.image_std

      _a			      =							size if size is not None else self.size
      _a			      =							get_size_dict(__magic_name__      ,				default_to_square=__magic_name__						)
      _a			      =							crop_size if crop_size is not None else self.crop_size
      _a			      =							get_size_dict(__magic_name__      ,				param_name='crop_size'						)

      if not valid_images(__magic_name__						):
           raise ValueError(
               'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
               'torch.Tensor, tf.Tensor or jax.ndarray.'						)

      _a			      =							make_batched(__magic_name__						)

      _a			      =							[
          [
              self._preprocess_image(
                  image=__magic_name__      ,				do_resize=__magic_name__      ,				size=__magic_name__      ,				resample=__magic_name__      ,				do_center_crop=__magic_name__      ,				crop_size=__magic_name__      ,				do_rescale=__magic_name__      ,				rescale_factor=__magic_name__      ,				do_normalize=__magic_name__      ,				image_mean=__magic_name__      ,				image_std=__magic_name__      ,				data_format=__magic_name__      ,				)
              for img in video
          ]
          for video in videos
      ]

      _a			      =							{'pixel_values': videos}
      return BatchFeature(data=__magic_name__      ,				tensor_type=__magic_name__						)





'''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 	_A				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[int]=0		)   ->  Optional[int]:




     '''simple docstring'''



     if name is None:
          _a			      =							None
     else:
          _a			      =							'.' * max(0				,						spaces - 2		) + '# {:' + str(50 - spaces		) + 's}'
          _a			      =							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 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[str]		)   ->  Any:




     '''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(*lowerCAmelCase__		)
          _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(*lowerCAmelCase__		)
          _a			      =							param.transpose(0				,						1		).contiguous()
     _a			      =							param.view(*lowerCAmelCase__		)
     return param






def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[str]		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							{}

     # old versions did not store training args
     _a			      =							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))

          _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(lowerCAmelCase__		)

          # 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				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               _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(lowerCAmelCase__				,						lowerCAmelCase__				,						3				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               # 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(lowerCAmelCase__				,						lowerCAmelCase__				,						3				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               # 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 	_A				()   ->  Any:




     '''simple docstring'''



     _a			      =							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.'				,						)
     _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(lowerCAmelCase__				,						map_location='cpu'		)
     else:
          _a			      =							torch.load(args.path_to_checkpoint				,						map_location='cpu'		)

     _a			      =							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:
                    _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=5_02_57				,						n_positions=10_24				,						n_embd=10_24				,						n_layer=24				,						n_head=16				,						n_inner=40_96				,						activation_function=lowerCAmelCase__				,						resid_pdrop=0.1				,						embd_pdrop=0.1				,						attn_pdrop=0.1				,						layer_norm_epsilon=1E-5				,						initializer_range=0.0_2				,						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=5_02_56				,						eos_token_id=5_02_56				,						)
     else:
          _a			      =							GPTaConfig.from_json_file(args.config_file		)

     _a			      =							['GPT2LMHeadModel']

     # Convert.
     print('Converting'		)
     _a			      =							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:
          _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(lowerCAmelCase__		)
     _a			      =							type(lowerCAmelCase__		).__name__
     _a			      =							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.
     _a			      =							os.path.join(lowerCAmelCase__				,						'pytorch_model.bin'		)
     print(f'Saving checkpoint to "{output_checkpoint_file}"'		)
     torch.save(lowerCAmelCase__				,						lowerCAmelCase__		)


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

if __name__ == "__main__":
 main()

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

