


"""simple docstring"""







import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig
def _A						(						lowercase	,   lowercase	,   lowercase=0		):







    """simple docstring"""
    # Format the message.
    if name is None:
        a					=None
    else:
        a					='''.''' * max(0	,   spaces - 2		) + '''# {:''' + str(50 - spaces		) + '''s}'''
        a					=fmt.format(lowercase		)

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







    """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(*lowercase		)
        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(*lowercase		)
        a					=param.transpose(0	,   1		).contiguous()
    a					=param.view(*lowercase		)
    return param
def _A						(						lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    a					={}

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

        # 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	,   lowercase	,   lowercase		)
            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(lowercase	,   lowercase	,   3	,   lowercase	,   lowercase		)
            # 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(lowercase	,   lowercase	,   3	,   lowercase	,   lowercase		)
            # 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						(						):







    """simple docstring"""
    a					=argparse.ArgumentParser()
    parser.add_argument('''--print-checkpoint-structure'''	,   action='''store_true'''		)
    parser.add_argument(
        '''path_to_checkpoint'''	,   type=lowercase	,   help='''Path to the checkpoint file (.zip archive or direct .pt file)'''	,   )
    parser.add_argument(
        '''--config_file'''	,   default=''''''	,   type=lowercase	,   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(lowercase	,   map_location='''cpu'''		)
    else:
        a					=torch.load(args.path_to_checkpoint	,   map_location='''cpu'''		)

    a					=input_state_dict.get('''args'''	,   lowercase		)

    # 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=lowercase	,   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=lowercase	,   summary_activation=lowercase	,   summary_proj_to_labels=lowercase	,   summary_first_dropout=0.1	,   scale_attn_weights=lowercase	,   use_cache=lowercase	,   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(lowercase	,   lowercase	,   lowercase		)

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

    # 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(lowercase		)
    a					=type(lowercase		).__name__
    a					=tokenizer_class

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

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

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


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

if __name__ == "__main__":
       main()

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



"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase_       :       List[Any]      						=  logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  ["pixel_values"]


 def __init__(		self			,   __A = True			,   __A = None			,   __A = PILImageResampling.BICUBIC			,   __A = True			,   __A = None			,   __A = True			,   __A = 1 / 255			,   __A = True			,   __A = None			,   __A = None			,   __A = True			,   **__A			,   ) ->					None:
     super().__init__(**__A						)
     a					=size if size is not None else {'''shortest_edge''': 224}
     a					=get_size_dict(__A			,   default_to_square=__A						)
     a					=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     a					=get_size_dict(__A			,   default_to_square=__A			,   param_name='''crop_size'''						)

     a					=do_resize
     a					=size
     a					=resample
     a					=do_center_crop
     a					=crop_size
     a					=do_rescale
     a					=rescale_factor
     a					=do_normalize
     a					=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     a					=image_std if image_std is not None else OPENAI_CLIP_STD
     a					=do_convert_rgb


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = PILImageResampling.BICUBIC			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     a					=get_size_dict(__A			,   default_to_square=__A						)
     if "shortest_edge" not in size:
         raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
     a					=get_resize_output_image_size(__A			,   size=size['''shortest_edge''']			,   default_to_square=__A						)
     return resize(__A			,   size=__A			,   resample=__A			,   data_format=__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     a					=get_size_dict(__A						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
     return center_crop(__A			,   size=(size['''height'''], size['''width'''])			,   data_format=__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					Any:
     return rescale(__A			,   scale=__A			,   data_format=__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     return normalize(__A			,   mean=__A			,   std=__A			,   data_format=__A			,   **__A						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = ChannelDimension.FIRST			,   **__A			,   ) ->					PIL.Image.Image:
     a					=do_resize if do_resize is not None else self.do_resize
     a					=size if size is not None else self.size
     a					=get_size_dict(__A			,   param_name='''size'''			,   default_to_square=__A						)
     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					=crop_size if crop_size is not None else self.crop_size
     a					=get_size_dict(__A			,   param_name='''crop_size'''			,   default_to_square=__A						)
     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					=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     a					=make_list_of_images(__A						)

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

     if do_resize and size is None:
         raise ValueError('''Size 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.'''						)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
         a					=[convert_to_rgb(__A						) for image in images]

     # All transformations expect numpy arrays.
     a					=[to_numpy_array(__A						) for image in images]

     if do_resize:
         a					=[self.resize(image=__A			,   size=__A			,   resample=__A						) for image in images]

     if do_center_crop:
         a					=[self.center_crop(image=__A			,   size=__A						) for image in images]

     if do_rescale:
         a					=[self.rescale(image=__A			,   scale=__A						) for image in images]

     if do_normalize:
         a					=[self.normalize(image=__A			,   mean=__A			,   std=__A						) for image in images]

     a					=[to_channel_dimension_format(__A			,   __A						) for image in images]

     a					={'''pixel_values''': images}
     return BatchFeature(data=__A			,   tensor_type=__A						)