


"""simple docstring"""







import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

lowerCamelCase_   :   str									=  [
    """word_embeddings_layernorm.weight""",
    """word_embeddings_layernorm.bias""",
    """input_layernorm.weight""",
    """input_layernorm.bias""",
    """post_attention_layernorm.weight""",
    """post_attention_layernorm.bias""",
    """self_attention.dense.bias""",
    """mlp.dense_4h_to_h.bias""",
    """ln_f.weight""",
    """ln_f.bias""",
]

lowerCamelCase_   :   int									=  [
    """mlp.dense_4h_to_h.weight""",
    """self_attention.dense.weight""",
]
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		={
        '''word_embeddings.weight''': '''word_embeddings.weight''',
        '''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
        '''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
        '''weight''': '''ln_f.weight''',
        '''bias''': '''ln_f.bias''',
    }

    if key in layer_rename_map:
        return layer_rename_map[key]

    # Handle transformer blocks
    a 		=int(re.match(R'''.*layer_(\d*).*'''       ,    lowercase		)[1]		)
    layer_number -= 3
    return f'''h.{layer_number}.''' + key
def _A						(						lowercase		):







    """simple docstring"""
    if dtype == torch.bool:
        return 1 / 8
    a 		=re.search(R'''[^\d](\d+)$'''       ,    str(lowercase		)		)
    if bit_search is None:
        raise ValueError(f'''`dtype` is not a valid dtype: {dtype}.'''		)
    a 		=int(bit_search.groups()[0]		)
    return bit_size // 8
def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    # Construct model
    if bloom_config_file == "":
        a 		=BloomConfig()
    else:
        a 		=BloomConfig.from_json_file(lowercase		)

    if shard_model:
        a 		=os.listdir(lowercase		)
        a 		=sorted(filter(lambda lowercase		: s.startswith('''layer'''		) and "model_00" in s       ,    lowercase		)		)

        a 		={'''weight_map''': {}, '''metadata''': {}}
        a 		=0

        a 		=None

        a 		=BloomConfig()

        for j, file in enumerate(lowercase		):
            print('''Processing file: {}'''.format(lowercase		)		)
            a 		=None

            for i in range(lowercase		):
                # load all TP files
                a 		=file.replace('''model_00'''       ,    f'''model_0{i}'''		)
                a 		=torch.load(os.path.join(lowercase       ,    lowercase		)       ,    map_location='''cpu'''		)

                # Rename keys in the transformers names
                a 		=list(temp.keys()		)
                for key in keys:
                    a 		=temp.pop(lowercase		)

                if tensors is None:
                    a 		=temp
                else:
                    for key in tensors.keys():
                        if any(key.endswith(lowercase		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
                            # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                            tensors[key] += temp[key]
                        else:
                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                            a 		=1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN		) else 0
                            # We concatenate these weights accross TP ranks
                            a 		=torch.cat([tensors[key], temp[key]]       ,    dim=lowercase		)

            # Divide by the number of TP the weights we want to average
            for key in tensors.keys():
                if any(key.endswith(lowercase		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
                    a 		=tensors[key] / pretraining_tp
            torch.save(
                lowercase       ,    os.path.join(
                    lowercase       ,    '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1		).zfill(5		)       ,    str(len(lowercase		)		).zfill(5		)		)       ,    )       ,    )

            for key in tensors.keys():
                a 		=tensors[key]
                total_size += value.numel() * get_dtype_size(value.dtype		)
                if key not in index_dict["weight_map"]:
                    a 		='''pytorch_model_{}-of-{}.bin'''.format(
                        str(j + 1		).zfill(5		)       ,    str(len(lowercase		)		).zfill(5		)		)

        a 		=BloomConfig()
        a 		=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
        a 		=total_size
        with open(lowercase       ,    '''w'''       ,    encoding='''utf-8'''		) as f:
            f.write(config.to_json_string()		)
        with open(os.path.join(lowercase       ,    WEIGHTS_NAME + '''.index.json'''		)       ,    '''w'''       ,    encoding='''utf-8'''		) as f:
            a 		=json.dumps(lowercase       ,    indent=2       ,    sort_keys=lowercase		) + '''\n'''
            f.write(lowercase		)
    else:
        a 		=BloomModel(lowercase		)

        a 		=os.listdir(lowercase		)
        a 		=sorted(filter(lambda lowercase		: s.startswith('''layer'''		) and "model_00" in s       ,    lowercase		)		)

        a 		=None
        for i, file in enumerate(lowercase		):
            a 		=None
            for i in range(lowercase		):
                # load all TP files
                a 		=file.replace('''model_00'''       ,    f'''model_0{i}'''		)
                a 		=torch.load(os.path.join(lowercase       ,    lowercase		)       ,    map_location='''cpu'''		)

                # Rename keys in the transformers names
                a 		=list(temp.keys()		)
                for key in keys:
                    a 		=temp.pop(lowercase		)

                if tensors is None:
                    a 		=temp
                else:
                    for key in tensors.keys():
                        # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                        if any(key.endswith(lowercase		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
                            tensors[key] += temp[key]
                        else:
                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                            a 		=1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN		) else 0
                            # We concatenate these weights accross TP ranks
                            a 		=torch.cat([tensors[key], temp[key]]       ,    dim=lowercase		)

            # Divide by the number of TP the weights we want to average
            for key in tensors.keys():
                if any(key.endswith(lowercase		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
                    a 		=tensors[key] / pretraining_tp

            a 		=model.load_state_dict(lowercase       ,    strict=lowercase		)
            assert not other_keys.unexpected_keys, f'''The keys {other_keys.unexpected_keys} are unexpected'''
            if missing_keys is None:
                a 		=set(other_keys.missing_keys		)
            else:
                a 		=missing_keys.intersection(set(other_keys.missing_keys		)		)

        assert not missing_keys, f'''The keys {missing_keys} are missing'''

        # Save pytorch-model
        os.makedirs(lowercase       ,    exist_ok=lowercase		)
        a 		=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
        a 		=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
        print(f'''Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}'''		)
        if config.torch_dtype is not None:
            a 		=model.to(config.torch_dtype		)
        torch.save(model.state_dict()       ,    lowercase		)
        print(f'''Save configuration file to {pytorch_config_dump_path}'''		)
        with open(lowercase       ,    '''w'''       ,    encoding='''utf-8'''		) as f:
            f.write(config.to_json_string()		)


if __name__ == "__main__":
       lowerCamelCase_   :   Any									=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--bloom_checkpoint_path""",
           default=None,
           type=str,
           required=True,
           help="""Path to the Megatron-LM checkpoint path.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--bloom_config_file""",
           default="""""",
           type=str,
           help=(
               """An optional config json file corresponding to the pre-trained model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--shard_model""",
           action="""store_true""",
           help="""An optional setting to shard the output model \nThis enables sharding the converted checkpoint""",
       )
       parser.add_argument(
           """--pretraining_tp""",
           default=4,
           type=int,
           help="""Pretraining TP rank that has been used when training the model in Megatron-LM \n""",
       )
       lowerCamelCase_   :   Dict									=  parser.parse_args()
       convert_bloom_checkpoint_to_pytorch(
           args.bloom_checkpoint_path,
           args.bloom_config_file,
           args.pytorch_dump_folder_path,
           args.shard_model,
           args.pretraining_tp,
       )





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__       :      str  =   logging.get_logger(__name__)

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False    )	-> Any:
       lowerCamelCase   :      Any			 =					[]
       for i in range(config.num_hidden_layers    ):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')    )
              rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')    )
              rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')    )
              rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')    )
              rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')    )
              rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')    )
              rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')    )

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ("cls_token", "deit.embeddings.cls_token"),
               ("dist_token", "deit.embeddings.distillation_token"),
               ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
               ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
               ("pos_embed", "deit.embeddings.position_embeddings"),
           ]    )

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                      ("pre_logits.fc.weight", "pooler.dense.weight"),
                      ("pre_logits.fc.bias", "pooler.dense.bias"),
                  ]    )

              # if just the base model, we should remove "deit" from all keys that start with "deit"
              lowerCamelCase   :      Union[str, Any]			 =					[(pair[0], pair[1][4:]) if pair[1].startswith("deit"    ) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ("norm.weight", "deit.layernorm.weight"),
                      ("norm.bias", "deit.layernorm.bias"),
                      ("head.weight", "cls_classifier.weight"),
                      ("head.bias", "cls_classifier.bias"),
                      ("head_dist.weight", "distillation_classifier.weight"),
                      ("head_dist.bias", "distillation_classifier.bias"),
                  ]    )

       return rename_keys

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False    )	-> str:
       for i in range(config.num_hidden_layers    ):
              if base_model:
                     lowerCamelCase   :      Optional[int]			 =					""
              else:
                     lowerCamelCase   :      List[str]			 =					"deit."
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowerCamelCase   :      List[str]			 =					state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''    )
              lowerCamelCase   :      Optional[int]			 =					state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''    )
              # next, add query, keys and values (in that order) to the state dict
              lowerCamelCase   :      List[Any]			 =					in_proj_weight[
                  : config.hidden_size, :
              ]
              lowerCamelCase   :      Any			 =					in_proj_bias[: config.hidden_size]
              lowerCamelCase   :      List[str]			 =					in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowerCamelCase   :      Optional[Any]			 =					in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowerCamelCase   :      List[str]			 =					in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowerCamelCase   :      List[Any]			 =					in_proj_bias[-config.hidden_size :]

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      List[str]			 =					dct.pop(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Any			 =					val

def      A      (      )	-> List[str]:
       lowerCamelCase   :      Union[str, Any]			 =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase   :      str			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    )
       return im

@torch.no_grad()
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[Any]:
       lowerCamelCase   :      Union[str, Any]			 =					DeiTConfig()
       # all deit models have fine-tuned heads
       lowerCamelCase   :      Optional[int]			 =					False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       lowerCamelCase   :      Dict			 =					1000
       lowerCamelCase   :      Tuple			 =					"huggingface/label-files"
       lowerCamelCase   :      List[str]			 =					"imagenet-1k-id2label.json"
       lowerCamelCase   :      List[Any]			 =					json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,repo_type="dataset"    ) ,"r"    )    )
       lowerCamelCase   :      Optional[int]			 =					{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
       lowerCamelCase   :      Tuple			 =					idalabel
       lowerCamelCase   :      str			 =					{v: k for k, v in idalabel.items()}
       lowerCamelCase   :      Dict			 =					int(deit_name[-6:-4]    )
       lowerCamelCase   :      Optional[Any]			 =					int(deit_name[-3:]    )
       # size of the architecture
       if deit_name[9:].startswith("tiny"    ):
              lowerCamelCase   :      Optional[Any]			 =					192
              lowerCamelCase   :      List[str]			 =					768
              lowerCamelCase   :      Tuple			 =					12
              lowerCamelCase   :      Optional[Any]			 =					3
       elif deit_name[9:].startswith("small"    ):
              lowerCamelCase   :      str			 =					384
              lowerCamelCase   :      Optional[Any]			 =					1536
              lowerCamelCase   :      Dict			 =					12
              lowerCamelCase   :      Optional[int]			 =					6
       if deit_name[9:].startswith("base"    ):
              pass
       elif deit_name[4:].startswith("large"    ):
              lowerCamelCase   :      str			 =					1024
              lowerCamelCase   :      List[str]			 =					4096
              lowerCamelCase   :      Any			 =					24
              lowerCamelCase   :      Dict			 =					16

       # load original model from timm
       lowerCamelCase   :      List[Any]			 =					timm.create_model(_SCREAMING_SNAKE_CASE ,pretrained=_SCREAMING_SNAKE_CASE    )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowerCamelCase   :      Dict			 =					timm_model.state_dict()
       lowerCamelCase   :      Dict			 =					create_rename_keys(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       for src, dest in rename_keys:
              rename_key(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       read_in_q_k_v(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # load HuggingFace model
       lowerCamelCase   :      Optional[Any]			 =					DeiTForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE    ).eval()
       model.load_state_dict(_SCREAMING_SNAKE_CASE    )

       # Check outputs on an image, prepared by DeiTImageProcessor
       lowerCamelCase   :      Any			 =					int(
           (256 / 224) * config.image_size    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       lowerCamelCase   :      Union[str, Any]			 =					DeiTImageProcessor(size=_SCREAMING_SNAKE_CASE ,crop_size=config.image_size    )
       lowerCamelCase   :      str			 =					image_processor(images=prepare_img() ,return_tensors="pt"    )
       lowerCamelCase   :      int			 =					encoding["pixel_values"]
       lowerCamelCase   :      Optional[Any]			 =					model(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Union[str, Any]			 =					timm_model(_SCREAMING_SNAKE_CASE    )
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(_SCREAMING_SNAKE_CASE ,outputs.logits ,atol=1e-3    )

       Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
       print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''    )
       model.save_pretrained(_SCREAMING_SNAKE_CASE    )
       print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
       image_processor.save_pretrained(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--deit_name',
           default='vit_deit_base_distilled_patch16_224',
           type=str,
           help='Name of the DeiT timm model you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )

       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






