code: 
  # 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_bert/convert_megatron_bert_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 MegatronBertConfig
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Any=0    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if name is None:
            lowercase__					:     Tuple   = None
       else:
            lowercase__					:     Union[str, Any]   = '.' * max(0			,  spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
            lowercase__					:     str   = fmt.format(SCREAMING_SNAKE_CASE_    )
  
       # Print and recurse (if needed).
       if isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
            if msg is not None:
                 print(SCREAMING_SNAKE_CASE_    )
            for k in val.keys():
                 recursive_print(SCREAMING_SNAKE_CASE_			,  val[k]			,  spaces + 2    )
       elif isinstance(SCREAMING_SNAKE_CASE_			,  torch.Tensor    ):
            print(SCREAMING_SNAKE_CASE_			,  ':'			,  val.size()    )
       else:
            print(SCREAMING_SNAKE_CASE_			,  ':'			,  SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Any    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[int]   = param.size()
       if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            lowercase__					:     Tuple   = (num_heads, hidden_size, num_splits) + input_shape[1:]
            lowercase__					:     Any   = param.view(*SCREAMING_SNAKE_CASE_    )
            lowercase__					:     str   = param.transpose(0			,  2    )
            lowercase__					:     str   = param.transpose(1			,  2    ).contiguous()
       elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            lowercase__					:     Union[str, Any]   = (num_heads, num_splits, hidden_size) + input_shape[1:]
            lowercase__					:     List[Any]   = param.view(*SCREAMING_SNAKE_CASE_    )
            lowercase__					:     int   = param.transpose(0			,  1    ).contiguous()
       lowercase__					:     str   = param.view(*SCREAMING_SNAKE_CASE_    )
       return param
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					List[str]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     str   = {}
  
       # old versions did not store training args
       lowercase__					:     Optional[Any]   = input_state_dict.get('args'			,  SCREAMING_SNAKE_CASE_    )
       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))
  
            lowercase__					:     Dict   = ds_args.tokenizer_type
            lowercase__					:     Optional[Any]   = ds_args.padded_vocab_size
            lowercase__					:     Any   = ds_args.max_position_embeddings
            lowercase__					:     Dict   = ds_args.hidden_size
            lowercase__					:     Optional[Any]   = ds_args.num_layers
            lowercase__					:     Union[str, Any]   = ds_args.num_attention_heads
            lowercase__					:     Union[str, Any]   = ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
            # pprint(config)
  
       # The number of heads.
       lowercase__					:     Optional[int]   = config.num_attention_heads
       # The hidden_size per head.
       lowercase__					:     Union[str, Any]   = config.hidden_size // heads
       # Megatron-LM checkpoint version
       if "checkpoint_version" in input_state_dict.keys():
            lowercase__					:     Dict   = input_state_dict['checkpoint_version']
       else:
            lowercase__					:     Tuple   = 0.0
  
       # The model.
       lowercase__					:     Optional[int]   = input_state_dict['model']
       # The language model.
       lowercase__					:     Any   = model['language_model']
       # The embeddings.
       lowercase__					:     Union[str, Any]   = lm['embedding']
  
       # The word embeddings.
       lowercase__					:     List[str]   = embeddings['word_embeddings']['weight']
       # Truncate the embedding table to vocab_size rows.
       lowercase__					:     int   = word_embeddings[: config.vocab_size, :]
       # Store the word embeddings.
       lowercase__					:     List[str]   = word_embeddings
  
       # The position embeddings.
       lowercase__					:     Optional[Any]   = embeddings['position_embeddings']['weight']
       assert pos_embeddings.size(0    ) == config.max_position_embeddings and pos_embeddings.size(1    ) == config.hidden_size
       # Store the position embeddings.
       lowercase__					:     Dict   = pos_embeddings
  
       # The token-type embeddings.
       lowercase__					:     Union[str, Any]   = embeddings['tokentype_embeddings']['weight']
       # Store the position embeddings.
       lowercase__					:     Optional[Any]   = tokentype_embeddings
  
       # The transformer.
       lowercase__					:     Dict   = lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
  
       # The regex to extract layer names.
       lowercase__					:     Optional[int]   = re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'    )
  
       # The simple map of names for "automated" rules.
       lowercase__					:     str   = {
           'attention.dense': '.attention.output.dense.',
           'self_attention.dense': '.attention.output.dense.',
           'mlp.dense_h_to_4h': '.intermediate.dense.',
           'mlp.dense_4h_to_h': '.output.dense.',
       }
  
       # Keep track of the attention/query/value tensor.
       lowercase__					:     int   = None
  
       # Extract the layers.
       for key, val in transformer.items():
            # Match the name.
            lowercase__					:     str   = layer_re.match(SCREAMING_SNAKE_CASE_    )
  
            # Stop if that's not a layer
            if m is None:
                 break
  
            # The index of the layer.
            lowercase__					:     str   = int(m.group(1    )    )
            # The name of the operation.
            lowercase__					:     Any   = m.group(2    )
            # Is it a weight or a bias?
            lowercase__					:     Optional[int]   = m.group(3    )
  
            # The name of the layer.
            lowercase__					:     Dict   = f"""bert.encoder.layer.{layer_idx}"""
  
            # For layernorm(s), simply store the layer norm.
            if op_name.endswith('layernorm'    ):
                 lowercase__					:     List[str]   = 'attention.ln' if op_name.startswith('input'    ) else 'ln'
                 lowercase__					:     Dict   = 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":
                 # Make sure the QKV pointer is nil.
                 assert attention_qkv_weight is None, ""
  
                 lowercase__					:     int   = fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  3			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                 # Store the tensor as we need the bias as well to interleave QKV and biases.
                 lowercase__					:     Optional[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":
                 # Make sure we read the weight tensor.
                 assert attention_qkv_weight is not None, ""
  
                 # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
                 lowercase__					:     Optional[Any]   = attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
                 lowercase__					:     Any   = attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
                 lowercase__					:     Optional[Any]   = attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
  
                 lowercase__					:     Optional[Any]   = fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  3			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                 # Split the bias.
                 lowercase__					:     List[str]   = out_val[0 * config.hidden_size : 1 * config.hidden_size]
                 lowercase__					:     Union[str, Any]   = out_val[1 * config.hidden_size : 2 * config.hidden_size]
                 lowercase__					:     Tuple   = out_val[2 * config.hidden_size : 3 * config.hidden_size]
  
                 # Store.
                 lowercase__					:     List[Any]   = q
                 lowercase__					:     int   = q_bias
                 lowercase__					:     Dict   = k
                 lowercase__					:     Dict   = k_bias
                 lowercase__					:     int   = v
                 lowercase__					:     Tuple   = v_bias
  
                 # Clear the stored tensor.
                 lowercase__					:     Union[str, Any]   = None
  
            # Copy weights and biases as is.
            elif weight_or_bias in ["weight", "bias"]:
                 lowercase__					:     Optional[int]   = megatron_to_transformers[op_name]
                 lowercase__					:     Optional[Any]   = val
  
      # The final layernorm.
       lowercase__					:     Optional[int]   = transformer['final_layernorm.weight']
       lowercase__					:     int   = transformer['final_layernorm.bias']
  
       # The pooler.
       lowercase__					:     str   = lm['pooler']
  
       # Store the matrix and the bias.
       lowercase__					:     Union[str, Any]   = pooler['dense.weight']
       lowercase__					:     Tuple   = pooler['dense.bias']
  
       # The LM head from Megatron (for RACE).
       lowercase__					:     Dict   = model['lm_head']
  
       # The transform matrix.
       lowercase__					:     int   = lm_head['dense.weight']
       lowercase__					:     Dict   = lm_head['dense.bias']
  
       # The transform LN.
       lowercase__					:     List[Any]   = lm_head['layernorm.weight']
       lowercase__					:     Optional[int]   = lm_head['layernorm.bias']
  
       # For the decoder, we replicate the weights.
       lowercase__					:     Union[str, Any]   = word_embeddings
       lowercase__					:     List[str]   = lm_head['bias']
  
       # The classifier from Megatron (for MLNI).
       lowercase__					:     Any   = model['binary_head']
  
       # Store the classifier.
       lowercase__					:     List[Any]   = binary_head['weight']
       lowercase__					:     int   = binary_head['bias']
  
       # It should be done!
       return output_state_dict
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[Any]   = argparse.ArgumentParser()
       parser.add_argument('--print-checkpoint-structure'			,  action='store_true'    )
       parser.add_argument('path_to_checkpoint'			,  type=SCREAMING_SNAKE_CASE_			,  help='Path to the ZIP file containing the checkpoint'    )
       parser.add_argument(
           '--config_file'			,  default=''			,  type=SCREAMING_SNAKE_CASE_			,  help='An optional config json file describing the pre-trained model.'			,  )
       lowercase__					:     Optional[int]   = parser.parse_args()
  
       # Extract the basename.
       lowercase__					:     Dict   = 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:
                      lowercase__					:     List[str]   = torch.load(SCREAMING_SNAKE_CASE_			,  map_location='cpu'    )
       else:
            lowercase__					:     Dict   = torch.load(args.path_to_checkpoint			,  map_location='cpu'    )
  
       if args.config_file == "":
            # Default config of megatron-bert 345m
            lowercase__					:     Tuple   = MegatronBertConfig()
  
            # different megatron-bert-*-345m models have different vocab sizes, so override the default
            # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
            lowercase__					:     Optional[int]   = input_state_dict['model']['lm_head']['bias'].numel()
       else:
            lowercase__					:     int   = MegatronBertConfig.from_json_file(args.config_file    )
  
       # Convert.
       print('Converting'    )
       lowercase__					:     Tuple   = convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       # Print the structure of converted state dict.
       if args.print_checkpoint_structure:
            recursive_print(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       # Store the config to file.
       print('Saving config'    )
       config.save_pretrained(SCREAMING_SNAKE_CASE_    )
  
       # Store the state_dict to file.
       lowercase__					:     Optional[int]   = os.path.join(SCREAMING_SNAKE_CASE_			,  'pytorch_model.bin'    )
       print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
       torch.save(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
         main()
  
  ####################################################################################################
  
  
style_context: 
  
  import argparse
  import json
  
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from timm.models import create_model
  
  from transformers import (
      BeitImageProcessor,
      DataaVecVisionConfig,
      DataaVecVisionForImageClassification,
      DataaVecVisionModel,
  )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					int=False			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]="data2vec."    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     str   = 'backbone.' if is_semantic else ''
  
       lowercase__					:     Dict   = []
       for i in range(config.num_hidden_layers    ):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (f"""{prefix}blocks.{i}.norm1.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")    )
            rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")    )
            rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")    )
            rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")    )
            rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")    )
  
       # projection layer + position embeddings
       rename_keys.extend(
           [
               (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
               (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
               (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
           ]    )
  
       if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ('mask_token', f"""{hf_prefix}embeddings.mask_token"""),
                    (
                        'rel_pos_bias.relative_position_bias_table',
                        f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                    ),
                    (
                        'rel_pos_bias.relative_position_index',
                        f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                    ),
                    ('norm.weight', 'layernorm.weight'),
                    ('norm.bias', 'layernorm.bias'),
                ]    )
       elif is_semantic:
            # semantic segmentation classification heads
            rename_keys.extend(
                [
                    ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                    ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                    ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                    ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
                ]    )
       else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('fc_norm.weight', f"""{hf_prefix}pooler.layernorm.weight"""),
                    ('fc_norm.bias', f"""{hf_prefix}pooler.layernorm.bias"""),
                    ('head.weight', 'classifier.weight'),
                    ('head.bias', 'classifier.bias'),
                ]    )
  
       return rename_keys
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					Dict=False			,  SCREAMING_SNAKE_CASE_	:					List[str]="data2vec_vision."    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       for i in range(config.num_hidden_layers    ):
            lowercase__					:     Optional[int]   = 'backbone.' if is_semantic else ''
            # queries, keys and values
            lowercase__					:     List[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""    )
            lowercase__					:     Optional[int]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""    )
            lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""    )
  
            lowercase__					:     Union[str, Any]   = in_proj_weight[
                : config.hidden_size, :
            ]
            lowercase__					:     List[str]   = q_bias
            lowercase__					:     Tuple   = in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowercase__					:     Union[str, Any]   = in_proj_weight[
                -config.hidden_size :, :
            ]
            lowercase__					:     Union[str, Any]   = v_bias
  
            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""    )
            lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""    )
  
            lowercase__					:     List[str]   = gamma_a
            lowercase__					:     Dict   = gamma_a
  
            # relative_position bias table + index
            if not has_lm_head:
                 # each layer has its own relative position bias
                 lowercase__					:     Tuple   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""    )
                 lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""    )
  
                 lowercase__					:     int   = table
                 lowercase__					:     List[str]   = index
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[str]   = argparse.ArgumentParser(
           'Convert Data2VecVision to HF for image classification and pretraining'			,  add_help=SCREAMING_SNAKE_CASE_    )
       parser.add_argument('--hf_checkpoint_name'			,  type=SCREAMING_SNAKE_CASE_    )
       parser.add_argument('--input_size'			,  default=224			,  type=SCREAMING_SNAKE_CASE_			,  help='images input size'    )
       parser.add_argument('--beit_checkpoint'			,  default=''			,  help='beit checkpoint'    )
  
       return parser.parse_args()
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					str    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       def load_state_dict(SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""			,  SCREAMING_SNAKE_CASE_	:					List[Any]="relative_position_index"    ):
            lowercase__					:     str   = []
            lowercase__					:     str   = []
            lowercase__					:     Dict   = []
            # copy state_dict so _load_from_state_dict can modify it
            lowercase__					:     List[Any]   = getattr(SCREAMING_SNAKE_CASE_			,  '_metadata'			,  SCREAMING_SNAKE_CASE_    )
            lowercase__					:     Optional[int]   = state_dict.copy()
            if metadata is not None:
                 lowercase__					:     Dict   = metadata
  
            def load(SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""    ):
                 lowercase__					:     Optional[Any]   = {} if metadata is None else metadata.get(prefix[:-1]			,  {}    )
                 module._load_from_state_dict(
                     SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                 for name, child in module._modules.items():
                      if child is not None:
                           load(SCREAMING_SNAKE_CASE_			,  prefix + name + '.'    )
  
            load(SCREAMING_SNAKE_CASE_			,  prefix=SCREAMING_SNAKE_CASE_    )
  
            lowercase__					:     List[Any]   = []
            lowercase__					:     Dict   = []
            for key in missing_keys:
                 lowercase__					:     Any   = True
                 for ignore_key in ignore_missing.split('|'    ):
                      if ignore_key in key:
                           lowercase__					:     Union[str, Any]   = False
                           break
                 if keep_flag:
                      warn_missing_keys.append(SCREAMING_SNAKE_CASE_    )
                 else:
                      ignore_missing_keys.append(SCREAMING_SNAKE_CASE_    )
  
            lowercase__					:     Tuple   = warn_missing_keys
  
            if len(SCREAMING_SNAKE_CASE_    ) > 0:
                 print(
                     'Weights of {} not initialized from pretrained model: {}'.format(
                         model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
            if len(SCREAMING_SNAKE_CASE_    ) > 0:
                 print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
            if len(SCREAMING_SNAKE_CASE_    ) > 0:
                 print(
                     'Ignored weights of {} not initialized from pretrained model: {}'.format(
                         model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
            if len(SCREAMING_SNAKE_CASE_    ) > 0:
                 print('\n'.join(SCREAMING_SNAKE_CASE_    )    )
  
       lowercase__					:     int   = {
           'pretrained': False,
           'use_shared_rel_pos_bias': True,
           'use_abs_pos_emb': False,
           'init_values': 0.1,
       }
  
       if is_finetuned:
            model_kwargs.update(
                {
                    'num_classes': 1_000,
                    'use_mean_pooling': True,
                    'init_scale': 0.001,
                    'use_rel_pos_bias': True,
                }    )
  
       lowercase__					:     Dict   = create_model(
           'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'			,  **SCREAMING_SNAKE_CASE_			,  )
       lowercase__					:     int   = model.patch_embed.patch_size
       lowercase__					:     int   = (args.input_size // patch_size[0], args.input_size // patch_size[1])
       lowercase__					:     str   = torch.load(args.beit_checkpoint			,  map_location='cpu'    )
  
       print(f"""Load ckpt from {args.beit_checkpoint}"""    )
       lowercase__					:     int   = None
       for model_key in ("model", "module"):
            if model_key in checkpoint:
                 lowercase__					:     Tuple   = checkpoint[model_key]
                 print(f"""Load state_dict by model_key = {model_key}"""    )
                 break
  
       lowercase__					:     Optional[Any]   = list(checkpoint_model.keys()    )
       for key in all_keys:
            if "relative_position_index" in key:
                 checkpoint_model.pop(SCREAMING_SNAKE_CASE_    )
  
            if "relative_position_bias_table" in key:
                 lowercase__					:     Any   = checkpoint_model[key]
                 lowercase__   ,				lowercase__					:     List[Any]   = rel_pos_bias.size()
                 lowercase__   ,				lowercase__					:     Optional[int]   = model.state_dict()[key].size()
                 lowercase__					:     Dict   = model.patch_embed.patch_shape
                 if dst_patch_shape[0] != dst_patch_shape[1]:
                      raise NotImplementedError()
  
       load_state_dict(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  prefix=''    )
  
       return model
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[str]   = get_args()
  
       lowercase__					:     Union[str, Any]   = 'ft1k' in args.hf_checkpoint_name
       lowercase__					:     Dict   = 'large' in args.hf_checkpoint_name
  
       if is_finetuned:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
            # into this folder.
            import modeling_finetune  # noqa: F401
       else:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
            # into this folder
            # IMPORTANT: Note that for now we've only converted the down-stream
            # model and not the full pretrained model. This means for the integration
            # test you need to add a `return x` after the following line:
            # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
            # to make the integration test pass.
            import modeling_cyclical  # noqa: F401
  
       # 1. Create model config
       lowercase__					:     Optional[Any]   = DataaVecVisionConfig()
       if is_finetuned:
            lowercase__					:     int   = True
            lowercase__					:     Any   = False
            lowercase__					:     Union[str, Any]   = True
            lowercase__					:     Union[str, Any]   = 1_000
  
            lowercase__					:     Any   = 'huggingface/label-files'
            lowercase__					:     Any   = 'imagenet-1k-id2label.json'
            lowercase__					:     Union[str, Any]   = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  repo_type='dataset'    )			,  'r'    )    )
            lowercase__					:     Dict   = {int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}
            lowercase__					:     Optional[int]   = idalabel
            lowercase__					:     Optional[int]   = {v: k for k, v in idalabel.items()}
       else:
            lowercase__					:     Dict   = False
            lowercase__					:     int   = True
            lowercase__					:     List[str]   = False
  
       if is_large:
            lowercase__					:     List[str]   = 1_024
            lowercase__					:     Union[str, Any]   = 4_096
            lowercase__					:     Union[str, Any]   = 24
            lowercase__					:     Optional[int]   = 16
  
       # 2. Load Beit model
       lowercase__					:     List[Any]   = load_beit_model(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
       orig_model.eval()
  
       # 3. Forward Beit model
       lowercase__					:     Optional[Any]   = BeitImageProcessor(size=config.image_size			,  do_center_crop=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Tuple   = Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'    )
       lowercase__					:     Tuple   = image_processor(images=SCREAMING_SNAKE_CASE_			,  return_tensors='pt'    )
       lowercase__					:     List[Any]   = encoding['pixel_values']
  
       lowercase__					:     List[Any]   = (pixel_values,) if is_finetuned else (pixel_values, None)
       with torch.no_grad():
            lowercase__					:     List[str]   = orig_model(*SCREAMING_SNAKE_CASE_    )
  
       # 4. Load HF Data2VecVision model
       if is_finetuned:
            lowercase__					:     Optional[int]   = DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_    )
            hf_model.eval()
            lowercase__					:     Union[str, Any]   = False
            lowercase__					:     Dict   = 'data2vec_vision.'
       else:
            lowercase__					:     Tuple   = DataaVecVisionModel(SCREAMING_SNAKE_CASE_    )
            hf_model.eval()
            lowercase__					:     int   = True
            lowercase__					:     Tuple   = ''
  
       lowercase__					:     Tuple   = create_rename_keys(SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     str   = orig_model.state_dict()
       for src, dest in rename_keys:
            lowercase__					:     int   = state_dict.pop(SCREAMING_SNAKE_CASE_    )
            lowercase__					:     Dict   = val
  
       read_in_q_k_v(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
       lowercase__   ,				lowercase__					:     List[Any]   = hf_model.load_state_dict(SCREAMING_SNAKE_CASE_			,  strict=SCREAMING_SNAKE_CASE_    )
       print('HF missing'			,  SCREAMING_SNAKE_CASE_    )
       print('HF unexpected_keys'			,  SCREAMING_SNAKE_CASE_    )
  
       # 5. Forward HF Data2VecVision model
       with torch.no_grad():
            lowercase__					:     Optional[int]   = hf_model(SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     Optional[int]   = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
  
       # 6. Compare
       lowercase__					:     Dict   = torch.max(torch.abs(hf_output - orig_model_output    )    ).item()
  
       print(f"""max_absolute_diff = {max_absolute_diff}"""    )
       lowercase__					:     Dict   = torch.allclose(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  atol=1E-3    )
       print('Do both models output the same tensors?'			,  '🔥' if success else '💩'    )
       if not success:
            raise Exception('Something went wRoNg'    )
  
       # 7. Save
       print(f"""Saving to {args.hf_checkpoint_name}"""    )
       hf_model.save_pretrained(args.hf_checkpoint_name    )
       image_processor.save_pretrained(args.hf_checkpoint_name    )
  
  
  if __name__ == "__main__":
         main()
         # Run the following to convert checkpoints
         #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
         #          --beit_checkpoint ./pretrained_base.pt \
         #          --hf_checkpoint_name "./data2vec-vision-base"
         #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
         #          --beit_checkpoint ./finetuned_base.pt \
         #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
         #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
         #          --beit_checkpoint ./pretrained_large.pt \
         #          --hf_checkpoint_name "./data2vec-vision-large"
         #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
         #          --beit_checkpoint ./finetuned_large.pt \
         #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
  
  