
'''simple docstring'''



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 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_="data2vec."  )       ->       List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			"""backbone.""" if is_semantic else """"""

   _SCREAMING_SNAKE_CASE       	=			[]
   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 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_="data2vec_vision."  )       ->       str:




   """simple docstring"""
   for i in range(config.num_hidden_layers  ):
      _SCREAMING_SNAKE_CASE       	=			"""backbone.""" if is_semantic else """"""
      # queries, keys and values
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"  )
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"  )
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"  )

      _SCREAMING_SNAKE_CASE       	=			in_proj_weight[
          : config.hidden_size, :
      ]
      _SCREAMING_SNAKE_CASE       	=			q_bias
      _SCREAMING_SNAKE_CASE       	=			in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _SCREAMING_SNAKE_CASE       	=			in_proj_weight[
          -config.hidden_size :, :
      ]
      _SCREAMING_SNAKE_CASE       	=			v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.gamma_1"  )
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.gamma_2"  )

      _SCREAMING_SNAKE_CASE       	=			gamma_a
      _SCREAMING_SNAKE_CASE       	=			gamma_a

      # relative_position bias table + index
      if not has_lm_head:
         # each layer has its own relative position bias
         _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"  )
         _SCREAMING_SNAKE_CASE       	=			state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"  )

         _SCREAMING_SNAKE_CASE       	=			table
         _SCREAMING_SNAKE_CASE       	=			index
def 				lowerCAmelCase_		(			)       ->       List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			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=2_24      , type=SCREAMING_SNAKE_CASE_      , help="""images input size"""  )
   parser.add_argument("""--beit_checkpoint"""      , default=""""""      , help="""beit checkpoint"""  )

   return parser.parse_args()
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       str:




   """simple docstring"""
   def load_state_dict(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=""      , SCREAMING_SNAKE_CASE_="relative_position_index"  ):
      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      # copy state_dict so _load_from_state_dict can modify it
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , """_metadata"""      , SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			state_dict.copy()
      if metadata is not None:
         _SCREAMING_SNAKE_CASE       	=			metadata

      def load(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=""  ):
         _SCREAMING_SNAKE_CASE       	=			{} 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_  )

      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      for key in missing_keys:
         _SCREAMING_SNAKE_CASE       	=			True
         for ignore_key in ignore_missing.split("""|"""  ):
            if ignore_key in key:
               _SCREAMING_SNAKE_CASE       	=			False
               break
         if keep_flag:
            warn_missing_keys.append(SCREAMING_SNAKE_CASE_  )
         else:
            ignore_missing_keys.append(SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			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_  )  )

   _SCREAMING_SNAKE_CASE       	=			{
       """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""": 10_00,
              """use_mean_pooling""": True,
              """init_scale""": 0.001,
              """use_rel_pos_bias""": True,
          }  )

   _SCREAMING_SNAKE_CASE       	=			create_model(
       """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""      , **SCREAMING_SNAKE_CASE_      , )
   _SCREAMING_SNAKE_CASE       	=			model.patch_embed.patch_size
   _SCREAMING_SNAKE_CASE       	=			(args.input_size // patch_size[0], args.input_size // patch_size[1])
   _SCREAMING_SNAKE_CASE       	=			torch.load(args.beit_checkpoint      , map_location="""cpu"""  )

   print(F"Load ckpt from {args.beit_checkpoint}"  )
   _SCREAMING_SNAKE_CASE       	=			None
   for model_key in ("model", "module"):
      if model_key in checkpoint:
         _SCREAMING_SNAKE_CASE       	=			checkpoint[model_key]
         print(F"Load state_dict by model_key = {model_key}"  )
         break

   _SCREAMING_SNAKE_CASE       	=			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:
         _SCREAMING_SNAKE_CASE       	=			checkpoint_model[key]
         _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			rel_pos_bias.size()
         _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			model.state_dict()[key].size()
         _SCREAMING_SNAKE_CASE       	=			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 				lowerCAmelCase_		(			)       ->       Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			get_args()

   _SCREAMING_SNAKE_CASE       	=			"""ft1k""" in args.hf_checkpoint_name
   _SCREAMING_SNAKE_CASE       	=			"""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
   _SCREAMING_SNAKE_CASE       	=			DataaVecVisionConfig()
   if is_finetuned:
      _SCREAMING_SNAKE_CASE       	=			True
      _SCREAMING_SNAKE_CASE       	=			False
      _SCREAMING_SNAKE_CASE       	=			True
      _SCREAMING_SNAKE_CASE       	=			10_00

      _SCREAMING_SNAKE_CASE       	=			"""huggingface/label-files"""
      _SCREAMING_SNAKE_CASE       	=			"""imagenet-1k-id2label.json"""
      _SCREAMING_SNAKE_CASE       	=			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , repo_type="""dataset"""  )      , """r"""  )  )
      _SCREAMING_SNAKE_CASE       	=			{int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
      _SCREAMING_SNAKE_CASE       	=			idalabel
      _SCREAMING_SNAKE_CASE       	=			{v: k for k, v in idalabel.items()}
   else:
      _SCREAMING_SNAKE_CASE       	=			False
      _SCREAMING_SNAKE_CASE       	=			True
      _SCREAMING_SNAKE_CASE       	=			False

   if is_large:
      _SCREAMING_SNAKE_CASE       	=			10_24
      _SCREAMING_SNAKE_CASE       	=			40_96
      _SCREAMING_SNAKE_CASE       	=			24
      _SCREAMING_SNAKE_CASE       	=			16

   # 2. Load Beit model
   _SCREAMING_SNAKE_CASE       	=			load_beit_model(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
   orig_model.eval()

   # 3. Forward Beit model
   _SCREAMING_SNAKE_CASE       	=			BeitImageProcessor(size=config.image_size      , do_center_crop=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""  )
   _SCREAMING_SNAKE_CASE       	=			image_processor(images=SCREAMING_SNAKE_CASE_      , return_tensors="""pt"""  )
   _SCREAMING_SNAKE_CASE       	=			encoding["""pixel_values"""]

   _SCREAMING_SNAKE_CASE       	=			(pixel_values,) if is_finetuned else (pixel_values, None)
   with torch.no_grad():
      _SCREAMING_SNAKE_CASE       	=			orig_model(*SCREAMING_SNAKE_CASE_  )

   # 4. Load HF Data2VecVision model
   if is_finetuned:
      _SCREAMING_SNAKE_CASE       	=			DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_  )
      hf_model.eval()
      _SCREAMING_SNAKE_CASE       	=			False
      _SCREAMING_SNAKE_CASE       	=			"""data2vec_vision."""
   else:
      _SCREAMING_SNAKE_CASE       	=			DataaVecVisionModel(SCREAMING_SNAKE_CASE_  )
      hf_model.eval()
      _SCREAMING_SNAKE_CASE       	=			True
      _SCREAMING_SNAKE_CASE       	=			""""""

   _SCREAMING_SNAKE_CASE       	=			create_rename_keys(SCREAMING_SNAKE_CASE_      , hf_prefix=SCREAMING_SNAKE_CASE_      , has_lm_head=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			orig_model.state_dict()
   for src, dest in rename_keys:
      _SCREAMING_SNAKE_CASE       	=			state_dict.pop(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			val

   read_in_q_k_v(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , hf_prefix=SCREAMING_SNAKE_CASE_      , has_lm_head=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			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():
      _SCREAMING_SNAKE_CASE       	=			hf_model(SCREAMING_SNAKE_CASE_  )

   _SCREAMING_SNAKE_CASE       	=			hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

   # 6. Compare
   _SCREAMING_SNAKE_CASE       	=			torch.max(torch.abs(hf_output - orig_model_output  )  ).item()

   print(F"max_absolute_diff = {max_absolute_diff}"  )
   _SCREAMING_SNAKE_CASE       	=			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"








'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__ :						int    							=					logging.get_logger(__name__)

UpperCamelCase__ :						Any    							=					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'cpmant'






       def __init__(							self    ,							A__ = 3_07_20    ,							A__ = 40_96    ,							A__ = 32    ,							A__ = 1_28    ,							A__ = 1_02_40    ,							A__ = 48    ,							A__ = 0.0    ,							A__ = 5_12    ,							A__ = 20_48    ,							A__ = 1E-6    ,							A__ = 1.0    ,							A__ = 32    ,							A__ = 32    ,							A__ = 32    ,							A__ = True    ,							**A__    ,							)      ->     List[Any]:
          super().__init__(**A__ )
          _SCREAMING_SNAKE_CASE       	=			prompt_types
          _SCREAMING_SNAKE_CASE       	=			prompt_length
          _SCREAMING_SNAKE_CASE       	=			segment_types
          _SCREAMING_SNAKE_CASE       	=			hidden_size
          _SCREAMING_SNAKE_CASE       	=			num_attention_heads
          _SCREAMING_SNAKE_CASE       	=			dim_head
          _SCREAMING_SNAKE_CASE       	=			dim_ff
          _SCREAMING_SNAKE_CASE       	=			num_hidden_layers
          _SCREAMING_SNAKE_CASE       	=			position_bias_num_buckets
          _SCREAMING_SNAKE_CASE       	=			position_bias_max_distance
          _SCREAMING_SNAKE_CASE       	=			dropout_p
          _SCREAMING_SNAKE_CASE       	=			eps
          _SCREAMING_SNAKE_CASE       	=			use_cache
          _SCREAMING_SNAKE_CASE       	=			vocab_size
          _SCREAMING_SNAKE_CASE       	=			init_std






