



"""simple docstring"""





import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def      a__			(   lowerCAmelCase__   ):
						UpperCAmelCase_    				=							[]
						embed.append(
						    (
						        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
						        f"""stage{idx}.patch_embed.proj.weight""",
						    )   )
						embed.append(
						    (
						        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
						        f"""stage{idx}.patch_embed.proj.bias""",
						    )   )
						embed.append(
						    (
						        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
						        f"""stage{idx}.patch_embed.norm.weight""",
						    )   )
						embed.append(
						    (
						        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
						        f"""stage{idx}.patch_embed.norm.bias""",
						    )   )
						return embed
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
						UpperCAmelCase_    				=							[]
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
						        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
						    )   )
						attention_weights.append(
						    (
						        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
						        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
						    )   )
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")   )
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")   )
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")   )
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")   )
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")   )
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")   )
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")   )
						attention_weights.append(
						    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")   )
						return attention_weights
def      a__			(   lowerCAmelCase__   ):
						UpperCAmelCase_    				=							[]
						token.append((f"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")   )
						return token
def      a__			(   ):
						UpperCAmelCase_    				=							[]
						head.append(("layernorm.weight", "norm.weight")   )
						head.append(("layernorm.bias", "norm.bias")   )
						head.append(("classifier.weight", "head.weight")   )
						head.append(("classifier.bias", "head.bias")   )
						return head
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
						UpperCAmelCase_    				=							"imagenet-1k-id2label.json"
						UpperCAmelCase_    				=							1000

						UpperCAmelCase_    				=							"huggingface/label-files"
						UpperCAmelCase_    				=							num_labels
						UpperCAmelCase_    				=							json.load(open(cached_download(hf_hub_url(lowerCAmelCase__			,    lowerCAmelCase__			,    repo_type="dataset"   )   )			,    "r"   )   )
						UpperCAmelCase_    				=							{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

						UpperCAmelCase_    				=							idalabel
						UpperCAmelCase_    				=							{v: k for k, v in idalabel.items()}

						UpperCAmelCase_    				=							UpperCAmelCase_    				=							CvtConfig(num_labels=lowerCAmelCase__			,    idalabel=lowerCAmelCase__			,    labelaid=lowerCAmelCase__   )

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit("/"			,    1   )[-1][4:6] == "13":
												UpperCAmelCase_    				=							[1, 2, 10]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit("/"			,    1   )[-1][4:6] == "21":
												UpperCAmelCase_    				=							[1, 4, 16]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												UpperCAmelCase_    				=							[2, 2, 20]
												UpperCAmelCase_    				=							[3, 12, 16]
												UpperCAmelCase_    				=							[192, 768, 1024]

						UpperCAmelCase_    				=							CvtForImageClassification(lowerCAmelCase__   )
						UpperCAmelCase_    				=							AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"   )
						UpperCAmelCase_    				=							image_size
						UpperCAmelCase_    				=							torch.load(lowerCAmelCase__			,    map_location=torch.device("cpu"   )   )

						UpperCAmelCase_    				=							OrderedDict()
						UpperCAmelCase_    				=							[]

						for idx in range(len(config.depth   )   ):
												if config.cls_token[idx]:
																		UpperCAmelCase_    				=							list_of_state_dict + cls_token(lowerCAmelCase__   )
												UpperCAmelCase_    				=							list_of_state_dict + embeddings(lowerCAmelCase__   )
												for cnt in range(config.depth[idx]   ):
																		UpperCAmelCase_    				=							list_of_state_dict + attention(lowerCAmelCase__			,    lowerCAmelCase__   )

						UpperCAmelCase_    				=							list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(lowerCAmelCase__   )
						for i in range(len(lowerCAmelCase__   )   ):
												UpperCAmelCase_    				=							original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(lowerCAmelCase__   )
						model.save_pretrained(lowerCAmelCase__   )
						image_processor.save_pretrained(lowerCAmelCase__   )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
		lowerCamelCase      =		argparse.ArgumentParser()
		parser.add_argument(
		    """--cvt_model""",
		    default="""cvt-w24""",
		    type=str,
		    help="""Name of the cvt model you'd like to convert.""",
		)
		parser.add_argument(
		    """--image_size""",
		    default=384,
		    type=int,
		    help="""Input Image Size""",
		)
		parser.add_argument(
		    """--cvt_file_name""",
		    default=r"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
		    type=str,
		    help="""Input Image Size""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
		)

		lowerCamelCase      =		parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''convbert'''



       def __init__(							self    :						Any	,       _UpperCAmelCase    :						Optional[int]=30522	,       _UpperCAmelCase    :						Tuple=768	,       _UpperCAmelCase    :						Optional[int]=12	,       _UpperCAmelCase    :						Dict=12	,       _UpperCAmelCase    :						Any=3072	,       _UpperCAmelCase    :						Dict="gelu"	,       _UpperCAmelCase    :						Optional[int]=0.1	,       _UpperCAmelCase    :						str=0.1	,       _UpperCAmelCase    :						Tuple=512	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						Union[str, Any]=0.02	,       _UpperCAmelCase    :						Dict=1e-12	,       _UpperCAmelCase    :						Optional[int]=1	,       _UpperCAmelCase    :						int=0	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						List[Any]=768	,       _UpperCAmelCase    :						Optional[int]=2	,       _UpperCAmelCase    :						str=9	,       _UpperCAmelCase    :						List[Any]=1	,       _UpperCAmelCase    :						Optional[Any]=None	,       **_UpperCAmelCase    :						str	,       )				->		List[Any]:


             '''simple docstring'''




             super().__init__(
                 pad_token_id=_UpperCAmelCase	,       bos_token_id=_UpperCAmelCase	,       eos_token_id=_UpperCAmelCase	,       **_UpperCAmelCase	,       )

             UpperCAmelCase_    				=							vocab_size
             UpperCAmelCase_    				=							hidden_size
             UpperCAmelCase_    				=							num_hidden_layers
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							intermediate_size
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							hidden_dropout_prob
             UpperCAmelCase_    				=							attention_probs_dropout_prob
             UpperCAmelCase_    				=							max_position_embeddings
             UpperCAmelCase_    				=							type_vocab_size
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps
             UpperCAmelCase_    				=							embedding_size
             UpperCAmelCase_    				=							head_ratio
             UpperCAmelCase_    				=							conv_kernel_size
             UpperCAmelCase_    				=							num_groups
             UpperCAmelCase_    				=							classifier_dropout



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       @property
       def   lowercase__       (							self    :						Dict					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "multiple-choice":
                   UpperCAmelCase_    				=							{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   UpperCAmelCase_    				=							{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                     ("token_type_ids", dynamic_axis),
                 ]					)




