'''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      UpperCamelCase_						(					snake_case_       :       Any      )					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						[]
 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      UpperCamelCase_						(					snake_case_       :       Tuple						,		snake_case_       :       Any      )					->     str:






 '''simple docstring'''

 __lowerCAmelCase											=						[]
 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      UpperCamelCase_						(					snake_case_       :       Union[str, Any]      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						[]
 token.append((f"""cvt.encoder.stages.{idx}.cls_token""", """stage2.cls_token""")      )
 return token







def      UpperCamelCase_						(					)					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						[]
 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      UpperCamelCase_						(					snake_case_       :       str						,		snake_case_       :       List[str]						,		snake_case_       :       List[str]						,		snake_case_       :       Optional[int]      )					->     str:






 '''simple docstring'''

 __lowerCAmelCase											=						"""imagenet-1k-id2label.json"""
 __lowerCAmelCase											=						10_00

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

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

 __lowerCAmelCase											=						CvtConfig(num_labels=SCREAMING_SNAKE_CASE__						,		idalabel=SCREAMING_SNAKE_CASE__						,		labelaid=SCREAMING_SNAKE_CASE__      )

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

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

 # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
 else:
  __lowerCAmelCase											=						[2, 2, 20]
  __lowerCAmelCase											=						[3, 12, 16]
  __lowerCAmelCase											=						[1_92, 7_68, 10_24]

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

 __lowerCAmelCase											=						OrderedDict()
 __lowerCAmelCase											=						[]

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

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

 model.load_state_dict(SCREAMING_SNAKE_CASE__      )
 model.save_pretrained(SCREAMING_SNAKE_CASE__      )
 image_processor.save_pretrained(SCREAMING_SNAKE_CASE__      )


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

if __name__ == "__main__":
 _A			:      Tuple					      =				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.'''
 )

 _A			:      Optional[int]					      =				parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase  :					List[str]						    =							logging.get_logger(__name__)

lowercase  :					Optional[Any]						    =							{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"gpt_neo"
    _a		:						Optional[int]=				["past_key_values"]
    _a		:						Dict=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
    def __init__(  self		,snake_case=50257		,snake_case=2048		,snake_case=2048		,snake_case=24		,snake_case=[[["global", "local"], 12]]		,snake_case=16		,snake_case=None		,snake_case=256		,snake_case="gelu_new"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.0		,snake_case=0.1		,snake_case=1e-5		,snake_case=0.02		,snake_case=True		,snake_case=50256		,snake_case=50256		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     int       	=      vocab_size
           lowercase							:     Union[str, Any]       	=      max_position_embeddings
           lowercase							:     Dict       	=      hidden_size
           lowercase							:     Union[str, Any]       	=      num_layers
           lowercase							:     Union[str, Any]       	=      num_heads
           lowercase							:     Optional[int]       	=      intermediate_size
           lowercase							:     List[str]       	=      window_size
           lowercase							:     Optional[int]       	=      activation_function
           lowercase							:     List[str]       	=      resid_dropout
           lowercase							:     int       	=      embed_dropout
           lowercase							:     Optional[int]       	=      attention_dropout
           lowercase							:     Tuple       	=      classifier_dropout
           lowercase							:     Optional[int]       	=      layer_norm_epsilon
           lowercase							:     Dict       	=      initializer_range
           lowercase							:     List[str]       	=      use_cache

           lowercase							:     Optional[int]       	=      bos_token_id
           lowercase							:     int       	=      eos_token_id

           lowercase							:     Union[str, Any]       	=      attention_types
           lowercase							:     Dict       	=      self.expand_attention_types_params(snake_case      )

           if len(self.attention_layers      ) != self.num_layers:
                  raise ValueError(
                      """Configuration for convolutional module is incorrect. """
                      """It is required that `len(config.attention_layers)` == `config.num_layers` """
                      f"but is `len(config.attention_layers) = {len(self.attention_layers      )}`, "
                      f"`config.num_layers = {self.num_layers}`. "
                      """`config.attention_layers` is prepared using `config.attention_types`. """
                      """Please verify the value of `config.attention_types` argument."""      )

           super().__init__(bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      []
           for item in attention_types:
                  for _ in range(item[1]      ):
                         attentions.extend(item[0]      )
           return attentions







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       import torch

       lowercase							:     Tuple       	=      input.size()
       lowercase							:     int       	=      len(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      shape[dimension]

       lowercase							:     int       	=      torch.arange(0					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.div(sizedim - size					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				) + 1
       lowercase							:     Dict       	=      torch.arange(SCREAMING_SNAKE_CASE__				) + low_indices[:min_length][:, None]

       lowercase							:     Union[str, Any]       	=      [slice(SCREAMING_SNAKE_CASE__				)] * rank
       lowercase							:     Optional[Any]       	=      indices
       lowercase							:     List[str]       	=      input[s]

       lowercase							:     Optional[int]       	=      list(range(0					,	rank + 1				)				)
       perm.append(perm.pop(dimension + 1				)				)

       return sliced.permute(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       import torch

       lowercase							:     Union[str, Any]       	=      torch.arange(1					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.remainder(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      remainders == 0
       lowercase							:     Optional[int]       	=      candidates[divisor_indices]
       lowercase							:     List[Any]       	=      torch.max(SCREAMING_SNAKE_CASE__				)
       return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				)




class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
                  lowercase							:     Dict       	=      {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}

           return common_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.num_heads
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      super(snake_case		,self      ).generate_dummy_inputs(
               snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           # We need to order the input in the way they appears in the forward()
           lowercase							:     List[str]       	=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
                  else:
                         import torch

                         lowercase	,			lowercase							:     List[Any]       	=      common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         lowercase							:     Optional[int]       	=      seqlen + 2
                         lowercase							:     int       	=      (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowercase							:     Optional[Any]       	=      [
                             (torch.zeros(snake_case      ), torch.zeros(snake_case      )) for _ in range(self.num_layers      )
                         ]

           lowercase							:     Optional[Any]       	=      common_inputs["""attention_mask"""]
           if self.use_past:
                  lowercase							:     Any       	=      ordered_inputs["""attention_mask"""].dtype
                  lowercase							:     Union[str, Any]       	=      torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )

           return ordered_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 13
