


'''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 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






						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 				lowerCamelCase__				(	A       :      str				,       A       :      Optional[int]					):




						'''simple docstring'''






						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 				lowerCamelCase__				(	A       :      str					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						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 				lowerCamelCase__				(	A       :      Tuple				,       A       :      Union[str, Any]				,       A       :      Optional[Any]				,       A       :      Optional[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     '''imagenet-1k-id2label.json'''
						UpperCAmelCase					    =     10_00

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

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

						UpperCAmelCase					    =     CvtConfig(num_labels=A				,       idalabel=A				,       labelaid=A					)

						# 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					    =     [1_92, 7_68, 10_24]

						UpperCAmelCase					    =     CvtForImageClassification(A					)
						UpperCAmelCase					    =     AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''					)
						UpperCAmelCase					    =     image_size
						UpperCAmelCase					    =     torch.load(A				,       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(A					)
												UpperCAmelCase					    =     list_of_state_dict + embeddings(A					)
												for cnt in range(config.depth[idx]					):
																		UpperCAmelCase					    =     list_of_state_dict + attention(A				,       A					)

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

						model.load_state_dict(A					)
						model.save_pretrained(A					)
						image_processor.save_pretrained(A					)


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

if __name__ == "__main__":
	_lowercase      :      List[str]            =					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."""
	)

	_lowercase      :      List[str]            =					parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









'''simple docstring'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


_lowercase      :      str            =					logging.get_logger(__name__)

_lowercase      :      Any            =					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase      :      Tuple            =					{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

_lowercase      :      Union[str, Any]            =					{
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Dict  		=	VOCAB_FILES_NAMES
				__magic_name__    :				str  		=	PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Any  		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				List[Any]  		=	["input_ids", "attention_mask"]
				__magic_name__    :				List[Any]  		=	GPTaTokenizer




				def __init__(		self      :			Tuple				,   lowerCAmelCase      :			List[Any]=None				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Tuple="<|endoftext|>"				,   lowerCAmelCase      :			Union[str, Any]="<|endoftext|>"				,   lowerCAmelCase      :			Union[str, Any]="<|endoftext|>"				,   lowerCAmelCase      :			Optional[int]=False				,   **lowerCAmelCase      :			Tuple				,   )->					int:

										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase				,   lowerCAmelCase				,   tokenizer_file=lowerCAmelCase				,   unk_token=lowerCAmelCase				,   bos_token=lowerCAmelCase				,   eos_token=lowerCAmelCase				,   add_prefix_space=lowerCAmelCase				,   **lowerCAmelCase				,   )

										UpperCAmelCase					    =     kwargs.pop('''add_bos_token'''				,   lowerCAmelCase				)

										UpperCAmelCase					    =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
										if pre_tok_state.get('''add_prefix_space'''				,   lowerCAmelCase				) != add_prefix_space:
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   pre_tok_state.pop('''type'''				)				)
																UpperCAmelCase					    =     add_prefix_space
																UpperCAmelCase					    =     pre_tok_class(**lowerCAmelCase				)

										UpperCAmelCase					    =     add_prefix_space




				def 					a__(		self      :			Union[str, Any]				,   *lowerCAmelCase      :			Union[str, Any]				,   **lowerCAmelCase      :			Dict				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			List[str]				,   *lowerCAmelCase      :			Any				,   **lowerCAmelCase      :			Tuple				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			str				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[str] = None				)->					Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._tokenizer.model.save(lowerCAmelCase				,   name=lowerCAmelCase				)
										return tuple(lowerCAmelCase				)





				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			"Conversation"				)->					List[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				) + [self.eos_token_id]				)

										if len(lowerCAmelCase				) > self.model_max_length:
																UpperCAmelCase					    =     input_ids[-self.model_max_length :]
										return input_ids





