






"""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 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    []
				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 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    []
				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 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    []
				token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')       )
				return token





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    []
				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 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'imagenet-1k-id2label.json'
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    1_000

				_SCREAMING_SNAKE_CASE   :		str       		=    'huggingface/label-files'
				_SCREAMING_SNAKE_CASE   :		Any       		=    num_labels
				_SCREAMING_SNAKE_CASE   :		str       		=    json.load(open(cached_download(hf_hub_url(lowerCamelCase,						lowerCamelCase,						repo_type='dataset'       )       ),						'r'       )       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    {int(lowerCamelCase       ): v for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE   :		Tuple       		=    idalabel
				_SCREAMING_SNAKE_CASE   :		str       		=    {v: k for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE   :		int       		=    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":
								_SCREAMING_SNAKE_CASE   :		str       		=    [1, 2, 10]

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

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    [2, 2, 20]
								_SCREAMING_SNAKE_CASE   :		int       		=    [3, 12, 16]
								_SCREAMING_SNAKE_CASE   :		Dict       		=    [192, 768, 1_024]

				_SCREAMING_SNAKE_CASE   :		int       		=    CvtForImageClassification(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       )
				_SCREAMING_SNAKE_CASE   :		int       		=    image_size
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.load(lowerCamelCase,						map_location=torch.device('cpu'       )       )

				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    OrderedDict()
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    []

				for idx in range(len(config.depth       )       ):
								if config.cls_token[idx]:
												_SCREAMING_SNAKE_CASE   :		str       		=    list_of_state_dict + cls_token(lowerCamelCase       )
								_SCREAMING_SNAKE_CASE   :		Any       		=    list_of_state_dict + embeddings(lowerCamelCase       )
								for cnt in range(config.depth[idx]       ):
												_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    list_of_state_dict + attention(lowerCamelCase,						lowerCamelCase       )

				_SCREAMING_SNAKE_CASE   :		str       		=    list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(lowerCamelCase       )
				for i in range(len(lowerCamelCase       )       ):
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    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"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			argparse.ArgumentParser(
			    description=(
			        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
			        ' Distillation'
			    )
			)
			parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
			parser.add_argument('--model_name', default='roberta-large', type=str)
			parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
			parser.add_argument('--vocab_transform', action='store_true')
			lowerCAmelCase__    						=			parser.parse_args()

			if args.model_type == "roberta":
						lowerCAmelCase__    						=			RobertaForMaskedLM.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'roberta'
			elif args.model_type == "gpt2":
						lowerCAmelCase__    						=			GPTaLMHeadModel.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'transformer'

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

			# Embeddings #
			if args.model_type == "gpt2":
						for param_name in ["wte.weight", "wpe.weight"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.{param_name}']
			else:
						for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.{w}.weight'
									lowerCAmelCase__    						=			state_dict[param_name]
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.LayerNorm.{w}'
									lowerCAmelCase__    						=			state_dict[param_name]

    # Transformer Blocks #
			lowerCAmelCase__    						=			0
			for teacher_idx in [0, 2, 4, 7, 9, 11]:
						if args.model_type == "gpt2":
									for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
															]
									lowerCAmelCase__    						=			state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
						else:
									for layer in [
									    "attention.self.query",
									    "attention.self.key",
									    "attention.self.value",
									    "attention.output.dense",
									    "attention.output.LayerNorm",
									    "intermediate.dense",
									    "output.dense",
									    "output.LayerNorm",
									]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
															]
						std_idx += 1

			# Language Modeling Head ###s
			if args.model_type == "roberta":
						for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
									lowerCAmelCase__    						=			state_dict[F'{layer}']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												lowerCAmelCase__    						=			state_dict[F'lm_head.dense.{w}']
												lowerCAmelCase__    						=			state_dict[F'lm_head.layer_norm.{w}']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.ln_f.{w}']
						lowerCAmelCase__    						=			state_dict['lm_head.weight']

			print(F'N layers selected for distillation: {std_idx}')
			print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

			print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
			torch.save(compressed_sd, args.dump_checkpoint)

