
"""simple docstring"""




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)


def 						UpperCAmelCase      (     a_		,       a_=False						)		->     Dict:







				"""simple docstring"""



				__A             =			[]
				for i in range(config.num_hidden_layers						):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')						)
								rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')						)
								rename_keys.append(
								    (F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')						)
								rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')						)
								rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')						)
								rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')						)
								rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')						)
								rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')						)
								rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')						)
								rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')						)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("module.cls_token", "vit.embeddings.cls_token"),
				        ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
				        ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
				        ("module.pos_embed", "vit.embeddings.position_embeddings"),
				    ]						)

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ("module.norm.weight", "layernorm.weight"),
								        ("module.norm.bias", "layernorm.bias"),
								    ]						)

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								__A             =			[(pair[0], pair[1][4:]) if pair[1].startswith("vit"						) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("norm.weight", "vit.layernorm.weight"),
								        ("norm.bias", "vit.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]						)

				return rename_keys


def 						UpperCAmelCase      (     a_		,       a_		,       a_=False						)		->     List[Any]:







				"""simple docstring"""



				for i in range(config.num_hidden_layers						):
								if base_model:
												__A             =			""
								else:
												__A             =			"vit."
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								__A             =			state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight'''						)
								__A             =			state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias'''						)
								# next, add query, keys and values (in that order) to the state dict
								__A             =			in_proj_weight[
								    : config.hidden_size, :
								]
								__A             =			in_proj_bias[: config.hidden_size]
								__A             =			in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__A             =			in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__A             =			in_proj_weight[
								    -config.hidden_size :, :
								]
								__A             =			in_proj_bias[-config.hidden_size :]


def 						UpperCAmelCase      (     a_						)		->     List[Any]:







				"""simple docstring"""



				__A             =			["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(a_		,       a_						)


def 						UpperCAmelCase      (     a_						)		->     Any:







				"""simple docstring"""



				__A             =			[
				    "module.fc.fc1.weight",
				    "module.fc.fc1.bias",
				    "module.fc.bn1.weight",
				    "module.fc.bn1.bias",
				    "module.fc.bn1.running_mean",
				    "module.fc.bn1.running_var",
				    "module.fc.bn1.num_batches_tracked",
				    "module.fc.fc2.weight",
				    "module.fc.fc2.bias",
				    "module.fc.bn2.weight",
				    "module.fc.bn2.bias",
				    "module.fc.bn2.running_mean",
				    "module.fc.bn2.running_var",
				    "module.fc.bn2.num_batches_tracked",
				    "module.fc.fc3.weight",
				    "module.fc.fc3.bias",
				]
				for k in ignore_keys:
								state_dict.pop(a_		,       a_						)


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     List[Any]:







				"""simple docstring"""



				__A             =			dct.pop(a_						)
				__A             =			val


def 						UpperCAmelCase      (     a_		,       a_						)		->     str:







				"""simple docstring"""



				__A             =			ViTMSNConfig()
				__A             =			1_0_0_0

				__A             =			"datasets/huggingface/label-files"
				__A             =			"imagenet-1k-id2label.json"
				__A             =			json.load(open(hf_hub_download(a_		,       a_						)		,       "r"						)						)
				__A             =			{int(a_						): v for k, v in idalabel.items()}
				__A             =			idalabel
				__A             =			{v: k for k, v in idalabel.items()}

				if "s16" in checkpoint_url:
								__A             =			3_8_4
								__A             =			1_5_3_6
								__A             =			6
				elif "l16" in checkpoint_url:
								__A             =			1_0_2_4
								__A             =			4_0_9_6
								__A             =			2_4
								__A             =			1_6
								__A             =			0.1
				elif "b4" in checkpoint_url:
								__A             =			4
				elif "l7" in checkpoint_url:
								__A             =			7
								__A             =			1_0_2_4
								__A             =			4_0_9_6
								__A             =			2_4
								__A             =			1_6
								__A             =			0.1

				__A             =			ViTMSNModel(a_						)

				__A             =			torch.hub.load_state_dict_from_url(a_		,       map_location="cpu"						)["target_encoder"]

				__A             =			ViTImageProcessor(size=config.image_size						)

				remove_projection_head(a_						)
				__A             =			create_rename_keys(a_		,       base_model=a_						)

				for src, dest in rename_keys:
								rename_key(a_		,       a_		,       a_						)
				read_in_q_k_v(a_		,       a_		,       base_model=a_						)

				model.load_state_dict(a_						)
				model.eval()

				__A             =			"http://images.cocodataset.org/val2017/000000039769.jpg"

				__A             =			Image.open(requests.get(a_		,       stream=a_						).raw						)
				__A             =			ViTImageProcessor(
				    size=config.image_size		,       image_mean=a_		,       image_std=a_						)
				__A             =			image_processor(images=a_		,       return_tensors="pt"						)

				# forward pass
				torch.manual_seed(2						)
				__A             =			model(**a_						)
				__A             =			outputs.last_hidden_state

				# The following Colab Notebook was used to generate these outputs:
				# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
				if "s16" in checkpoint_url:
								__A             =			torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]						)
				elif "b16" in checkpoint_url:
								__A             =			torch.tensor([[14.2_889, -18.9_045, 11.7_281]]						)
				elif "l16" in checkpoint_url:
								__A             =			torch.tensor([[41.5_028, -22.8_681, 45.6_475]]						)
				elif "b4" in checkpoint_url:
								__A             =			torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]						)
				else:
								__A             =			torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]						)

				# verify logits
				assert torch.allclose(last_hidden_state[:, 0, :3]		,       a_		,       atol=1E-4						)

				print(F'''Saving model to {pytorch_dump_folder_path}'''						)
				model.save_pretrained(a_						)

				print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
				image_processor.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Dict										=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--checkpoint_url',
					    default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
					    type=str,
					    help='URL of the checkpoint you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)

					SCREAMING_SNAKE_CASE							:Dict										=    parser.parse_args()
					convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]






	@register_to_config
	def __init__(	self :			Tuple							,A :			int							,A :			int							,A :			Optional[int] = None							,A :			int = 5_02_57							,A :			int = 10_24							,A :			int = 7_68							,A :			int = 12							,A :			int = 12							,A :			Optional[int] = None							,A :			str = "gelu_new"							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 1E-5							,A :			float = 0.02							,A :			bool = True							,A :			bool = True							,A :			bool = False							,A :			bool = False							,):
					super().__init__()

					__A             =			prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
									raise ValueError(
									    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
									    f''' `n_embd`: {n_embd} are not equal.'''			)

					__A             =			prefix_inner_dim
					__A             =			prefix_hidden_dim

					__A             =			(
					    nn.Linear(self.prefix_inner_dim							,self.prefix_hidden_dim			)
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					__A             =			(
					    nn.Linear(self.prefix_hidden_dim							,A			) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					__A             =			GPTaConfig(
					    vocab_size=A							,n_positions=A							,n_embd=A							,n_layer=A							,n_head=A							,n_inner=A							,activation_function=A							,resid_pdrop=A							,embd_pdrop=A							,attn_pdrop=A							,layer_norm_epsilon=A							,initializer_range=A							,scale_attn_weights=A							,use_cache=A							,scale_attn_by_inverse_layer_idx=A							,reorder_and_upcast_attn=A							,)
					__A             =			GPTaLMHeadModel(A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			torch.Tensor							,A :			torch.Tensor							,A :			Optional[torch.Tensor] = None							,A :			Optional[torch.Tensor] = None							,):
					__A             =			self.transformer.transformer.wte(A			)
					__A             =			self.encode_prefix(A			)
					__A             =			self.decode_prefix(A			)
					__A             =			torch.cat((prefix_embeds, embedding_text)							,dim=1			)

					if labels is not None:
									__A             =			self.get_dummy_token(input_ids.shape[0]							,input_ids.device			)
									__A             =			torch.cat((dummy_token, input_ids)							,dim=1			)
					__A             =			self.transformer(inputs_embeds=A							,labels=A							,attention_mask=A			)
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out






	def     UpperCamelCase_  (	self :			int							,A :			int							,A :			torch.device			):
					return torch.zeros(A							,self.prefix_length							,dtype=torch.intaa							,device=A			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[Any]			):
					return self.encode_prefix(A			)






	@torch.no_grad()
	def     UpperCamelCase_  (	self :			str							,A :			List[str]							,A :			List[Any]							,A :			List[Any]			):
					__A             =			torch.split(A							,1							,dim=0			)
					__A             =			[]
					__A             =			[]
					for feature in features:
									__A             =			self.decode_prefix(feature.to(A			)			)  # back to the clip feature
									# Only support beam search for now
									__A      ,							__A             =			self.generate_beam(
									    input_embeds=A							,device=A							,eos_token_id=A			)
									generated_tokens.append(output_tokens[0]			)
									generated_seq_lengths.append(seq_lengths[0]			)
					__A             =			torch.stack(A			)
					__A             =			torch.stack(A			)
					return generated_tokens, generated_seq_lengths







	@torch.no_grad()
	def     UpperCamelCase_  (	self :			Tuple							,A :			List[str]=None							,A :			Dict=None							,A :			Optional[int]=None							,A :			int = 5							,A :			int = 67							,A :			float = 1.0							,A :			Optional[int] = None							,):
					__A             =			eos_token_id
					__A             =			None
					__A             =			None
					__A             =			torch.ones(A							,device=A							,dtype=torch.int			)
					__A             =			torch.zeros(A							,device=A							,dtype=torch.bool			)

					if input_embeds is not None:
									__A             =			input_embeds
					else:
									__A             =			self.transformer.transformer.wte(A			)

					for i in range(A			):
									__A             =			self.transformer(inputs_embeds=A			)
									__A             =			outputs.logits
									__A             =			logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									__A             =			logits.softmax(-1			).log()

									if scores is None:
													__A      ,							__A             =			logits.topk(A							,-1			)
													__A             =			generated.expand(A							,*generated.shape[1:]			)
													__A      ,							__A             =			next_tokens.permute(1							,0			), scores.squeeze(0			)
													if tokens is None:
																	__A             =			next_tokens
													else:
																	__A             =			tokens.expand(A							,*tokens.shape[1:]			)
																	__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
									else:
													__A             =			-float(np.inf			)
													__A             =			0
													__A             =			scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													__A             =			scores_sum / seq_lengths[:, None]
													__A      ,							__A             =			scores_sum_average.view(-1			).topk(A							,-1			)
													__A             =			next_tokens // scores_sum.shape[1]
													__A             =			seq_lengths[next_tokens_source]
													__A             =			next_tokens % scores_sum.shape[1]
													__A             =			next_tokens.unsqueeze(1			)
													__A             =			tokens[next_tokens_source]
													__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
													__A             =			generated[next_tokens_source]
													__A             =			scores_sum_average * seq_lengths
													__A             =			is_stopped[next_tokens_source]

									__A             =			self.transformer.transformer.wte(next_tokens.squeeze()			).view(generated.shape[0]							,1							,-1			)
									__A             =			torch.cat((generated, next_token_embed)							,dim=1			)
									__A             =			is_stopped + next_tokens.eq(A			).squeeze()
									if is_stopped.all():
													break

					__A             =			scores / seq_lengths
					__A             =			scores.argsort(descending=A			)
					# tokens tensors are already padded to max_seq_length
					__A             =			[tokens[i] for i in order]
					__A             =			torch.stack(A							,dim=0			)
					__A             =			torch.tensor([seq_lengths[i] for i in order]							,dtype=seq_lengths.dtype			)
					return output_texts, seq_lengths
