




import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_						=logging.get_logger(__name__)



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

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

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''layernorm.weight'''),
														        ('''norm.bias''', '''layernorm.bias'''),
														        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
														        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
														    ]							)

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



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		['''head.weight''', '''head.bias''']
							for k in ignore_keys:
														state_dict.pop(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		dct.pop(_snake_case							)
							lowerCAmelCase   =		val



def       UpperCAmelCase       (     ):
							lowerCAmelCase   =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							lowerCAmelCase   =		Image.open(requests.get(_snake_case						, stream=_snake_case							).raw							)
							return im



@torch.no_grad()
def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							lowerCAmelCase   =		ViTConfig()
							lowerCAmelCase   =		False
							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							if vit_name[-5:] == "in21k":
														lowerCAmelCase   =		True
														lowerCAmelCase   =		int(vit_name[-12:-10]							)
														lowerCAmelCase   =		int(vit_name[-9:-6]							)
							else:
														lowerCAmelCase   =		1000
														lowerCAmelCase   =		'''huggingface/label-files'''
														lowerCAmelCase   =		'''imagenet-1k-id2label.json'''
														lowerCAmelCase   =		json.load(open(hf_hub_download(_snake_case						, _snake_case						, repo_type='''dataset'''							)						, '''r'''							)							)
														lowerCAmelCase   =		{int(_snake_case							): v for k, v in idalabel.items()}
														lowerCAmelCase   =		idalabel
														lowerCAmelCase   =		{v: k for k, v in idalabel.items()}
														lowerCAmelCase   =		int(vit_name[-6:-4]							)
														lowerCAmelCase   =		int(vit_name[-3:]							)
							# size of the architecture
							if "deit" in vit_name:
														if vit_name[9:].startswith('''tiny'''							):
																					lowerCAmelCase   =		192
																					lowerCAmelCase   =		768
																					lowerCAmelCase   =		12
																					lowerCAmelCase   =		3
														elif vit_name[9:].startswith('''small'''							):
																					lowerCAmelCase   =		384
																					lowerCAmelCase   =		1536
																					lowerCAmelCase   =		12
																					lowerCAmelCase   =		6
														else:
																					pass
							else:
														if vit_name[4:].startswith('''small'''							):
																					lowerCAmelCase   =		768
																					lowerCAmelCase   =		2304
																					lowerCAmelCase   =		8
																					lowerCAmelCase   =		8
														elif vit_name[4:].startswith('''base'''							):
																					pass
														elif vit_name[4:].startswith('''large'''							):
																					lowerCAmelCase   =		1024
																					lowerCAmelCase   =		4096
																					lowerCAmelCase   =		24
																					lowerCAmelCase   =		16
														elif vit_name[4:].startswith('''huge'''							):
																					lowerCAmelCase   =		1280
																					lowerCAmelCase   =		5120
																					lowerCAmelCase   =		32
																					lowerCAmelCase   =		16

    # load original model from timm
							lowerCAmelCase   =		timm.create_model(_snake_case						, pretrained=_snake_case							)
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							lowerCAmelCase   =		timm_model.state_dict()
							if base_model:
														remove_classification_head_(_snake_case							)
							lowerCAmelCase   =		create_rename_keys(_snake_case						, _snake_case							)
							for src, dest in rename_keys:
														rename_key(_snake_case						, _snake_case						, _snake_case							)
							read_in_q_k_v(_snake_case						, _snake_case						, _snake_case							)

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														lowerCAmelCase   =		ViTModel(_snake_case							).eval()
							else:
														lowerCAmelCase   =		ViTForImageClassification(_snake_case							).eval()
							model.load_state_dict(_snake_case							)

							# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
							if "deit" in vit_name:
														lowerCAmelCase   =		DeiTImageProcessor(size=config.image_size							)
							else:
														lowerCAmelCase   =		ViTImageProcessor(size=config.image_size							)
							lowerCAmelCase   =		image_processor(images=prepare_img()						, return_tensors='''pt'''							)
							lowerCAmelCase   =		encoding['''pixel_values''']
							lowerCAmelCase   =		model(_snake_case							)

							if base_model:
														lowerCAmelCase   =		timm_model.forward_features(_snake_case							)
														assert timm_pooled_output.shape == outputs.pooler_output.shape
														assert torch.allclose(_snake_case						, outputs.pooler_output						, atol=1E-3							)
							else:
														lowerCAmelCase   =		timm_model(_snake_case							)
														assert timm_logits.shape == outputs.logits.shape
														assert torch.allclose(_snake_case						, outputs.logits						, atol=1E-3							)

							Path(_snake_case							).mkdir(exist_ok=_snake_case							)
							print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""							)
							model.save_pretrained(_snake_case							)
							print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
							image_processor.save_pretrained(_snake_case							)


if __name__ == "__main__":
		UpperCAmelCase_						=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--vit_name""",
		    default="""vit_base_patch16_224""",
		    type=str,
		    help="""Name of the ViT timm model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
		)

		UpperCAmelCase_						=parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						={
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										="""switch_transformers"""
				__a							: Union[str, Any]										=["""past_key_values"""]
				__a							: Dict										={"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



				def __init__( self       , UpperCAmelCase_=3_21_28       , UpperCAmelCase_=7_68       , UpperCAmelCase_=64       , UpperCAmelCase_=20_48       , UpperCAmelCase_=64       , UpperCAmelCase_=12       , UpperCAmelCase_=3       , UpperCAmelCase_=12       , UpperCAmelCase_=3       , UpperCAmelCase_=12       , UpperCAmelCase_=8       , UpperCAmelCase_=False       , UpperCAmelCase_=0.01       , UpperCAmelCase_="float32"       , UpperCAmelCase_=False       , UpperCAmelCase_=32       , UpperCAmelCase_=1_28       , UpperCAmelCase_=0.1       , UpperCAmelCase_=1E-6       , UpperCAmelCase_=0.001       , UpperCAmelCase_=0.001       , UpperCAmelCase_=1.0       , UpperCAmelCase_="relu"       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_=True       , UpperCAmelCase_=0       , UpperCAmelCase_=1       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		vocab_size
											lowerCAmelCase   =		d_model
											lowerCAmelCase   =		d_kv
											lowerCAmelCase   =		d_ff

											lowerCAmelCase   =		num_sparse_encoder_layers

											lowerCAmelCase   =		num_layers
											lowerCAmelCase   =		(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											lowerCAmelCase   =		num_sparse_decoder_layers

											# This tells us, each how many encoder layer we'll have to set a sparse layer.
											if self.num_sparse_encoder_layers > 0:
																		lowerCAmelCase   =		self.num_layers // self.num_sparse_encoder_layers
											else:
																		lowerCAmelCase   =		self.num_layers  # HACK: this will create 0 sparse layers

											# This tells us, each how many encoder layer we'll have to set a sparse layer.
											if self.num_sparse_decoder_layers > 0:
																		lowerCAmelCase   =		self.num_decoder_layers // self.num_sparse_decoder_layers
											else:
																		lowerCAmelCase   =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

											lowerCAmelCase   =		num_heads
											lowerCAmelCase   =		num_experts
											lowerCAmelCase   =		expert_capacity
											lowerCAmelCase   =		router_bias
											lowerCAmelCase   =		router_jitter_noise
											if router_dtype not in ["float32", "float16", "bfloat16"]:
																		raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""						)
											lowerCAmelCase   =		router_dtype

											lowerCAmelCase   =		router_ignore_padding_tokens
											lowerCAmelCase   =		relative_attention_num_buckets
											lowerCAmelCase   =		relative_attention_max_distance

											lowerCAmelCase   =		dropout_rate
											lowerCAmelCase   =		layer_norm_epsilon
											lowerCAmelCase   =		initializer_factor
											lowerCAmelCase   =		feed_forward_proj
											lowerCAmelCase   =		use_cache
											lowerCAmelCase   =		add_router_probs

											lowerCAmelCase   =		router_z_loss_coef
											lowerCAmelCase   =		router_aux_loss_coef

											lowerCAmelCase   =		self.feed_forward_proj.split('''-'''						)
											lowerCAmelCase   =		act_info[-1]
											lowerCAmelCase   =		act_info[0] == '''gated'''

											if len(UpperCAmelCase_						) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_						) > 2:
																		raise ValueError(
																		    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																		    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																		    '''\'gated-gelu\' or \'relu\''''						)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																		lowerCAmelCase   =		'''gelu_new'''

											super().__init__(
											    pad_token_id=UpperCAmelCase_       , eos_token_id=UpperCAmelCase_       , is_encoder_decoder=UpperCAmelCase_       , **UpperCAmelCase_       , )



