

"""simple docstring"""




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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def       lowercase__						(       snake_case_   :List[Any]			,		snake_case_   :List[str]=False    ):
	__UpperCAmelCase      		= []

	# fmt: off
	# stem:
	rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')    )
	rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')    )

	rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')    )
	rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')    )

	# backbone
	rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')    )
	rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')    )
	rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')    )

	for stage_idx in range(len(config.backbone_config.depths    )    ):
		for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')    )

		rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')    )
		rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')    )
		rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')    )

	# transformer encoder
	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''')    )

	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"
		__UpperCAmelCase      		= [(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'''),
		    ]    )
	# fmt: on

	return rename_keys





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





def       lowercase__						(       snake_case_   :str    ):
	__UpperCAmelCase      		= ['''head.weight''', '''head.bias''']
	for k in ignore_keys:
		state_dict.pop(snake_case_			,		snake_case_    )





def       lowercase__						(       snake_case_   :Optional[int]			,		snake_case_   :int			,		snake_case_   :str    ):
	__UpperCAmelCase      		= dct.pop(snake_case_    )
	__UpperCAmelCase      		= val





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





@torch.no_grad()
def       lowercase__						(       snake_case_   :Union[str, Any]			,		snake_case_   :Any			,		snake_case_   :List[str]=False    ):
	__UpperCAmelCase      		= BitConfig(
	    global_padding='''same'''			,		layer_type='''bottleneck'''			,		depths=(3, 4, 9)			,		out_features=['''stage3''']			,		embedding_dynamic_padding=snake_case_			,		)
	__UpperCAmelCase      		= ViTHybridConfig(backbone_config=snake_case_			,		image_size=384			,		num_labels=1_000    )
	__UpperCAmelCase      		= False

	# load original model from timm
	__UpperCAmelCase      		= timm.create_model(snake_case_			,		pretrained=snake_case_    )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__UpperCAmelCase      		= timm_model.state_dict()
	if base_model:
		remove_classification_head_(snake_case_    )
	__UpperCAmelCase      		= 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_    )

	__UpperCAmelCase      		= '''huggingface/label-files'''
	__UpperCAmelCase      		= '''imagenet-1k-id2label.json'''
	__UpperCAmelCase      		= json.load(open(hf_hub_download(snake_case_			,		snake_case_			,		repo_type='''dataset'''    )			,		'''r'''    )    )
	__UpperCAmelCase      		= {int(snake_case_    ): v for k, v in idalabel.items()}
	__UpperCAmelCase      		= idalabel
	__UpperCAmelCase      		= {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__UpperCAmelCase      		= ViTHybridModel(snake_case_    ).eval()
	else:
		__UpperCAmelCase      		= ViTHybridForImageClassification(snake_case_    ).eval()
	model.load_state_dict(snake_case_    )

	# create image processor
	__UpperCAmelCase      		= create_transform(**resolve_data_config({}			,		model=snake_case_    )    )
	__UpperCAmelCase      		= transform.transforms

	__UpperCAmelCase      		= {
	    '''bilinear''': PILImageResampling.BILINEAR,
	    '''bicubic''': PILImageResampling.BICUBIC,
	    '''nearest''': PILImageResampling.NEAREST,
	}

	__UpperCAmelCase      		= ViTHybridImageProcessor(
	    do_resize=snake_case_			,		size={'''shortest_edge''': timm_transforms[0].size}			,		resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,		do_center_crop=snake_case_			,		crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}			,		do_normalize=snake_case_			,		image_mean=timm_transforms[-1].mean.tolist()			,		image_std=timm_transforms[-1].std.tolist()			,		)

	__UpperCAmelCase      		= prepare_img()
	__UpperCAmelCase      		= transform(snake_case_    ).unsqueeze(0    )
	__UpperCAmelCase      		= processor(snake_case_			,		return_tensors='''pt'''    ).pixel_values

	# verify pixel values
	assert torch.allclose(snake_case_			,		snake_case_    )

	# verify logits
	with torch.no_grad():
		__UpperCAmelCase      		= model(snake_case_    )
		__UpperCAmelCase      		= outputs.logits

	print('''Predicted class:'''			,		logits.argmax(-1    ).item()    )
	if base_model:
		__UpperCAmelCase      		= 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:
		__UpperCAmelCase      		= timm_model(snake_case_    )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(snake_case_			,		outputs.logits			,		atol=1E-3    )
	print('''Looks ok!'''    )

	if pytorch_dump_folder_path is not None:
		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 processor to {pytorch_dump_folder_path}'''    )
		processor.save_pretrained(snake_case_    )

	if push_to_hub:
		print(F'''Pushing model and processor to the hub {vit_name}'''    )
		model.push_to_hub(F'''ybelkada/{vit_name}'''    )
		processor.push_to_hub(F'''ybelkada/{vit_name}'''    )


if __name__ == "__main__":
				_lowercase   :	Dict												=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--vit_name',
				    default='vit_base_r50_s16_384',
				    type=str,
				    help='Name of the hybrid 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.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
				)

				_lowercase   :	List[str]												=						parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""

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

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


UpperCAmelCase__					=logging.get_logger(__name__)

UpperCAmelCase__					={
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class  lowerCamelCase__    (		_a     ):
      a			:  Tuple           =		"""gptj"""
      a			:  Union[str, Any]           =		{
          """max_position_embeddings""": """n_positions""",
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(		self						:						int   ,							A_						:						Optional[int]=5_0_4_0_0   ,							A_						:						Optional[Any]=2_0_4_8   ,							A_						:						Optional[Any]=4_0_9_6   ,							A_						:						Any=2_8   ,							A_						:						Union[str, Any]=1_6   ,							A_						:						int=6_4   ,							A_						:						int=None   ,							A_						:						str="gelu_new"   ,							A_						:						str=0.0   ,							A_						:						Optional[Any]=0.0   ,							A_						:						Dict=0.0   ,							A_						:						Dict=1e-5   ,							A_						:						Optional[int]=0.02   ,							A_						:						List[str]=True   ,							A_						:						List[Any]=5_0_2_5_6   ,							A_						:						Optional[int]=5_0_2_5_6   ,							A_						:						List[Any]=False   ,							**A_						:						int   ,							):






       '''simple docstring'''


       __lowercase						=						vocab_size
       __lowercase						=						n_positions
       __lowercase						=						n_embd
       __lowercase						=						n_layer
       __lowercase						=						n_head
       __lowercase						=						n_inner
       __lowercase						=						rotary_dim
       __lowercase						=						activation_function
       __lowercase						=						resid_pdrop
       __lowercase						=						embd_pdrop
       __lowercase						=						attn_pdrop
       __lowercase						=						layer_norm_epsilon
       __lowercase						=						initializer_range
       __lowercase						=						use_cache

       __lowercase						=						bos_token_id
       __lowercase						=						eos_token_id

       super().__init__(
           bos_token_id=A_   ,							eos_token_id=A_   ,							tie_word_embeddings=A_   ,							**A_				)






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						int   ,							A_						:						PretrainedConfig   ,							A_						:						str = "default"   ,							A_						:						List[PatchingSpec] = None   ,							A_						:						bool = False   ,							):






       '''simple docstring'''


       super().__init__(A_   ,							task=A_   ,							patching_specs=A_   ,							use_past=A_				)
       if not getattr(self._config   ,							"""pad_token_id"""   ,							A_				):
        # TODO: how to do that better?
        __lowercase						=						0
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
       if self.use_past:
        self.fill_with_past_key_values_(A_   ,							direction="""inputs"""				)
        __lowercase						=						{0: """batch""", 1: """past_sequence + sequence"""}
       else:
        __lowercase						=						{0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       return self._config.n_layer
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       return self._config.n_head
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


       __lowercase						=						super(A_   ,							self				).generate_dummy_inputs(
           A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)

       # We need to order the input in the way they appears in the forward()
       __lowercase						=						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						=						common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         __lowercase						=						seqlen + 2
         __lowercase						=						(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         __lowercase						=						[
             (torch.zeros(A_				), torch.zeros(A_				)) for _ in range(self.num_layers				)
         ]

       __lowercase						=						common_inputs["""attention_mask"""]
       if self.use_past:
        __lowercase						=						ordered_inputs["""attention_mask"""].dtype
        __lowercase						=						torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(A_   ,							A_   ,							dtype=A_				)]   ,							dim=1				)

       return ordered_inputs






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return 1_3
