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


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



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



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = ["""head.weight""", """head.bias"""]
		for k in ignore_keys:
				state_dict.pop(_lowerCamelCase  ,				_lowerCamelCase				)



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = dct.pop(_lowerCamelCase				)
		__SCREAMING_SNAKE_CASE            = val



def 						_lowerCAmelCase				(				):
		__SCREAMING_SNAKE_CASE            = """http://images.cocodataset.org/val2017/000000039769.jpg"""
		__SCREAMING_SNAKE_CASE            = Image.open(requests.get(_lowerCamelCase  ,				stream=_lowerCamelCase				).raw				)
		return im



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

    # load original model from timm
		__SCREAMING_SNAKE_CASE            = timm.create_model(_lowerCamelCase  ,				pretrained=_lowerCamelCase				)
		timm_model.eval()

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

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

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

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

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


if __name__ == "__main__":
							__magic_name__       =			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."
							)

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











'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor, CLIPProcessor






@require_vision
class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     A__				=			tempfile.mkdtemp()

     # fmt: off
     A__				=			["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
     # fmt: on
     A__				=			dict(zip(lowercase_						,       range(len(lowercase_							)							)							)							)
     A__				=			["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
     A__				=			{"unk_token": "<unk>"}

     A__				=			os.path.join(self.tmpdirname						,       VOCAB_FILES_NAMES["vocab_file"]							)
     A__				=			os.path.join(self.tmpdirname						,       VOCAB_FILES_NAMES["merges_file"]							)
     with open(self.vocab_file						,       "w"						,       encoding="utf-8"							) as fp:
         fp.write(json.dumps(lowercase_							) + "\n"							)
     with open(self.merges_file						,       "w"						,       encoding="utf-8"							) as fp:
         fp.write("\n".join(lowercase_							)							)

     A__				=			{
         "do_resize": True,
         "size": 20,
         "do_center_crop": True,
         "crop_size": 18,
         "do_normalize": True,
         "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
         "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
     }
     A__				=			os.path.join(self.tmpdirname						,       lowercase_							)
     with open(self.image_processor_file						,       "w"						,       encoding="utf-8"							) as fp:
         json.dump(lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       **lowercase_      :Union[str, Any]							)->							Tuple:
     return CLIPTokenizer.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       **lowercase_      :Tuple							)->							Dict:
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Dict						,       **lowercase_      :Union[str, Any]							)->							Any:
     return CLIPImageProcessor.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							int:
     shutil.rmtree(self.tmpdirname							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Optional[int]:
     A__				=			[np.random.randint(2_55						,       size=(3, 30, 4_00)						,       dtype=np.uinta							)]

     A__				=			[Image.fromarray(np.moveaxis(lowercase_						,       0						,       -1							)							) for x in image_inputs]

     return image_inputs
 def      UpperCAmelCase_							(				self      :int							)->							List[Any]:
     A__				=			self.get_tokenizer()
     A__				=			self.get_rust_tokenizer()
     A__				=			self.get_image_processor()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)
     processor_slow.save_pretrained(self.tmpdirname							)
     A__				=			CLIPProcessor.from_pretrained(self.tmpdirname						,       use_fast=lowercase_							)

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)
     processor_fast.save_pretrained(self.tmpdirname							)
     A__				=			CLIPProcessor.from_pretrained(self.tmpdirname							)

     self.assertEqual(processor_slow.tokenizer.get_vocab()						,       tokenizer_slow.get_vocab()							)
     self.assertEqual(processor_fast.tokenizer.get_vocab()						,       tokenizer_fast.get_vocab()							)
     self.assertEqual(tokenizer_slow.get_vocab()						,       tokenizer_fast.get_vocab()							)
     self.assertIsInstance(processor_slow.tokenizer						,       lowercase_							)
     self.assertIsInstance(processor_fast.tokenizer						,       lowercase_							)

     self.assertEqual(processor_slow.image_processor.to_json_string()						,       image_processor.to_json_string()							)
     self.assertEqual(processor_fast.image_processor.to_json_string()						,       image_processor.to_json_string()							)
     self.assertIsInstance(processor_slow.image_processor						,       lowercase_							)
     self.assertIsInstance(processor_fast.image_processor						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Optional[int]:
     A__				=			CLIPProcessor(tokenizer=self.get_tokenizer()						,       image_processor=self.get_image_processor()							)
     processor.save_pretrained(self.tmpdirname							)

     A__				=			self.get_tokenizer(bos_token="(BOS)"						,       eos_token="(EOS)"							)
     A__				=			self.get_image_processor(do_normalize=lowercase_						,       padding_value=1.0							)

     A__				=			CLIPProcessor.from_pretrained(
         self.tmpdirname						,       bos_token="(BOS)"						,       eos_token="(EOS)"						,       do_normalize=lowercase_						,       padding_value=1.0							)

     self.assertEqual(processor.tokenizer.get_vocab()						,       tokenizer_add_kwargs.get_vocab()							)
     self.assertIsInstance(processor.tokenizer						,       lowercase_							)

     self.assertEqual(processor.image_processor.to_json_string()						,       image_processor_add_kwargs.to_json_string()							)
     self.assertIsInstance(processor.image_processor						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Tuple:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			self.prepare_image_inputs()

     A__				=			image_processor(lowercase_						,       return_tensors="np"							)
     A__				=			processor(images=lowercase_						,       return_tensors="np"							)

     for key in input_image_proc.keys():
         self.assertAlmostEqual(input_image_proc[key].sum()						,       input_processor[key].sum()						,       delta=1E-2							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			"lower newer"

     A__				=			processor(text=lowercase_							)

     A__				=			tokenizer(lowercase_							)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]						,       encoded_processor[key]							)
 def      UpperCAmelCase_							(				self      :str							)->							Any:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			"lower newer"
     A__				=			self.prepare_image_inputs()

     A__				=			processor(text=lowercase_						,       images=lowercase_							)

     self.assertListEqual(list(inputs.keys()							)						,       ["input_ids", "attention_mask", "pixel_values"]							)

     # test if it raises when no input is passed
     with pytest.raises(lowercase_							):
         processor()
 def      UpperCAmelCase_							(				self      :Tuple							)->							Tuple:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     A__				=			processor.batch_decode(lowercase_							)
     A__				=			tokenizer.batch_decode(lowercase_							)

     self.assertListEqual(lowercase_						,       lowercase_							)



 def      UpperCAmelCase_							(				self      :List[Any]							)->							Dict:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			"lower newer"
     A__				=			self.prepare_image_inputs()

     A__				=			processor(text=lowercase_						,       images=lowercase_							)

     self.assertListEqual(list(inputs.keys()							)						,       processor.model_input_names							)



