





'''simple docstring'''






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def      __A						(							a_						:  Tuple      ,a_						:  Dict	):
		lowerCAmelCase     :    Any						=   old_name

		if "patch_embed" in old_name:
				lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    Optional[int]						=   old_name.split("."	)

				if layer == "0":
						lowerCAmelCase     :    Tuple						=   old_name.replace("0"      ,"convolution1"	)
				elif layer == "1":
						lowerCAmelCase     :    List[str]						=   old_name.replace("1"      ,"batchnorm_before"	)
				elif layer == "3":
						lowerCAmelCase     :    Dict						=   old_name.replace("3"      ,"convolution2"	)
				else:
						lowerCAmelCase     :    Tuple						=   old_name.replace("4"      ,"batchnorm_after"	)

		if "network" in old_name and re.search(R"\d\.\d"      ,a_	):
				lowerCAmelCase     :    Tuple						=   R"\b\d{2}\b"
				if bool(re.search(a_      ,a_	)	):
						lowerCAmelCase     :    str						=   re.search(R"\d\.\d\d."      ,a_	).group()
				else:
						lowerCAmelCase     :    str						=   re.search(R"\d\.\d."      ,a_	).group()
				if int(match[0]	) < 6:
						lowerCAmelCase     :    str						=   old_name.replace(a_      ,""	)
						lowerCAmelCase     :    str						=   trimmed_name.replace("network"      ,match[0] + ".meta4D_layers.blocks." + match[2:-1]	)
						lowerCAmelCase     :    Any						=   "intermediate_stages." + trimmed_name
				else:
						lowerCAmelCase     :    int						=   old_name.replace(a_      ,""	)
						if int(match[2]	) < num_meta4D_last_stage:
								lowerCAmelCase     :    Tuple						=   trimmed_name.replace("network"      ,"meta4D_layers.blocks." + match[2]	)
						else:
								lowerCAmelCase     :    List[str]						=   str(int(match[2]	) - num_meta4D_last_stage	)
								lowerCAmelCase     :    Optional[Any]						=   trimmed_name.replace("network"      ,"meta3D_layers.blocks." + layer_index	)
								if "norm1" in old_name:
										lowerCAmelCase     :    str						=   trimmed_name.replace("norm1"      ,"layernorm1"	)
								elif "norm2" in old_name:
										lowerCAmelCase     :    List[str]						=   trimmed_name.replace("norm2"      ,"layernorm2"	)
								elif "fc1" in old_name:
										lowerCAmelCase     :    Tuple						=   trimmed_name.replace("fc1"      ,"linear_in"	)
								elif "fc2" in old_name:
										lowerCAmelCase     :    Union[str, Any]						=   trimmed_name.replace("fc2"      ,"linear_out"	)

						lowerCAmelCase     :    Optional[int]						=   "last_stage." + trimmed_name

		elif "network" in old_name and re.search(R".\d."      ,a_	):
				lowerCAmelCase     :    Any						=   old_name.replace("network"      ,"intermediate_stages"	)

		if "fc" in new_name:
				lowerCAmelCase     :    Optional[Any]						=   new_name.replace("fc"      ,"convolution"	)
		elif ("norm1" in new_name) and ("layernorm1" not in new_name):
				lowerCAmelCase     :    Any						=   new_name.replace("norm1"      ,"batchnorm_before"	)
		elif ("norm2" in new_name) and ("layernorm2" not in new_name):
				lowerCAmelCase     :    List[Any]						=   new_name.replace("norm2"      ,"batchnorm_after"	)
		if "proj" in new_name:
				lowerCAmelCase     :    Optional[Any]						=   new_name.replace("proj"      ,"projection"	)
		if "dist_head" in new_name:
				lowerCAmelCase     :    Union[str, Any]						=   new_name.replace("dist_head"      ,"distillation_classifier"	)
		elif "head" in new_name:
				lowerCAmelCase     :    Optional[Any]						=   new_name.replace("head"      ,"classifier"	)
		elif "patch_embed" in new_name:
				lowerCAmelCase     :    Optional[int]						=   "efficientformer." + new_name
		elif new_name == "norm.weight" or new_name == "norm.bias":
				lowerCAmelCase     :    Tuple						=   new_name.replace("norm"      ,"layernorm"	)
				lowerCAmelCase     :    List[Any]						=   "efficientformer." + new_name
		else:
				lowerCAmelCase     :    List[str]						=   "efficientformer.encoder." + new_name

		return new_name






def      __A						(							a_						:  Optional[Any]      ,a_						:  Optional[Any]	):
		for key in checkpoint.copy().keys():
				lowerCAmelCase     :    Optional[Any]						=   checkpoint.pop(a_	)
				lowerCAmelCase     :    List[Any]						=   val

		return checkpoint






def      __A						(							):
		lowerCAmelCase     :    Any						=   "http://images.cocodataset.org/val2017/000000039769.jpg"
		lowerCAmelCase     :    Tuple						=   Image.open(requests.get(a_      ,stream=a_	).raw	)

		return image






def      __A						(							a_						:  Path      ,a_						:  Path      ,a_						:  Path      ,a_						:  bool	):
		lowerCAmelCase     :    Optional[Any]						=   torch.load(a_      ,map_location="cpu"	)["model"]
		lowerCAmelCase     :    Optional[int]						=   EfficientFormerConfig.from_json_file(a_	)
		lowerCAmelCase     :    str						=   EfficientFormerForImageClassificationWithTeacher(a_	)
		lowerCAmelCase     :    Tuple						=   "_".join(checkpoint_path.split("/"	)[-1].split("."	)[0].split("_"	)[:-1]	)

		lowerCAmelCase     :    str						=   config.depths[-1] - config.num_metaad_blocks + 1
		lowerCAmelCase     :    List[str]						=   convert_torch_checkpoint(a_      ,a_	)

		model.load_state_dict(a_	)
		model.eval()

		lowerCAmelCase     :    Any						=   {
		    "bilinear": PILImageResampling.BILINEAR,
		    "bicubic": PILImageResampling.BICUBIC,
		    "nearest": PILImageResampling.NEAREST,
		}

		# prepare image
		lowerCAmelCase     :    Dict						=   prepare_img()
		lowerCAmelCase     :    List[str]						=   2_5_6
		lowerCAmelCase     :    List[Any]						=   2_2_4
		lowerCAmelCase     :    Any						=   EfficientFormerImageProcessor(
		    size={"shortest_edge": image_size}      ,crop_size={"height": crop_size, "width": crop_size}      ,resample=pillow_resamplings["bicubic"]      ,)
		lowerCAmelCase     :    Tuple						=   processor(images=a_      ,return_tensors="pt"	).pixel_values

		# original processing pipeline
		lowerCAmelCase     :    Union[str, Any]						=   Compose(
		    [
		        Resize(a_      ,interpolation=pillow_resamplings["bicubic"]	),
		        CenterCrop(a_	),
		        ToTensor(),
		        Normalize(a_      ,a_	),
		    ]	)
		lowerCAmelCase     :    str						=   image_transforms(a_	).unsqueeze(0	)

		assert torch.allclose(a_      ,a_	)

		lowerCAmelCase     :    Optional[int]						=   model(a_	)
		lowerCAmelCase     :    Optional[Any]						=   outputs.logits

		lowerCAmelCase     :    Optional[int]						=   (1, 1_0_0_0)

		if "l1" in model_name:
				lowerCAmelCase     :    List[Any]						=   torch.Tensor(
				    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]	)
				assert torch.allclose(logits[0, :1_0]      ,a_      ,atol=1e-3	)
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				lowerCAmelCase     :    Any						=   torch.Tensor(
				    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]	)
				assert torch.allclose(logits[0, :1_0]      ,a_      ,atol=1e-3	)
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				lowerCAmelCase     :    List[str]						=   torch.Tensor(
				    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]	)
				assert logits.shape == expected_shape
		else:
				raise ValueError(
				    f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''	)

		# Save Checkpoints
		Path(a_	).mkdir(exist_ok=a_	)
		model.save_pretrained(a_	)
		print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''	)
		processor.save_pretrained(a_	)
		print(f'''Processor successfuly saved at {pytorch_dump_path}'''	)

		if push_to_hub:
				print("Pushing model to the hub..."	)

				model.push_to_hub(
				    repo_id=f'''Bearnardd/{pytorch_dump_path}'''      ,commit_message="Add model"      ,use_temp_dir=a_      ,)
				processor.push_to_hub(
				    repo_id=f'''Bearnardd/{pytorch_dump_path}'''      ,commit_message="Add image processor"      ,use_temp_dir=a_      ,)


if __name__ == "__main__":
					lowerCAmelCase       		=      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--pytorch_model_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to EfficientFormer pytorch checkpoint.""",
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The json file for EfficientFormer model config.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)

					parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
					parser.add_argument(
					    """--no-push_to_hub""",
					    dest="""push_to_hub""",
					    action="""store_false""",
					    help="""Do not push model and image processor to the hub""",
					)
					parser.set_defaults(push_to_hub=True)

					lowerCAmelCase       		=      parser.parse_args()
					convert_efficientformer_checkpoint(
					    checkpoint_path=args.pytorch_model_path,
					    efficientformer_config_file=args.config_file,
					    pytorch_dump_path=args.pytorch_dump_path,
					    push_to_hub=args.push_to_hub,
					)










'''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 OwlViTImageProcessor, OwlViTProcessor
@require_vision
class 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   tempfile.mkdtemp()

			# fmt: off
			lowerCAmelCase     :    List[Any]						=   ["", "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
			lowerCAmelCase     :    Dict						=   dict(zip(a_		,      range(len(a_	)	)	)	)
			lowerCAmelCase     :    List[str]						=   ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
			lowerCAmelCase     :    List[Any]						=   {"unk_token": "<unk>"}

			lowerCAmelCase     :    int						=   os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]	)
			lowerCAmelCase     :    List[str]						=   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(a_	) + "\n"	)
			with open(self.merges_file		,      "w"		,      encoding="utf-8"	) as fp:
					fp.write("\n".join(a_	)	)

			lowerCAmelCase     :    Dict						=   {
			    "do_resize": True,
			    "size": 20,
			    "do_center_crop": True,
			    "crop_size": 18,
			    "do_normalize": True,
			    "image_mean": [0.48145466, 0.4578275, 0.40821073],
			    "image_std": [0.26862954, 0.26130258, 0.27577711],
			}
			lowerCAmelCase     :    int						=   os.path.join(self.tmpdirname		,      a_	)
			with open(self.image_processor_file		,      "w"		,      encoding="utf-8"	) as fp:
					json.dump(a_		,      a_	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return CLIPTokenizer.from_pretrained(self.tmpdirname		,      pad_token="!"		,      **a_	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      pad_token="!"		,      **a_	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return OwlViTImageProcessor.from_pretrained(self.tmpdirname		,      **a_	)



	def   _lowerCamelCase    (   self	):
			shutil.rmtree(self.tmpdirname	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   [np.random.randint(255		,      size=(3, 30, 400)		,      dtype=np.uinta	)]

			lowerCAmelCase     :    Optional[int]						=   [Image.fromarray(np.moveaxis(a_		,      0		,      -1	)	) for x in image_inputs]

			return image_inputs



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.get_tokenizer()
			lowerCAmelCase     :    Union[str, Any]						=   self.get_rust_tokenizer()
			lowerCAmelCase     :    Optional[int]						=   self.get_image_processor()

			lowerCAmelCase     :    Optional[int]						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)
			processor_slow.save_pretrained(self.tmpdirname	)
			lowerCAmelCase     :    Optional[Any]						=   OwlViTProcessor.from_pretrained(self.tmpdirname		,      use_fast=a_	)

			lowerCAmelCase     :    int						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)
			processor_fast.save_pretrained(self.tmpdirname	)
			lowerCAmelCase     :    List[Any]						=   OwlViTProcessor.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		,      a_	)
			self.assertIsInstance(processor_fast.tokenizer		,      a_	)

			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		,      a_	)
			self.assertIsInstance(processor_fast.image_processor		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   OwlViTProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()	)
			processor.save_pretrained(self.tmpdirname	)

			lowerCAmelCase     :    int						=   self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"	)
			lowerCAmelCase     :    Dict						=   self.get_image_processor(do_normalize=a_	)

			lowerCAmelCase     :    Optional[int]						=   OwlViTProcessor.from_pretrained(
			    self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=a_	)

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

			self.assertEqual(processor.image_processor.to_json_string()		,      image_processor_add_kwargs.to_json_string()	)
			self.assertIsInstance(processor.image_processor		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.get_image_processor()
			lowerCAmelCase     :    Optional[int]						=   self.get_tokenizer()

			lowerCAmelCase     :    str						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    Optional[int]						=   self.prepare_image_inputs()

			lowerCAmelCase     :    List[str]						=   image_processor(a_		,      return_tensors="np"	)
			lowerCAmelCase     :    Any						=   processor(images=a_		,      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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   self.get_image_processor()
			lowerCAmelCase     :    List[str]						=   self.get_tokenizer()

			lowerCAmelCase     :    int						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    Tuple						=   "lower newer"

			lowerCAmelCase     :    int						=   processor(text=a_		,      return_tensors="np"	)

			lowerCAmelCase     :    Tuple						=   tokenizer(a_		,      return_tensors="np"	)

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key][0].tolist()		,      encoded_processor[key][0].tolist()	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.get_image_processor()
			lowerCAmelCase     :    int						=   self.get_tokenizer()

			lowerCAmelCase     :    List[str]						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    Dict						=   "lower newer"
			lowerCAmelCase     :    Tuple						=   self.prepare_image_inputs()

			lowerCAmelCase     :    Any						=   processor(text=a_		,      images=a_	)

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

			# test if it raises when no input is passed
			with pytest.raises(a_	):
					processor()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   "google/owlvit-base-patch32"
			lowerCAmelCase     :    List[Any]						=   OwlViTProcessor.from_pretrained(a_	)

			lowerCAmelCase     :    Dict						=   ["cat", "nasa badge"]
			lowerCAmelCase     :    Optional[int]						=   processor(text=a_	)

			lowerCAmelCase     :    Optional[int]						=   16
			self.assertListEqual(list(inputs.keys()	)		,      ["input_ids", "attention_mask"]	)
			self.assertEqual(inputs["input_ids"].shape		,      (2, seq_length)	)

			# test if it raises when no input is passed
			with pytest.raises(a_	):
					processor()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   "google/owlvit-base-patch32"
			lowerCAmelCase     :    Tuple						=   OwlViTProcessor.from_pretrained(a_	)

			lowerCAmelCase     :    List[Any]						=   [["cat", "nasa badge"], ["person"]]
			lowerCAmelCase     :    int						=   processor(text=a_	)

			lowerCAmelCase     :    List[Any]						=   16
			lowerCAmelCase     :    Union[str, Any]						=   len(a_	)
			lowerCAmelCase     :    str						=   max([len(a_	) for texts in input_texts]	)

			self.assertListEqual(list(inputs.keys()	)		,      ["input_ids", "attention_mask"]	)
			self.assertEqual(inputs["input_ids"].shape		,      (batch_size * num_max_text_queries, seq_length)	)

			# test if it raises when no input is passed
			with pytest.raises(a_	):
					processor()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   "google/owlvit-base-patch32"
			lowerCAmelCase     :    Union[str, Any]						=   OwlViTProcessor.from_pretrained(a_	)

			lowerCAmelCase     :    List[Any]						=   ["cat", "nasa badge"]
			lowerCAmelCase     :    Any						=   processor(text=a_	)

			lowerCAmelCase     :    Optional[Any]						=   16
			lowerCAmelCase     :    Optional[Any]						=   inputs["input_ids"]
			lowerCAmelCase     :    str						=   [
			    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
			    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
			]

			self.assertListEqual(list(inputs.keys()	)		,      ["input_ids", "attention_mask"]	)
			self.assertEqual(inputs["input_ids"].shape		,      (2, seq_length)	)
			self.assertListEqual(list(input_ids[0]	)		,      predicted_ids[0]	)
			self.assertListEqual(list(input_ids[1]	)		,      predicted_ids[1]	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.get_image_processor()
			lowerCAmelCase     :    Optional[Any]						=   self.get_tokenizer()

			lowerCAmelCase     :    Dict						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    str						=   self.prepare_image_inputs()
			lowerCAmelCase     :    Tuple						=   self.prepare_image_inputs()

			lowerCAmelCase     :    List[Any]						=   processor(images=a_		,      query_images=a_	)

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

			# test if it raises when no input is passed
			with pytest.raises(a_	):
					processor()





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.get_image_processor()
			lowerCAmelCase     :    int						=   self.get_tokenizer()

			lowerCAmelCase     :    str						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    Union[str, Any]						=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			lowerCAmelCase     :    List[Any]						=   processor.batch_decode(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   tokenizer.batch_decode(a_	)

			self.assertListEqual(a_		,      a_	)



