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 CLIPSegProcessor, ViTImageProcessor





@require_vision
class 					A							(    unittest.TestCase					):


			def       lowercase_				(self					:						Union[str, Any]		)						->    int:
									"""simple docstring"""



									UpperCAmelCase__								=  tempfile.mkdtemp()

									# fmt: off
									UpperCAmelCase__								=  ["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
									UpperCAmelCase__								=  dict(zip(lowercase_	,  range(len(lowercase_		)		)		)		)
									UpperCAmelCase__								=  ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
									UpperCAmelCase__								=  {"unk_token": "<unk>"}

									UpperCAmelCase__								=  os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]		)
									UpperCAmelCase__								=  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_		)		)

									UpperCAmelCase__								=  {
									    "do_resize": True,
									    "size": 2_0,
									    "do_center_crop": True,
									    "crop_size": 1_8,
									    "do_normalize": True,
									    "image_mean": [0.48145466, 0.4578275, 0.40821073],
									    "image_std": [0.26862954, 0.26130258, 0.27577711],
									}
									UpperCAmelCase__								=  os.path.join(self.tmpdirname	,  lowercase_		)
									with open(self.image_processor_file	,  "w"	,  encoding="utf-8"		) as fp:
															json.dump(lowercase_	,  lowercase_		)


			def       lowercase_				(self					:						List[Any]	,  **__UpperCAmelCase					:						Optional[Any]		)						->    Optional[int]:
									"""simple docstring"""



									return CLIPTokenizer.from_pretrained(self.tmpdirname	,  **lowercase_		)


			def       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Tuple		)						->    Optional[int]:
									"""simple docstring"""



									return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,  **lowercase_		)


			def       lowercase_				(self					:						Optional[int]	,  **__UpperCAmelCase					:						Dict		)						->    Dict:
									"""simple docstring"""



									return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **lowercase_		)


			def       lowercase_				(self					:						Any		)						->    Dict:
									"""simple docstring"""



									shutil.rmtree(self.tmpdirname		)


			def       lowercase_				(self					:						int		)						->    List[Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  [np.random.randint(2_5_5	,  size=(3, 3_0, 4_0_0)	,  dtype=np.uinta		)]

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

									return image_inputs


			def       lowercase_				(self					:						Optional[int]		)						->    Union[str, Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  self.get_tokenizer()
									UpperCAmelCase__								=  self.get_rust_tokenizer()
									UpperCAmelCase__								=  self.get_image_processor()

									UpperCAmelCase__								=  CLIPSegProcessor(tokenizer=lowercase_	,  image_processor=lowercase_		)
									processor_slow.save_pretrained(self.tmpdirname		)
									UpperCAmelCase__								=  CLIPSegProcessor.from_pretrained(self.tmpdirname	,  use_fast=lowercase_		)

									UpperCAmelCase__								=  CLIPSegProcessor(tokenizer=lowercase_	,  image_processor=lowercase_		)
									processor_fast.save_pretrained(self.tmpdirname		)
									UpperCAmelCase__								=  CLIPSegProcessor.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       lowercase_				(self					:						str		)						->    List[str]:
									"""simple docstring"""



									UpperCAmelCase__								=  CLIPSegProcessor(tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()		)
									processor.save_pretrained(self.tmpdirname		)

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

									UpperCAmelCase__								=  CLIPSegProcessor.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       lowercase_				(self					:						Optional[Any]		)						->    Optional[Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  self.get_image_processor()
									UpperCAmelCase__								=  self.get_tokenizer()

									UpperCAmelCase__								=  CLIPSegProcessor(tokenizer=lowercase_	,  image_processor=lowercase_		)

									UpperCAmelCase__								=  self.prepare_image_inputs()

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

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1E-2		)


			def       lowercase_				(self					:						Tuple		)						->    List[Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  self.get_image_processor()
									UpperCAmelCase__								=  self.get_tokenizer()

									UpperCAmelCase__								=  CLIPSegProcessor(tokenizer=lowercase_	,  image_processor=lowercase_		)

									UpperCAmelCase__								=  "lower newer"

									UpperCAmelCase__								=  processor(text=lowercase_		)

									UpperCAmelCase__								=  tokenizer(lowercase_		)

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


			def       lowercase_				(self					:						List[str]		)						->    str:
									"""simple docstring"""



									UpperCAmelCase__								=  self.get_image_processor()
									UpperCAmelCase__								=  self.get_tokenizer()

									UpperCAmelCase__								=  CLIPSegProcessor(tokenizer=lowercase_	,  image_processor=lowercase_		)

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

									UpperCAmelCase__								=  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       lowercase_				(self					:						Any		)						->    Optional[Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  self.get_image_processor()
									UpperCAmelCase__								=  self.get_tokenizer()

									UpperCAmelCase__								=  CLIPSegProcessor(tokenizer=lowercase_	,  image_processor=lowercase_		)

									UpperCAmelCase__								=  self.prepare_image_inputs()
									UpperCAmelCase__								=  self.prepare_image_inputs()

									UpperCAmelCase__								=  processor(images=lowercase_	,  visual_prompt=lowercase_		)

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

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





			def       lowercase_				(self					:						Optional[int]		)						->    Union[str, Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  self.get_image_processor()
									UpperCAmelCase__								=  self.get_tokenizer()

									UpperCAmelCase__								=  CLIPSegProcessor(tokenizer=lowercase_	,  image_processor=lowercase_		)

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

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

									self.assertListEqual(lowercase_	,  lowercase_		)




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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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




def        lowerCAmelCase_			(					__A,  __A=False							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      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"""deit.encoder.layer.{i}.layernorm_before.weight""")							)
            rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")							)
            rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")							)
            rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")							)
            rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")							)
            rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")							)
            rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")							)
            rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")							)
            rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")							)
            rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")							)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("cls_token", "deit.embeddings.cls_token"),
              ("dist_token", "deit.embeddings.distillation_token"),
              ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
              ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
              ("pos_embed", "deit.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 "deit" from all keys that start with "deit"
            UpperCAmelCase__								=  [(pair[0], pair[1][4:]) if pair[1].startswith("deit"							) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("norm.weight", "deit.layernorm.weight"),
                    ("norm.bias", "deit.layernorm.bias"),
                    ("head.weight", "cls_classifier.weight"),
                    ("head.bias", "cls_classifier.bias"),
                    ("head_dist.weight", "distillation_classifier.weight"),
                    ("head_dist.bias", "distillation_classifier.bias"),
                ]							)

      return rename_keys




def        lowerCAmelCase_			(					__A,  __A,  __A=False							)					->    Tuple:







      '''simple docstring'''


      for i in range(config.num_hidden_layers							):
            if base_model:
                  UpperCAmelCase__								=  ""
            else:
                  UpperCAmelCase__								=  "deit."
            # 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        lowerCAmelCase_			(					__A,  __A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  dct.pop(__A							)
      UpperCAmelCase__								=  val




def        lowerCAmelCase_			(					)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  "http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase__								=  Image.open(requests.get(__A,  stream=__A							).raw							)
      return im




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  DeiTConfig()
      # all deit models have fine-tuned heads
      UpperCAmelCase__								=  False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      UpperCAmelCase__								=  1_000
      UpperCAmelCase__								=  "huggingface/label-files"
      UpperCAmelCase__								=  "imagenet-1k-id2label.json"
      UpperCAmelCase__								=  json.load(open(hf_hub_download(__A,  __A,  repo_type="dataset"							),  "r"							)							)
      UpperCAmelCase__								=  {int(__A							): v for k, v in idalabel.items()}
      UpperCAmelCase__								=  idalabel
      UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}
      UpperCAmelCase__								=  int(deit_name[-6:-4]							)
      UpperCAmelCase__								=  int(deit_name[-3:]							)
      # size of the architecture
      if deit_name[9:].startswith("tiny"							):
            UpperCAmelCase__								=  192
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  3
      elif deit_name[9:].startswith("small"							):
            UpperCAmelCase__								=  384
            UpperCAmelCase__								=  1_536
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  6
      if deit_name[9:].startswith("base"							):
            pass
      elif deit_name[4:].startswith("large"							):
            UpperCAmelCase__								=  1_024
            UpperCAmelCase__								=  4_096
            UpperCAmelCase__								=  24
            UpperCAmelCase__								=  16

      # load original model from timm
      UpperCAmelCase__								=  timm.create_model(__A,  pretrained=__A							)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      UpperCAmelCase__								=  timm_model.state_dict()
      UpperCAmelCase__								=  create_rename_keys(__A,  __A							)
      for src, dest in rename_keys:
            rename_key(__A,  __A,  __A							)
      read_in_q_k_v(__A,  __A,  __A							)

      # load HuggingFace model
      UpperCAmelCase__								=  DeiTForImageClassificationWithTeacher(__A							).eval()
      model.load_state_dict(__A							)

      # Check outputs on an image, prepared by DeiTImageProcessor
      UpperCAmelCase__								=  int(
          (256 / 224) * config.image_size							)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      UpperCAmelCase__								=  DeiTImageProcessor(size=__A,  crop_size=config.image_size							)
      UpperCAmelCase__								=  image_processor(images=prepare_img(),  return_tensors="pt"							)
      UpperCAmelCase__								=  encoding["pixel_values"]
      UpperCAmelCase__								=  model(__A							)

      UpperCAmelCase__								=  timm_model(__A							)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__A,  outputs.logits,  atol=1e-3							)

      Path(__A							).mkdir(exist_ok=__A							)
      print(f"""Saving model {deit_name} 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__":
      UpperCamelCase__    							= argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--deit_name',
          default='vit_deit_base_distilled_patch16_224',
          type=str,
          help='Name of the DeiT 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_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



