"""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()
__lowercase		:		Dict														=							logging.get_logger(__name__)





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





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(snake_case,      snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __snake_case				=							dct.pop(snake_case)
     __snake_case				=							val





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





@torch.no_grad()
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							ViTConfig()
     __snake_case				=							False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          __snake_case				=							True
          __snake_case				=							int(vit_name[-12:-10])
          __snake_case				=							int(vit_name[-9:-6])
     else:
          __snake_case				=							10_00
          __snake_case				=							'''huggingface/label-files'''
          __snake_case				=							'''imagenet-1k-id2label.json'''
          __snake_case				=							json.load(open(hf_hub_download(snake_case,      snake_case,      repo_type='''dataset'''),      '''r'''))
          __snake_case				=							{int(snake_case): v for k, v in idalabel.items()}
          __snake_case				=							idalabel
          __snake_case				=							{v: k for k, v in idalabel.items()}
          __snake_case				=							int(vit_name[-6:-4])
          __snake_case				=							int(vit_name[-3:])
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith('''tiny'''):
               __snake_case				=							1_92
               __snake_case				=							7_68
               __snake_case				=							12
               __snake_case				=							3
          elif vit_name[9:].startswith('''small'''):
               __snake_case				=							3_84
               __snake_case				=							15_36
               __snake_case				=							12
               __snake_case				=							6
          else:
               pass
     else:
          if vit_name[4:].startswith('''small'''):
               __snake_case				=							7_68
               __snake_case				=							23_04
               __snake_case				=							8
               __snake_case				=							8
          elif vit_name[4:].startswith('''base'''):
               pass
          elif vit_name[4:].startswith('''large'''):
               __snake_case				=							10_24
               __snake_case				=							40_96
               __snake_case				=							24
               __snake_case				=							16
          elif vit_name[4:].startswith('''huge'''):
               __snake_case				=							12_80
               __snake_case				=							51_20
               __snake_case				=							32
               __snake_case				=							16

    # load original model from timm
     __snake_case				=							timm.create_model(snake_case,      pretrained=snake_case)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     __snake_case				=							timm_model.state_dict()
     if base_model:
          remove_classification_head_(snake_case)
     __snake_case				=							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":
          __snake_case				=							ViTModel(snake_case).eval()
     else:
          __snake_case				=							ViTForImageClassification(snake_case).eval()
     model.load_state_dict(snake_case)

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

     if base_model:
          __snake_case				=							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:
          __snake_case				=							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__":
       __lowercase		:		int														=							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."
       )

       __lowercase		:		Union[str, Any]														=							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 CLIPSegProcessor, ViTImageProcessor



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[int]    )    ->       List[str]:
         __snake_case				=							tempfile.mkdtemp()

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

         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''vocab_file''']    )
         __snake_case				=							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_    )    )

         __snake_case				=							{
             '''do_resize''': True,
             '''size''': 20,
             '''do_center_crop''': True,
             '''crop_size''': 18,
             '''do_normalize''': True,
             '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
         }
         __snake_case				=							os.path.join(self.tmpdirname	,      A_    )
         with open(self.image_processor_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              json.dump(A_	,      A_    )
    def  lowercase					(		self							:     Optional[Any]	,      **A_							:     Dict    )    ->       Any:
         return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]	,      **A_							:     str    )    ->       str:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Any	,      **A_							:     Tuple    )    ->       Tuple:
         return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

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

         return image_inputs
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_rust_tokenizer()
         __snake_case				=							self.get_image_processor()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_slow.save_pretrained(self.tmpdirname    )
         __snake_case				=							CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A_    )

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_fast.save_pretrained(self.tmpdirname    )
         __snake_case				=							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	,      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  lowercase					(		self							:     Union[str, Any]    )    ->       Any:
         __snake_case				=							CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()    )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(bos_token='''(BOS)'''	,      eos_token='''(EOS)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_	,      padding_value=1.0    )

         __snake_case				=							CLIPSegProcessor.from_pretrained(
             self.tmpdirname	,      bos_token='''(BOS)'''	,      eos_token='''(EOS)'''	,      do_normalize=A_	,      padding_value=1.0    )

         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  lowercase					(		self							:     Any    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      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							:     List[str]    )    ->       List[Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''lower newer'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_    )

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

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							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  lowercase					(		self							:     Union[str, Any]    )    ->       Any:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(images=A_	,      visual_prompt=A_    )

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

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







    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

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

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )