

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_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'''),
												    ]			)

												# 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   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_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   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      ['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      dct.pop(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      val


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


@torch.no_grad()
def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=True			):
						snake_case_  =      ViTConfig()
						# patch_size
						if model_name[-1] == "8":
												snake_case_  =      8
						# set labels if required
						if not base_model:
												snake_case_  =      1000
												snake_case_  =      '''huggingface/label-files'''
												snake_case_  =      '''imagenet-1k-id2label.json'''
												snake_case_  =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						repo_type='''dataset'''			)    ,						'''r'''			)			)
												snake_case_  =      {int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
												snake_case_  =      idalabel
												snake_case_  =      {v: k for k, v in idalabel.items()}
						# size of the architecture
						if model_name in ["dino_vits8", "dino_vits16"]:
												snake_case_  =      384
												snake_case_  =      1536
												snake_case_  =      12
												snake_case_  =      6

						# load original model from torch hub
						snake_case_  =      torch.hub.load('''facebookresearch/dino:main'''    ,						SCREAMING_SNAKE_CASE__			)
						original_model.eval()

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

						# load HuggingFace model
						if base_model:
												snake_case_  =      ViTModel(SCREAMING_SNAKE_CASE__    ,						add_pooling_layer=SCREAMING_SNAKE_CASE__			).eval()
						else:
												snake_case_  =      ViTForImageClassification(SCREAMING_SNAKE_CASE__			).eval()
						model.load_state_dict(SCREAMING_SNAKE_CASE__			)

						# Check outputs on an image, prepared by ViTImageProcessor
						snake_case_  =      ViTImageProcessor()
						snake_case_  =      image_processor(images=prepare_img()    ,						return_tensors='''pt'''			)
						snake_case_  =      encoding['''pixel_values''']
						snake_case_  =      model(SCREAMING_SNAKE_CASE__			)

						if base_model:
												snake_case_  =      original_model(SCREAMING_SNAKE_CASE__			)
												assert torch.allclose(SCREAMING_SNAKE_CASE__    ,						outputs.last_hidden_state[:, 0, :]    ,						atol=1E-1			)
						else:
												snake_case_  =      original_model(SCREAMING_SNAKE_CASE__			)
												assert logits.shape == outputs.logits.shape
												assert torch.allclose(SCREAMING_SNAKE_CASE__    ,						outputs.logits    ,						atol=1E-3			)

						Path(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
						print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''			)
						model.save_pretrained(SCREAMING_SNAKE_CASE__			)
						print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
					lowerCAmelCase_			      =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--model_name''',
					    default='''dino_vitb16''',
					    type=str,
					    help='''Name of the model trained with DINO 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(
					    '''--base_model''',
					    action='''store_true''',
					    help='''Whether to only convert the base model (no projection head weights).''',
					)

					parser.set_defaults(base_model=True)
					lowerCAmelCase_			      =      parser.parse_args()
					convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)

import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						MvpTokenizer
       __lowerCamelCase				       =						MvpTokenizerFast
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						filter_roberta_detectors


       def 	_snake_case				(    self				)						->    int:
              super().setUp()
              SCREAMING_SNAKE_CASE_							:				List[str]		=[
                  '''l''',
                  '''o''',
                  '''w''',
                  '''e''',
                  '''r''',
                  '''s''',
                  '''t''',
                  '''i''',
                  '''d''',
                  '''n''',
                  '''\u0120''',
                  '''\u0120l''',
                  '''\u0120n''',
                  '''\u0120lo''',
                  '''\u0120low''',
                  '''er''',
                  '''\u0120lowest''',
                  '''\u0120newer''',
                  '''\u0120wider''',
                  '''<unk>''',
              ]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=dict(zip(__A						,       range(len(__A				)				)				)				)
              SCREAMING_SNAKE_CASE_							:				int		=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
              SCREAMING_SNAKE_CASE_							:				Dict		={'''unk_token''': '''<unk>'''}

              SCREAMING_SNAKE_CASE_							:				Any		=os.path.join(self.tmpdirname						,       VOCAB_FILES_NAMES['''vocab_file''']				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=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				)				)


       def 	_snake_case				(    self						,       **__A				)						->    int:
              kwargs.update(self.special_tokens_map				)
              return self.tokenizer_class.from_pretrained(self.tmpdirname						,       **__A				)


       def 	_snake_case				(    self						,       **__A				)						->    Any:
              kwargs.update(self.special_tokens_map				)
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,       **__A				)


       def 	_snake_case				(    self						,       __A				)						->    Any:
              return "lower newer", "lower newer"


       @cached_property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''				)


       @cached_property
       def 	_snake_case				(    self				)						->    Optional[int]:
              return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''				)


       @require_torch
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				str		=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              SCREAMING_SNAKE_CASE_							:				Tuple		=[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer(__A						,       max_length=len(__A				)						,       padding=__A						,       return_tensors='''pt'''				)
                     self.assertIsInstance(__A						,       __A				)

                     self.assertEqual((2, 9)						,       batch.input_ids.shape				)
                     self.assertEqual((2, 9)						,       batch.attention_mask.shape				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=batch.input_ids.tolist()[0]
                     self.assertListEqual(__A						,       __A				)
                     # Test that special tokens are reset


       @require_torch
       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer(__A						,       padding=__A						,       return_tensors='''pt'''				)
                     # check if input_ids are returned and no labels
                     self.assertIn('''input_ids'''						,       __A				)
                     self.assertIn('''attention_mask'''						,       __A				)
                     self.assertNotIn('''labels'''						,       __A				)
                     self.assertNotIn('''decoder_attention_mask'''						,       __A				)


       @require_torch
       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Any		=[
                  '''Summary of the text.''',
                  '''Another summary.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer(text_target=__A						,       max_length=32						,       padding='''max_length'''						,       return_tensors='''pt'''				)
                     self.assertEqual(32						,       targets['''input_ids'''].shape[1]				)


       @require_torch
       def 	_snake_case				(    self				)						->    str:
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer(
                         ['''I am a small frog''' * 1_024, '''I am a small frog''']						,       padding=__A						,       truncation=__A						,       return_tensors='''pt'''				)
                     self.assertIsInstance(__A						,       __A				)
                     self.assertEqual(batch.input_ids.shape						,       (2, 1_024)				)


       @require_torch
       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				str		=['''A long paragraph for summarization.''']
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=[
                  '''Summary of the text.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer(__A						,       text_target=__A						,       return_tensors='''pt'''				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=inputs['''input_ids''']
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=inputs['''labels''']
                     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
                     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
                     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
                     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)


       def 	_snake_case				(    self				)						->    Any:
              pass





       def 	_snake_case				(    self				)						->    Optional[Any]:
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.rust_tokenizer_class.from_pretrained(__A						,       **__A				)
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.tokenizer_class.from_pretrained(__A						,       **__A				)
                            SCREAMING_SNAKE_CASE_							:				List[Any]		='''A, <mask> AllenNLP sentence.'''
                            SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer_r.encode_plus(__A						,       add_special_tokens=__A						,       return_token_type_ids=__A				)
                            SCREAMING_SNAKE_CASE_							:				Any		=tokenizer_p.encode_plus(__A						,       add_special_tokens=__A						,       return_token_type_ids=__A				)

                            # token_type_ids should put 0 everywhere
                            self.assertEqual(sum(tokens_r['''token_type_ids''']				)						,       sum(tokens_p['''token_type_ids''']				)				)

                            # attention_mask should put 1 everywhere, so sum over length should be 1
                            self.assertEqual(
                                sum(tokens_r['''attention_mask''']				) / len(tokens_r['''attention_mask''']				)						,       sum(tokens_p['''attention_mask''']				) / len(tokens_p['''attention_mask''']				)						,       )

                            SCREAMING_SNAKE_CASE_							:				Any		=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
                            SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']				)

                            # Rust correctly handles the space before the mask while python doesnt
                            self.assertSequenceEqual(tokens_p['''input_ids''']						,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
                            self.assertSequenceEqual(tokens_r['''input_ids''']						,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

                            self.assertSequenceEqual(
                                __A						,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
                            self.assertSequenceEqual(
                                __A						,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
