

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 os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						"""ylacombe/bark-small"""
							lowercase__	     =						tempfile.mkdtemp()
							lowercase__	     =						"""en_speaker_1"""
							lowercase__	     =						"""This is a test string"""
							lowercase__	     =						"""speaker_embeddings_path.json"""
							lowercase__	     =						"""speaker_embeddings"""

					def     lowerCamelCase__	(self       :		str ,   **_UpperCAmelCase       :		Optional[int]						)		->  str:




							"""simple docstring"""


							return AutoTokenizer.from_pretrained(self.checkpoint ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							shutil.rmtree(self.tmpdirname						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)
							lowercase__	     =						BarkProcessor.from_pretrained(self.tmpdirname						)

							self.assertEqual(processor.tokenizer.get_vocab() ,   tokenizer.get_vocab()						)

					@slow
					def     lowerCamelCase__	(self       :		str						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   )
							processor.save_pretrained(
							    self.tmpdirname ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   speaker_embeddings_directory=self.speaker_embeddings_directory ,   )

							lowercase__	     =						self.get_tokenizer(bos_token="""(BOS)""" ,   eos_token="""(EOS)"""						)

							lowercase__	     =						BarkProcessor.from_pretrained(
							    self.tmpdirname ,   self.speaker_embeddings_dict_path ,   bos_token="""(BOS)""" ,   eos_token="""(EOS)""" ,   )

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

					def     lowerCamelCase__	(self       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   )

							lowercase__	     =						35
							lowercase__	     =						2
							lowercase__	     =						8

							lowercase__	     =						{
							    """semantic_prompt""": np.ones(_UpperCAmelCase						),
							    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)						),
							    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)						),
							}

							# test providing already loaded voice_preset
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)

							lowercase__	     =						inputs["""history_prompt"""]
							for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist() ,   processed_voice_preset.get(_UpperCAmelCase ,   np.array([]						)						).tolist()						)

							# test loading voice preset from npz file
							lowercase__	     =						os.path.join(self.tmpdirname ,   """file.npz"""						)
							np.savez(_UpperCAmelCase ,   **_UpperCAmelCase						)
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)
							lowercase__	     =						inputs["""history_prompt"""]

							for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist() ,   processed_voice_preset.get(_UpperCAmelCase ,   np.array([]						)						).tolist()						)

							# test loading voice preset from the hub
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=self.voice_preset						)


					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							lowercase__	     =						processor(text=self.input_string						)

							lowercase__	     =						tokenizer(
							    self.input_string ,   padding="""max_length""" ,   max_length=256 ,   add_special_tokens=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   return_token_type_ids=_UpperCAmelCase ,   )

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

