


'''simple docstring'''

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a_  :				Any						= get_tests_dir("""fixtures/test_sentencepiece.model""")





@require_sentencepiece
@require_tokenizers
class      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  XGLMTokenizer
  _lowerCamelCase		    =  XGLMTokenizerFast
  _lowerCamelCase		    =  True
  _lowerCamelCase		    =  True






  def   snake_case		(    self     ):







   """simple docstring"""
   super().setUp()

   # We have a SentencePiece fixture for testing
   lowerCamelCase_      =  XGLMTokenizer(UpperCamelCase			,  keep_accents=UpperCamelCase     )
   tokenizer.save_pretrained(self.tmpdirname     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  "<pad>"
   lowerCamelCase_      =  1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase     )			,  UpperCamelCase     )
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase     )			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  list(self.get_tokenizer().get_vocab().keys()     )

   self.assertEqual(vocab_keys[0]			,  "<s>"     )
   self.assertEqual(vocab_keys[1]			,  "<pad>"     )
   self.assertEqual(len(UpperCamelCase     )			,  1008     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self.assertEqual(self.get_tokenizer().vocab_size			,  1008     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  XGLMTokenizer(UpperCamelCase			,  keep_accents=UpperCamelCase     )

   lowerCamelCase_      =  tokenizer.tokenize("This is a test"     )
   self.assertListEqual(UpperCamelCase			,  ["▁This", "▁is", "▁a", "▁t", "est"]     )

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(UpperCamelCase     )			,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,  )

   lowerCamelCase_      =  tokenizer.tokenize("I was born in 92000, and this is falsé."     )
   self.assertListEqual(
       UpperCamelCase			,  [
           SPIECE_UNDERLINE + "I",
           SPIECE_UNDERLINE + "was",
           SPIECE_UNDERLINE + "b",
           "or",
           "n",
           SPIECE_UNDERLINE + "in",
           SPIECE_UNDERLINE + "",
           "9",
           "2",
           "0",
           "0",
           "0",
           ",",
           SPIECE_UNDERLINE + "and",
           SPIECE_UNDERLINE + "this",
           SPIECE_UNDERLINE + "is",
           SPIECE_UNDERLINE + "f",
           "al",
           "s",
           "é",
           ".",
       ]			,  )
   lowerCamelCase_      =  tokenizer.convert_tokens_to_ids(UpperCamelCase     )
   self.assertListEqual(
       UpperCamelCase			,  [
           value + tokenizer.fairseq_offset
           for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
       ]			,  )

   lowerCamelCase_      =  tokenizer.convert_ids_to_tokens(UpperCamelCase     )
   self.assertListEqual(
       UpperCamelCase			,  [
           SPIECE_UNDERLINE + "I",
           SPIECE_UNDERLINE + "was",
           SPIECE_UNDERLINE + "b",
           "or",
           "n",
           SPIECE_UNDERLINE + "in",
           SPIECE_UNDERLINE + "",
           "<unk>",
           "2",
           "0",
           "0",
           "0",
           ",",
           SPIECE_UNDERLINE + "and",
           SPIECE_UNDERLINE + "this",
           SPIECE_UNDERLINE + "is",
           SPIECE_UNDERLINE + "f",
           "al",
           "s",
           "<unk>",
           ".",
       ]			,  )






  @cached_property
  def   snake_case		(    self     ):







   """simple docstring"""
   return XGLMTokenizer.from_pretrained("facebook/xglm-564M"     )






  def   snake_case		(    self     ):







   """simple docstring"""
   with tempfile.NamedTemporaryFile() as f:
    shutil.copyfile(UpperCamelCase			,  f.name     )
    lowerCamelCase_      =  XGLMTokenizer(f.name			,  keep_accents=UpperCamelCase     )
    lowerCamelCase_      =  pickle.dumps(UpperCamelCase     )
   pickle.loads(UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   if not self.test_rust_tokenizer:
    return

   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  self.get_rust_tokenizer()

   lowerCamelCase_      =  "I was born in 92000, and this is falsé."

   lowerCamelCase_      =  tokenizer.tokenize(UpperCamelCase     )
   lowerCamelCase_      =  rust_tokenizer.tokenize(UpperCamelCase     )
   self.assertListEqual(UpperCamelCase			,  UpperCamelCase     )

   lowerCamelCase_      =  tokenizer.encode(UpperCamelCase			,  add_special_tokens=UpperCamelCase     )
   lowerCamelCase_      =  rust_tokenizer.encode(UpperCamelCase			,  add_special_tokens=UpperCamelCase     )
   self.assertListEqual(UpperCamelCase			,  UpperCamelCase     )

   lowerCamelCase_      =  self.get_rust_tokenizer()
   lowerCamelCase_      =  tokenizer.encode(UpperCamelCase     )
   lowerCamelCase_      =  rust_tokenizer.encode(UpperCamelCase     )
   self.assertListEqual(UpperCamelCase			,  UpperCamelCase     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  "Hello World!"
   lowerCamelCase_      =  [2, 3_1227, 4447, 35]

   self.assertListEqual(UpperCamelCase			,  self.big_tokenizer.encode(UpperCamelCase     )     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  (
       "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
       " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
   )
   # fmt: off
   lowerCamelCase_      =  [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
   # fmt: on

   self.assertListEqual(UpperCamelCase			,  self.big_tokenizer.encode(UpperCamelCase     )     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   # fmt: off
   lowerCamelCase_      =  {
       "input_ids": [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
       "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
   }  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=UpperCamelCase			,  model_name="facebook/xglm-564M"			,  padding=UpperCamelCase			,  )







'''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()
a_  :				int						= logging.get_logger(__name__)





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





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  ["head.weight", "head.bias"]
 for k in ignore_keys:
  state_dict.pop(UpperCAmelCase_					,				UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  dct.pop(UpperCAmelCase_		)
 lowerCamelCase_      =  val





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





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  ViTConfig()
 lowerCamelCase_      =  False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  True
  lowerCamelCase_      =  int(vit_name[-12:-10]		)
  lowerCamelCase_      =  int(vit_name[-9:-6]		)
 else:
  lowerCamelCase_      =  1000
  lowerCamelCase_      =  "huggingface/label-files"
  lowerCamelCase_      =  "imagenet-1k-id2label.json"
  lowerCamelCase_      =  json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type="dataset"		)					,				"r"		)		)
  lowerCamelCase_      =  {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
  lowerCamelCase_      =  idalabel
  lowerCamelCase_      =  {v: k for k, v in idalabel.items()}
  lowerCamelCase_      =  int(vit_name[-6:-4]		)
  lowerCamelCase_      =  int(vit_name[-3:]		)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("tiny"		):
   lowerCamelCase_      =  192
   lowerCamelCase_      =  768
   lowerCamelCase_      =  12
   lowerCamelCase_      =  3
  elif vit_name[9:].startswith("small"		):
   lowerCamelCase_      =  384
   lowerCamelCase_      =  1536
   lowerCamelCase_      =  12
   lowerCamelCase_      =  6
  else:
   pass
 else:
  if vit_name[4:].startswith("small"		):
   lowerCamelCase_      =  768
   lowerCamelCase_      =  2304
   lowerCamelCase_      =  8
   lowerCamelCase_      =  8
  elif vit_name[4:].startswith("base"		):
   pass
  elif vit_name[4:].startswith("large"		):
   lowerCamelCase_      =  1024
   lowerCamelCase_      =  4096
   lowerCamelCase_      =  24
   lowerCamelCase_      =  16
  elif vit_name[4:].startswith("huge"		):
   lowerCamelCase_      =  1280
   lowerCamelCase_      =  5120
   lowerCamelCase_      =  32
   lowerCamelCase_      =  16

    # load original model from timm
 lowerCamelCase_      =  timm.create_model(UpperCAmelCase_					,				pretrained=UpperCAmelCase_		)
 timm_model.eval()

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

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  ViTModel(UpperCAmelCase_		).eval()
 else:
  lowerCamelCase_      =  ViTForImageClassification(UpperCAmelCase_		).eval()
 model.load_state_dict(UpperCAmelCase_		)

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

 if base_model:
  lowerCamelCase_      =  timm_model.forward_features(UpperCAmelCase_		)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.pooler_output					,				atol=1E-3		)
 else:
  lowerCamelCase_      =  timm_model(UpperCAmelCase_		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.logits					,				atol=1E-3		)

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


if __name__ == "__main__":
     a_  :				Union[str, Any]						= 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."""
     )

     a_  :				List[str]						= parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



