


'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_ :     Union[str, Any]								=      logging.getLogger()


def 			_lowercase       (				):
 __A  :		Any       =			argparse.ArgumentParser()
 parser.add_argument('-f'			)
 __A  :		Tuple       =			parser.parse_args()
 return args.f


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A  :		List[Any]       =			{}
 __A  :		Optional[Any]       =			os.path.join(UpperCamelCase__,				'all_results.json'			)
 if os.path.exists(UpperCamelCase__			):
  with open(UpperCamelCase__,				'r'			) as f:
   __A  :		Tuple       =			json.load(UpperCamelCase__			)
 else:
  raise ValueError(f"""can't find {path}"""			)
 return results


def 			_lowercase       (				):
 __A  :		List[str]       =			torch.cuda.is_available() and torch_device == 'cuda'
 return is_using_cuda and is_apex_available()


UpperCAmelCase_ :     Optional[Any]								=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''


 @classmethod
 def  snake_case__   (      cls						):

  """simple docstring"""







  __A  :		Dict       =			tempfile.mkdtemp()
  __A  :		int       =			os.path.join(cls.tmpdir      ,      'default_config.yml'						)
  write_basic_config(save_location=cls.configPath						)
  __A  :		Optional[Any]       =			['accelerate', 'launch', '--config_file', cls.configPath]


 @classmethod
 def  snake_case__   (      cls						):

  """simple docstring"""







  shutil.rmtree(cls.tmpdir						)


 @mock.patch.dict(os.environ      ,      {'WANDB_MODE': 'offline'}						)
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			self.get_auto_remove_tmp_dir()
  __A  :		int       =			F"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

  if is_cuda_and_apex_available():
   testargs.append('--fp16'						)

  run_command(self._launch_args + testargs						)
  __A  :		Any       =			get_results(__lowercase						)
  self.assertGreaterEqual(result['eval_accuracy']      ,      0.7_5						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'epoch_0'						)						)						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'glue_no_trainer'						)						)						)


 @mock.patch.dict(os.environ      ,      {'WANDB_MODE': 'offline'}						)
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			self.get_auto_remove_tmp_dir()
  __A  :		Tuple       =			F"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

  if torch.cuda.device_count() > 1:
   # Skipping because there are not enough batches to train the model + would need a drop_last to work.
   return

  run_command(self._launch_args + testargs						)
  __A  :		Union[str, Any]       =			get_results(__lowercase						)
  self.assertLess(result['perplexity']      ,      100						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'epoch_0'						)						)						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'clm_no_trainer'						)						)						)


 @mock.patch.dict(os.environ      ,      {'WANDB_MODE': 'offline'}						)
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_auto_remove_tmp_dir()
  __A  :		Optional[Any]       =			F"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

  run_command(self._launch_args + testargs						)
  __A  :		List[Any]       =			get_results(__lowercase						)
  self.assertLess(result['perplexity']      ,      42						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'epoch_0'						)						)						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'mlm_no_trainer'						)						)						)


 @mock.patch.dict(os.environ      ,      {'WANDB_MODE': 'offline'}						)
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			7 if get_gpu_count() > 1 else 2

  __A  :		int       =			self.get_auto_remove_tmp_dir()
  __A  :		Any       =			F"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

  run_command(self._launch_args + testargs						)
  __A  :		Union[str, Any]       =			get_results(__lowercase						)
  self.assertGreaterEqual(result['eval_accuracy']      ,      0.7_5						)
  self.assertLess(result['train_loss']      ,      0.5						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'epoch_0'						)						)						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'ner_no_trainer'						)						)						)


 @unittest.skip(reason='Fix me @muellerzr'						)
 @mock.patch.dict(os.environ      ,      {'WANDB_MODE': 'offline'}						)
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.get_auto_remove_tmp_dir()
  __A  :		str       =			F"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

  run_command(self._launch_args + testargs						)
  __A  :		Optional[int]       =			get_results(__lowercase						)
  # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
  self.assertGreaterEqual(result['eval_f1']      ,      28						)
  self.assertGreaterEqual(result['eval_exact']      ,      28						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'epoch_0'						)						)						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'qa_no_trainer'						)						)						)


 @mock.patch.dict(os.environ      ,      {'WANDB_MODE': 'offline'}						)
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Tuple       =			self.get_auto_remove_tmp_dir()
  __A  :		Tuple       =			F"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

  run_command(self._launch_args + testargs						)
  __A  :		str       =			get_results(__lowercase						)
  self.assertGreaterEqual(result['eval_accuracy']      ,      0.8						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'swag_no_trainer'						)						)						)


 @slow
 @mock.patch.dict(os.environ      ,      {'WANDB_MODE': 'offline'}						)
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			self.get_auto_remove_tmp_dir()
  __A  :		Tuple       =			F"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

  run_command(self._launch_args + testargs						)
  __A  :		Optional[Any]       =			get_results(__lowercase						)
  self.assertGreaterEqual(result['eval_rouge1']      ,      10						)
  self.assertGreaterEqual(result['eval_rouge2']      ,      2						)
  self.assertGreaterEqual(result['eval_rougeL']      ,      7						)
  self.assertGreaterEqual(result['eval_rougeLsum']      ,      7						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'epoch_0'						)						)						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'summarization_no_trainer'						)						)						)


 @slow
 @mock.patch.dict(os.environ      ,      {'WANDB_MODE': 'offline'}						)
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			self.get_auto_remove_tmp_dir()
  __A  :		Any       =			F"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

  run_command(self._launch_args + testargs						)
  __A  :		Union[str, Any]       =			get_results(__lowercase						)
  self.assertGreaterEqual(result['eval_bleu']      ,      30						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'epoch_0'						)						)						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'translation_no_trainer'						)						)						)


 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			logging.StreamHandler(sys.stdout						)
  logger.addHandler(__lowercase						)

  __A  :		str       =			self.get_auto_remove_tmp_dir()
  __A  :		Tuple       =			F"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

  run_command(self._launch_args + testargs						)
  __A  :		List[str]       =			get_results(__lowercase						)
  self.assertGreaterEqual(result['eval_overall_accuracy']      ,      0.1_0						)







 @mock.patch.dict(os.environ      ,      {'WANDB_MODE': 'offline'}						)
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_auto_remove_tmp_dir()
  __A  :		Optional[Any]       =			F"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

  if is_cuda_and_apex_available():
   testargs.append('--fp16'						)

  run_command(self._launch_args + testargs						)
  __A  :		Dict       =			get_results(__lowercase						)
  # The base model scores a 25%
  self.assertGreaterEqual(result['eval_accuracy']      ,      0.6						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'step_1'						)						)						)
  self.assertTrue(os.path.exists(os.path.join(__lowercase      ,      'image_classification_no_trainer'						)						)						)










'''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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ :     Tuple								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Tuple=False			):
 __A  :		List[str]       =			[]

 # fmt: off
 # stem:
 rename_keys.append(('cls_token', 'vit.embeddings.cls_token')			)
 rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')			)

 rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')			)
 rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')			)

 # backbone
 rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')			)
 rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')			)
 rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')			)

 for stage_idx in range(len(config.backbone_config.depths			)			):
  for layer_idx in range(config.backbone_config.depths[stage_idx]			):
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")			)

  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")			)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")			)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")			)

 # transformer encoder
 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""")			)

 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"
  __A  :		List[str]       =			[(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'),
      ]			)
 # fmt: on

 return rename_keys


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


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 __A  :		Any       =			['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		Dict       =			dct.pop(UpperCamelCase__			)
 __A  :		Optional[Any]       =			val


def 			_lowercase       (				):
 __A  :		Tuple       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		Optional[Any]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Any=False			):
 __A  :		Optional[Any]       =			BitConfig(
     global_padding='same',				layer_type='bottleneck',				depths=(3, 4, 9),				out_features=['stage3'],				embedding_dynamic_padding=UpperCamelCase__,				)
 __A  :		str       =			ViTHybridConfig(backbone_config=UpperCamelCase__,				image_size=384,				num_labels=1000			)
 __A  :		Union[str, Any]       =			False

 # load original model from timm
 __A  :		List[Any]       =			timm.create_model(UpperCamelCase__,				pretrained=UpperCamelCase__			)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 __A  :		Optional[int]       =			timm_model.state_dict()
 if base_model:
  remove_classification_head_(UpperCamelCase__			)
 __A  :		List[str]       =			create_rename_keys(UpperCamelCase__,				UpperCamelCase__			)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 read_in_q_k_v(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 __A  :		List[str]       =			'huggingface/label-files'
 __A  :		Optional[Any]       =			'imagenet-1k-id2label.json'
 __A  :		Optional[int]       =			json.load(open(hf_hub_download(UpperCamelCase__,				UpperCamelCase__,				repo_type='dataset'			),				'r'			)			)
 __A  :		Optional[int]       =			{int(UpperCamelCase__			): v for k, v in idalabel.items()}
 __A  :		List[Any]       =			idalabel
 __A  :		Tuple       =			{v: k for k, v in idalabel.items()}

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  __A  :		List[Any]       =			ViTHybridModel(UpperCamelCase__			).eval()
 else:
  __A  :		int       =			ViTHybridForImageClassification(UpperCamelCase__			).eval()
 model.load_state_dict(UpperCamelCase__			)

 # create image processor
 __A  :		Tuple       =			create_transform(**resolve_data_config({},				model=UpperCamelCase__			)			)
 __A  :		Union[str, Any]       =			transform.transforms

 __A  :		str       =			{
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

 __A  :		List[str]       =			ViTHybridImageProcessor(
     do_resize=UpperCamelCase__,				size={'shortest_edge': timm_transforms[0].size},				resample=pillow_resamplings[timm_transforms[0].interpolation.value],				do_center_crop=UpperCamelCase__,				crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]},				do_normalize=UpperCamelCase__,				image_mean=timm_transforms[-1].mean.tolist(),				image_std=timm_transforms[-1].std.tolist(),				)

 __A  :		Optional[Any]       =			prepare_img()
 __A  :		Any       =			transform(UpperCamelCase__			).unsqueeze(0			)
 __A  :		Dict       =			processor(UpperCamelCase__,				return_tensors='pt'			).pixel_values

 # verify pixel values
 assert torch.allclose(UpperCamelCase__,				UpperCamelCase__			)

 # verify logits
 with torch.no_grad():
  __A  :		Union[str, Any]       =			model(UpperCamelCase__			)
  __A  :		Dict       =			outputs.logits

 print('Predicted class:',				logits.argmax(-1			).item()			)
 if base_model:
  __A  :		Tuple       =			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:
  __A  :		Any       =			timm_model(UpperCamelCase__			)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCamelCase__,				outputs.logits,				atol=1E-3			)
 print('Looks ok!'			)

 if pytorch_dump_folder_path is not None:
  Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)
  print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""			)
  model.save_pretrained(UpperCamelCase__			)
  print(f"""Saving processor to {pytorch_dump_folder_path}"""			)
  processor.save_pretrained(UpperCamelCase__			)

 if push_to_hub:
  print(f"""Pushing model and processor to the hub {vit_name}"""			)
  model.push_to_hub(f"""ybelkada/{vit_name}"""			)
  processor.push_to_hub(f"""ybelkada/{vit_name}"""			)


if __name__ == "__main__":
 UpperCAmelCase_ :     Optional[Any]								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--vit_name',
     default='vit_base_r50_s16_384',
     type=str,
     help='Name of the hybrid 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.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
 )

 UpperCAmelCase_ :     Union[str, Any]								=      parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






