






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()
__snake_case      :      Tuple             =       logging.get_logger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Union[str, Any]=False						)					->				Union[str, Any]:

						"""simple docstring"""



						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 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Dict=False						)					->				Union[str, Any]:

						"""simple docstring"""



						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 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]						)					->				Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__        =  ['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase_      ,    UpperCamelCase_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   int						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  dct.pop(UpperCamelCase_						)
						lowerCAmelCase__        =  val







def 		_UpperCamelCase				(      )					->				Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__        =  'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase__        =  Image.open(requests.get(UpperCamelCase_      ,    stream=UpperCamelCase_						).raw						)
						return im







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Any						)					->				List[str]:

						"""simple docstring"""



						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__":
					__snake_case      :      Tuple             =       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."""
					)

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













import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__snake_case      :      Tuple             =       """sshleifer/mar_enro_6_3_student"""


class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def  UpperCamelCase__ (		self				):






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

												lowerCAmelCase__        =  cached_path(
												    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'       ,  extract_compressed_file=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												MarianMTModel.from_pretrained(_UpperCamelCase				)







						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  {
												    '$MAX_LEN': 64,
												    '$BS': 64,
												    '$GAS': 1,
												    '$ENRO_DIR': self.data_dir,
												    'facebook/mbart-large-cc25': MARIAN_MODEL,
												    # "val_check_interval=0.25": "val_check_interval=1.0",
												    '--learning_rate=3e-5': '--learning_rate 3e-4',
												    '--num_train_epochs 6': '--num_train_epochs 1',
												}

												# Clean up bash script
												lowerCAmelCase__        =  (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()

												# bash_script = bash_script.replace("--fp16 ", "")
												lowerCAmelCase__        =  F"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
												# XXX: args.gpus > 1 : handle multi_gpu in the future

												lowerCAmelCase__        =  ['finetune.py'] + bash_script.split() + args
												with patch.object(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationModule.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		lowerCAmelCase__        =  main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  metrics['val'][-1]
												self.assertEqual(len(metrics['val']				)       ,  (args.max_epochs / args.val_check_interval)				)
												assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]       ,  _UpperCamelCase				)

												self.assertGreater(last_step_stats['val_avg_gen_time']       ,  0.01				)
												# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
												self.assertLessEqual(last_step_stats['val_avg_gen_time']       ,  1.0				)

												# test learning requirements:

												# 1. BLEU improves over the course of training by more than 2 pts
												self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu']       ,  2				)

												# 2. BLEU finishes above 17
												self.assertGreater(last_step_stats['val_avg_bleu']       ,  17				)

												# 3. test BLEU and val BLEU within ~1.1 pt.
												self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']				)       ,  1.1				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics['test']				) == 1




class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						@timeout_decorator.timeout(6_00				)
						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
												lowerCAmelCase__        =  {
												    '--fp16_opt_level=O1': '',
												    '$MAX_LEN': 1_28,
												    '$BS': 16,
												    '$GAS': 1,
												    '$ENRO_DIR': data_dir,
												    '$m': 'sshleifer/student_marian_en_ro_6_1',
												    'val_check_interval=0.25': 'val_check_interval=1.0',
												}

												# Clean up bash script
												lowerCAmelCase__        =  (
												    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
												)
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												lowerCAmelCase__        =  bash_script.replace('--fp16 '       ,  ' '				)

												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()
												lowerCAmelCase__        =  bash_script.replace('--fp16'       ,  ''				)
												lowerCAmelCase__        =  6
												lowerCAmelCase__        =  (
												    ['distillation.py']
												    + bash_script.split()
												    + [
												        F"--output_dir={output_dir}",
												        '--gpus=1',
												        '--learning_rate=1e-3',
												        F"--num_train_epochs={epochs}",
												        '--warmup_steps=10',
												        '--val_check_interval=1.0',
												        '--do_predict',
												    ]
												)
												with patch.object(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationDistiller.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		lowerCAmelCase__        =  distill_main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  metrics['val'][-1]
												assert len(metrics['val']				) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

												assert last_step_stats["val_avg_gen_time"] >= 0.01

												assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
												assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
												assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]       ,  _UpperCamelCase				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics['test']				) == 1





