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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__		:       Union[str, Any]       						=       logging.get_logger(__name__)







def      _a					(   lowercase__	:      List[str]  ,	lowercase__	:      List[str]=False			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					int      =		[]
     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'''deit.encoder.layer.{i}.layernorm_before.weight''')			)
          rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')			)
          rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')			)
          rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')			)
          rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')			)
          rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')			)
          rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')			)
          rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')			)
          rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')			)
          rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')			)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('cls_token', 'deit.embeddings.cls_token'),
             ('dist_token', 'deit.embeddings.distillation_token'),
             ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
             ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
             ('pos_embed', 'deit.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 "deit" from all keys that start with "deit"
          SCREAMING_SNAKE_CASE__					:					Tuple      =		[(pair[0], pair[1][4:]) if pair[1].startswith('deit'			) else pair for pair in rename_keys]
     else:
          # layernorm + classification heads
          rename_keys.extend(
              [
                  ('norm.weight', 'deit.layernorm.weight'),
                  ('norm.bias', 'deit.layernorm.bias'),
                  ('head.weight', 'cls_classifier.weight'),
                  ('head.bias', 'cls_classifier.bias'),
                  ('head_dist.weight', 'distillation_classifier.weight'),
                  ('head_dist.bias', 'distillation_classifier.bias'),
              ]			)

     return rename_keys







def      _a					(   lowercase__	:      Optional[Any]  ,	lowercase__	:      Union[str, Any]  ,	lowercase__	:      Optional[int]=False			):







     '''simple docstring'''


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







def      _a					(   lowercase__	:      List[Any]  ,	lowercase__	:      str  ,	lowercase__	:      Optional[int]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					int      =		dct.pop(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					str      =		val







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE__					:					Any      =		Image.open(requests.get(lowercase__  ,	stream=lowercase__			).raw			)
     return im







@torch.no_grad()
def      _a					(   lowercase__	:      Any  ,	lowercase__	:      Union[str, Any]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Tuple      =		DeiTConfig()
     # all deit models have fine-tuned heads
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		False
     # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		10_00
     SCREAMING_SNAKE_CASE__					:					str      =		'huggingface/label-files'
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		'imagenet-1k-id2label.json'
     SCREAMING_SNAKE_CASE__					:					Dict      =		json.load(open(hf_hub_download(lowercase__  ,	lowercase__  ,	repo_type='dataset'			)  ,	'r'			)			)
     SCREAMING_SNAKE_CASE__					:					Any      =		{int(lowercase__			): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__					:					int      =		idalabel
     SCREAMING_SNAKE_CASE__					:					Any      =		{v: k for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__					:					Any      =		int(deit_name[-6:-4]			)
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		int(deit_name[-3:]			)
     # size of the architecture
     if deit_name[9:].startswith('tiny'			):
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		1_92
          SCREAMING_SNAKE_CASE__					:					str      =		7_68
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		12
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		3
     elif deit_name[9:].startswith('small'			):
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		3_84
          SCREAMING_SNAKE_CASE__					:					Dict      =		15_36
          SCREAMING_SNAKE_CASE__					:					Dict      =		12
          SCREAMING_SNAKE_CASE__					:					List[str]      =		6
     if deit_name[9:].startswith('base'			):
          pass
     elif deit_name[4:].startswith('large'			):
          SCREAMING_SNAKE_CASE__					:					List[str]      =		10_24
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		40_96
          SCREAMING_SNAKE_CASE__					:					List[str]      =		24
          SCREAMING_SNAKE_CASE__					:					str      =		16

     # load original model from timm
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		timm.create_model(lowercase__  ,	pretrained=lowercase__			)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		timm_model.state_dict()
     SCREAMING_SNAKE_CASE__					:					Any      =		create_rename_keys(lowercase__  ,	lowercase__			)
     for src, dest in rename_keys:
          rename_key(lowercase__  ,	lowercase__  ,	lowercase__			)
     read_in_q_k_v(lowercase__  ,	lowercase__  ,	lowercase__			)

     # load HuggingFace model
     SCREAMING_SNAKE_CASE__					:					str      =		DeiTForImageClassificationWithTeacher(lowercase__			).eval()
     model.load_state_dict(lowercase__			)

     # Check outputs on an image, prepared by DeiTImageProcessor
     SCREAMING_SNAKE_CASE__					:					Tuple      =		int(
         (2_56 / 2_24) * config.image_size			)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		DeiTImageProcessor(size=lowercase__  ,	crop_size=config.image_size			)
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		image_processor(images=prepare_img()  ,	return_tensors='pt'			)
     SCREAMING_SNAKE_CASE__					:					Any      =		encoding['pixel_values']
     SCREAMING_SNAKE_CASE__					:					Tuple      =		model(lowercase__			)

     SCREAMING_SNAKE_CASE__					:					List[str]      =		timm_model(lowercase__			)
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(lowercase__  ,	outputs.logits  ,	atol=1E-3			)

     Path(lowercase__			).mkdir(exist_ok=lowercase__			)
     print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''			)
     model.save_pretrained(lowercase__			)
     print(f'''Saving image processor to {pytorch_dump_folder_path}'''			)
     image_processor.save_pretrained(lowercase__			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:       int       						=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--deit_name",
          default="vit_deit_base_distilled_patch16_224",
          type=str,
          help="Name of the DeiT 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."
      )

      SCREAMING_SNAKE_CASE__		:       Dict       						=       parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









'''simple docstring'''


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


lowerCAmelCase_      :  List[str]				     =  'sshleifer/mar_enro_6_3_student'







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           super().setUp()

           a_     :							Optional[int]     						=  cached_path(
               """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""						,						extract_compressed_file=lowercase__						,						)
           a_     :							List[Any]     						=  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           MarianMTModel.from_pretrained(lowercase__       )






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  {
               """$MAX_LEN""": 64,
               """$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
           a_     :							Optional[int]     						=  (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""       )[1].strip()
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           for k, v in env_vars_to_replace.items():
                a_     :							Optional[Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  self.get_auto_remove_tmp_dir()

           # bash_script = bash_script.replace("--fp16 ", "")
           a_     :							List[str]     						=  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

           a_     :							Dict     						=  ["""finetune.py"""] + bash_script.split() + args
           with patch.object(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							List[Any]     						=  argparse.ArgumentParser()
                a_     :							int     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							Dict     						=  SummarizationModule.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[str]     						=  parser.parse_args()
                a_     :							List[Any]     						=  main(lowercase__       )

           # Check metrics
           a_     :							List[str]     						=  load_json(model.metrics_save_path       )
           a_     :							Any     						=  metrics["""val"""][0]
           a_     :							int     						=  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}"]						,						lowercase__       )

           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
           a_     :							Dict     						=  os.listdir(lowercase__       )
           a_     :							Union[str, Any]     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Dict     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							Tuple     						=  """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:
                a_     :							int     						=  {os.path.basename(lowercase__       ) 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    (						snake_case_				):






      @timeout_decorator.timeout(600       )
      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							int     						=  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
           a_     :							Dict     						=  {
               """--fp16_opt_level=O1""": """""",
               """$MAX_LEN""": 128,
               """$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
           a_     :							List[str]     						=  (
               (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""       )[1].strip()
           )
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           a_     :							Tuple     						=  bash_script.replace("""--fp16 """						,						""" """       )

           for k, v in env_vars_to_replace.items():
                a_     :							Union[str, Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							List[str]     						=  self.get_auto_remove_tmp_dir()
           a_     :							List[str]     						=  bash_script.replace("""--fp16"""						,						""""""       )
           a_     :							str     						=  6
           a_     :							Union[str, Any]     						=  (
               ["""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(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							Dict     						=  argparse.ArgumentParser()
                a_     :							Tuple     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							int     						=  SummarizationDistiller.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[Any]     						=  parser.parse_args()
                # assert args.gpus == gpus THIS BREAKS for multi_gpu

                a_     :							Union[str, Any]     						=  distill_main(lowercase__       )

           # Check metrics
           a_     :							Union[str, Any]     						=  load_json(model.metrics_save_path       )
           a_     :							List[str]     						=  metrics["""val"""][0]
           a_     :							Optional[Any]     						=  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}"]						,						lowercase__       )

           # check lightning ckpt can be loaded and has a reasonable statedict
           a_     :							Optional[int]     						=  os.listdir(lowercase__       )
           a_     :							Dict     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Optional[Any]     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							List[str]     						=  """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:
                a_     :							int     						=  {os.path.basename(lowercase__       ) 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
