


'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__A :  Dict    							=				logging.get_logger(__name__)

__A :  List[str]    							=				{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= 'table-transformer'
    lowercase       			= ['past_key_values']
    lowercase       			= {
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }

    def __init__(       self   :  Tuple   ,						lowerCamelCase   :  Union[str, Any]=True   ,						lowerCamelCase   :  Optional[Any]=None   ,						lowerCamelCase   :  Union[str, Any]=3   ,						lowerCamelCase   :  int=1_00   ,						lowerCamelCase   :  str=6   ,						lowerCamelCase   :  List[str]=20_48   ,						lowerCamelCase   :  Optional[Any]=8   ,						lowerCamelCase   :  int=6   ,						lowerCamelCase   :  Tuple=20_48   ,						lowerCamelCase   :  Union[str, Any]=8   ,						lowerCamelCase   :  Tuple=0.0   ,						lowerCamelCase   :  List[str]=0.0   ,						lowerCamelCase   :  str=True   ,						lowerCamelCase   :  Optional[int]="relu"   ,						lowerCamelCase   :  Tuple=2_56   ,						lowerCamelCase   :  Dict=0.1   ,						lowerCamelCase   :  int=0.0   ,						lowerCamelCase   :  List[Any]=0.0   ,						lowerCamelCase   :  str=0.02   ,						lowerCamelCase   :  List[Any]=1.0   ,						lowerCamelCase   :  Dict=False   ,						lowerCamelCase   :  List[str]="sine"   ,						lowerCamelCase   :  int="resnet50"   ,						lowerCamelCase   :  Union[str, Any]=True   ,						lowerCamelCase   :  str=False   ,						lowerCamelCase   :  List[Any]=1   ,						lowerCamelCase   :  List[str]=5   ,						lowerCamelCase   :  Optional[int]=2   ,						lowerCamelCase   :  str=1   ,						lowerCamelCase   :  Optional[int]=1   ,						lowerCamelCase   :  Tuple=5   ,						lowerCamelCase   :  List[str]=2   ,						lowerCamelCase   :  int=0.1   ,						**lowerCamelCase   :  List[str]   ,						)       ->  Union[str, Any]:
      if backbone_config is not None and use_timm_backbone:
        raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""		)

      if not use_timm_backbone:
        if backbone_config is None:
          logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
          lowerCAmelCase_      :				int      =       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]		)
        elif isinstance(lowerCamelCase   ,						lowerCamelCase		):
          lowerCAmelCase_      :				int      =       backbone_config.get("""model_type"""		)
          lowerCAmelCase_      :				Optional[int]      =       CONFIG_MAPPING[backbone_model_type]
          lowerCAmelCase_      :				str      =       config_class.from_dict(lowerCamelCase		)
        # set timm attributes to None
        lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				Tuple      =       None, None, None

      lowerCAmelCase_      :				Optional[Any]      =       use_timm_backbone
      lowerCAmelCase_      :				Optional[int]      =       backbone_config
      lowerCAmelCase_      :				Union[str, Any]      =       num_channels
      lowerCAmelCase_      :				Dict      =       num_queries
      lowerCAmelCase_      :				Dict      =       d_model
      lowerCAmelCase_      :				str      =       encoder_ffn_dim
      lowerCAmelCase_      :				Optional[Any]      =       encoder_layers
      lowerCAmelCase_      :				Optional[int]      =       encoder_attention_heads
      lowerCAmelCase_      :				List[Any]      =       decoder_ffn_dim
      lowerCAmelCase_      :				Union[str, Any]      =       decoder_layers
      lowerCAmelCase_      :				Optional[int]      =       decoder_attention_heads
      lowerCAmelCase_      :				Optional[Any]      =       dropout
      lowerCAmelCase_      :				Dict      =       attention_dropout
      lowerCAmelCase_      :				int      =       activation_dropout
      lowerCAmelCase_      :				List[str]      =       activation_function
      lowerCAmelCase_      :				Union[str, Any]      =       init_std
      lowerCAmelCase_      :				str      =       init_xavier_std
      lowerCAmelCase_      :				Any      =       encoder_layerdrop
      lowerCAmelCase_      :				List[Any]      =       decoder_layerdrop
      lowerCAmelCase_      :				Union[str, Any]      =       encoder_layers
      lowerCAmelCase_      :				Optional[Any]      =       auxiliary_loss
      lowerCAmelCase_      :				str      =       position_embedding_type
      lowerCAmelCase_      :				Tuple      =       backbone
      lowerCAmelCase_      :				List[Any]      =       use_pretrained_backbone
      lowerCAmelCase_      :				Optional[int]      =       dilation
      # Hungarian matcher
      lowerCAmelCase_      :				Optional[Any]      =       class_cost
      lowerCAmelCase_      :				Tuple      =       bbox_cost
      lowerCAmelCase_      :				Any      =       giou_cost
      # Loss coefficients
      lowerCAmelCase_      :				Dict      =       mask_loss_coefficient
      lowerCAmelCase_      :				Optional[Any]      =       dice_loss_coefficient
      lowerCAmelCase_      :				Tuple      =       bbox_loss_coefficient
      lowerCAmelCase_      :				Optional[Any]      =       giou_loss_coefficient
      lowerCAmelCase_      :				Any      =       eos_coefficient
      super().__init__(is_encoder_decoder=lowerCamelCase   ,						**lowerCamelCase		)

    @property
    def __lowercase       (       self   :  Union[str, Any]		)       ->  int:
      return self.encoder_attention_heads





    @property
    def __lowercase       (       self   :  Tuple		)       ->  int:
      return self.d_model







class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= version.parse('1.11')

    @property
    def __lowercase       (       self   :  int		)       ->  Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
              ("""pixel_mask""", {0: """batch"""}),
          ]		)

    @property
    def __lowercase       (       self   :  Dict		)       ->  float:
      return 1E-5





    @property
    def __lowercase       (       self   :  List[Any]		)       ->  int:
      return 12







'''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


__A :  Tuple    							=				"sshleifer/mar_enro_6_3_student"






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
      super().setUp()

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

    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  str		)       ->  str:
      MarianMTModel.from_pretrained(lowerCamelCase		)





    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				str      =       {
          """$MAX_LEN""": 64,
          """$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_      :				Dict      =       (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""		)[1].strip()
      lowerCAmelCase_      :				Optional[int]      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				Optional[Any]      =       self.get_auto_remove_tmp_dir()

      # bash_script = bash_script.replace("--fp16 ", "")
      lowerCAmelCase_      :				Tuple      =       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_      :				Tuple      =       ["""finetune.py"""] + bash_script.split() + args
      with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
        lowerCAmelCase_      :				Optional[Any]      =       argparse.ArgumentParser()
        lowerCAmelCase_      :				Any      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
        lowerCAmelCase_      :				List[str]      =       SummarizationModule.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
        lowerCAmelCase_      :				Tuple      =       parser.parse_args()
        lowerCAmelCase_      :				Dict      =       main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				int      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Optional[Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Tuple      =       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}']   ,						lowerCamelCase		)

      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_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Union[str, Any]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				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:
        lowerCAmelCase_      :				List[str]      =       {os.path.basename(lowerCamelCase		) 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        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @timeout_decorator.timeout(6_00		)
    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  Optional[Any]		)       ->  Dict:
      lowerCAmelCase_      :				List[str]      =       F'{self.test_file_dir_str}/test_data/wmt_en_ro'
      lowerCAmelCase_      :				Dict      =       {
          """--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_      :				int      =       (
          (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""		)[1].strip()
      )
      lowerCAmelCase_      :				str      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      lowerCAmelCase_      :				Tuple      =       bash_script.replace("""--fp16 """   ,						""" """		)

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

        lowerCAmelCase_      :				str      =       distill_main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				Union[str, Any]      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Union[str, 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}']   ,						lowerCamelCase		)

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



