




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


lowerCamelCase    :		int			   =       logging.get_logger(__name__)

lowerCamelCase    :		Any			   =       {
    "sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''poolformer'''

 def __init__(    self :  str						,							A_ :  List[Any]=3						,							A_ :  Optional[int]=16						,							A_ :  Dict=16						,							A_ :  Union[str, Any]=3						,							A_ :  int=4.0						,							A_ :  Any=[2, 2, 6, 2]						,							A_ :  str=[64, 128, 320, 512]						,							A_ :  List[Any]=[7, 3, 3, 3]						,							A_ :  str=[4, 2, 2, 2]						,							A_ :  Optional[Any]=[2, 1, 1, 1]						,							A_ :  int=4						,							A_ :  Union[str, Any]=0.0						,							A_ :  Dict="gelu"						,							A_ :  Optional[int]=True						,							A_ :  int=1E-5						,							A_ :  Dict=0.02						,							**A_ :  Tuple						,							)       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   num_channels
       lowerCamelCase_							=   patch_size
       lowerCamelCase_							=   stride
       lowerCamelCase_							=   padding
       lowerCamelCase_							=   pool_size
       lowerCamelCase_							=   hidden_sizes
       lowerCamelCase_							=   mlp_ratio
       lowerCamelCase_							=   depths
       lowerCamelCase_							=   patch_sizes
       lowerCamelCase_							=   strides
       lowerCamelCase_							=   num_encoder_blocks
       lowerCamelCase_							=   drop_path_rate
       lowerCamelCase_							=   hidden_act
       lowerCamelCase_							=   use_layer_scale
       lowerCamelCase_							=   layer_scale_init_value
       lowerCamelCase_							=   initializer_range
       super().__init__(**A_  )







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						version.parse('''1.11'''       )

 @property
 def     a__			(    self :  Tuple  )       ->     Mapping[str, Mapping[int, str]]:

       """simple docstring"""






       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]  )





 @property
 def     a__			(    self :  Any  )       ->     float:

       """simple docstring"""






       return 2E-3









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    :		Tuple			   =       "sshleifer/mar_enro_6_3_student"







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 def     a__			(    self :  Union[str, Any]  )       ->     str:

       """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=A_						,							)
       lowerCamelCase_							=   f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""

 @slow
 @require_torch_gpu
 def     a__			(    self :  List[str]  )       ->     int:

       """simple docstring"""






       MarianMTModel.from_pretrained(A_  )





 @slow
 @require_torch_gpu
 def     a__			(    self :  List[str]  )       ->     Union[str, Any]:

       """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(A_						,							str(A_  )  )
       lowerCamelCase_							=   self.get_auto_remove_tmp_dir()

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

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

       # 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}"""]						,							A_  )

       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(A_  )
       lowerCamelCase_							=   [x for x in contents if x.endswith('.ckpt'  )][0]
       lowerCamelCase_							=   os.path.join(args.output_dir						,							A_  )
       lowerCamelCase_							=   torch.load(A_						,							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(A_  ) 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   A(						UpperCamelCase       ):



 '''simple docstring'''


 @timeout_decorator.timeout(600  )
 @slow
 @require_torch_gpu
 def     a__			(    self :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
       lowerCamelCase_							=   {
           '--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
       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(A_						,							str(A_  )  )
       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(A_						,							'argv'						,							A_  ):
             lowerCamelCase_							=   argparse.ArgumentParser()
             lowerCamelCase_							=   pl.Trainer.add_argparse_args(A_  )
             lowerCamelCase_							=   SummarizationDistiller.add_model_specific_args(A_						,							os.getcwd()  )
             lowerCamelCase_							=   parser.parse_args()
             # assert args.gpus == gpus THIS BREAKS for multi_gpu

             lowerCamelCase_							=   distill_main(A_  )

       # 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}"""]						,							A_  )

       # check lightning ckpt can be loaded and has a reasonable statedict
       lowerCamelCase_							=   os.listdir(A_  )
       lowerCamelCase_							=   [x for x in contents if x.endswith('.ckpt'  )][0]
       lowerCamelCase_							=   os.path.join(args.output_dir						,							A_  )
       lowerCamelCase_							=   torch.load(A_						,							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(A_  ) 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



