'''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  :Dict  				=    """sshleifer/mar_enro_6_3_student"""






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







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

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






    @slow
    @require_torch_gpu
    def       _UpperCamelCase    (       self )							-> Any:
         MarianMTModel.from_pretrained(lowercase__ )






    @slow
    @require_torch_gpu
    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			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
         SCREAMING_SNAKE_CASE					:			str    	=       (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       bash_script.replace('\\\n'    ,							'' ).strip().replace('"$@"'    ,							'' )
         for k, v in env_vars_to_replace.items():
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       bash_script.replace(lowercase__    ,							str(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			int    	=       self.get_auto_remove_tmp_dir()

         # bash_script = bash_script.replace("--fp16 ", "")
         SCREAMING_SNAKE_CASE					:			Tuple    	=       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

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

         # Check metrics
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       load_json(model.metrics_save_path )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       metrics['val'][0]
         SCREAMING_SNAKE_CASE					:			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']    ,							0.0_1 )
         # 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
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       os.listdir(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [x for x in contents if x.endswith('.ckpt' )][0]
         SCREAMING_SNAKE_CASE					:			Dict    	=       os.path.join(args.output_dir    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.load(lowercase__    ,							map_location='cpu' )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       '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:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       {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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    @timeout_decorator.timeout(600 )
    @slow
    @require_torch_gpu
    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       F"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
             '--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
         SCREAMING_SNAKE_CASE					:			int    	=       (
             (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
         )
         SCREAMING_SNAKE_CASE					:			Any    	=       bash_script.replace('\\\n'    ,							'' ).strip().replace('"$@"'    ,							'' )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       bash_script.replace('--fp16 '    ,							' ' )

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

              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       distill_main(lowercase__ )

         # Check metrics
         SCREAMING_SNAKE_CASE					:			str    	=       load_json(model.metrics_save_path )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       metrics['val'][0]
         SCREAMING_SNAKE_CASE					:			int    	=       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"] >= 0.0_1

         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
         SCREAMING_SNAKE_CASE					:			Any    	=       os.listdir(lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       [x for x in contents if x.endswith('.ckpt' )][0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       os.path.join(args.output_dir    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.load(lowercase__    ,							map_location='cpu' )
         SCREAMING_SNAKE_CASE					:			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:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       {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




'''simple docstring'''





import warnings
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  :str  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Optional[int]  				=    {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						"segformer"






    def __init__(       self    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=[2, 2, 2, 2]    ,							lowercase__=[8, 4, 2, 1]    ,							lowercase__=[32, 64, 160, 256]    ,							lowercase__=[7, 3, 3, 3]    ,							lowercase__=[4, 2, 2, 2]    ,							lowercase__=[1, 2, 5, 8]    ,							lowercase__=[4, 4, 4, 4]    ,							lowercase__="gelu"    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.1    ,							lowercase__=0.0_2    ,							lowercase__=0.1    ,							lowercase__=1E-6    ,							lowercase__=256    ,							lowercase__=255    ,							**lowercase__    ,							)							-> Union[str, Any]:
         super().__init__(**lowercase__ )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                  ' removed, as the behaviour will default to that of reshape_last_stage = True.'    ,							lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			int    	=       num_channels
         SCREAMING_SNAKE_CASE					:			int    	=       num_encoder_blocks
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       depths
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sr_ratios
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_sizes
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       patch_sizes
         SCREAMING_SNAKE_CASE					:			int    	=       strides
         SCREAMING_SNAKE_CASE					:			Tuple    	=       mlp_ratios
         SCREAMING_SNAKE_CASE					:			List[str]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       classifier_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			int    	=       layer_norm_eps
         SCREAMING_SNAKE_CASE					:			Any    	=       decoder_hidden_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       kwargs.get('reshape_last_stage'    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       semantic_loss_ignore_index






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Tuple							=						version.parse("1.11" )






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






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return 12



