"""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							 =     "sshleifer/mar_enro_6_3_student"
class 			UpperCamelCase__(	__A       ):





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

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





						@slow
						@require_torch_gpu
						def  snake_case__		(				self   )    -> Optional[Any]:
													MarianMTModel.from_pretrained(__UpperCAmelCase   )





						@slow
						@require_torch_gpu
						def  snake_case__		(				self   )    -> Dict:
													A__		   =							{
													    '$MAX_LEN': 64,
													    '$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__		   =							(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'   )[1].strip()
													A__		   =							bash_script.replace('\\\n'						,''   ).strip().replace('"$@"'						,''   )
													for k, v in env_vars_to_replace.items():
																				A__		   =							bash_script.replace(__UpperCAmelCase						,str(__UpperCAmelCase   )   )
													A__		   =							self.get_auto_remove_tmp_dir()

													# bash_script = bash_script.replace("--fp16 ", "")
													A__		   =							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

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

													# Check metrics
													A__		   =							load_json(model.metrics_save_path   )
													A__		   =							metrics['val'][0]
													A__		   =							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']						,0.0_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
													A__		   =							os.listdir(__UpperCAmelCase   )
													A__		   =							[x for x in contents if x.endswith('.ckpt'   )][0]
													A__		   =							os.path.join(args.output_dir						,__UpperCAmelCase   )
													A__		   =							torch.load(__UpperCAmelCase						,map_location='cpu'   )
													A__		   =							'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__		   =							{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 			UpperCamelCase__(	__A       ):





						@timeout_decorator.timeout(6_00   )
						@slow
						@require_torch_gpu
						def  snake_case__		(				self   )    -> str:
													A__		   =							f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
													A__		   =							{
													    '--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
													A__		   =							(
													    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'   )[1].strip()
													)
													A__		   =							bash_script.replace('\\\n'						,''   ).strip().replace('"$@"'						,''   )
													A__		   =							bash_script.replace('--fp16 '						,' '   )

													for k, v in env_vars_to_replace.items():
																				A__		   =							bash_script.replace(__UpperCAmelCase						,str(__UpperCAmelCase   )   )
													A__		   =							self.get_auto_remove_tmp_dir()
													A__		   =							bash_script.replace('--fp16'						,''   )
													A__		   =							6
													A__		   =							(
													    ['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   ):
																				A__		   =							argparse.ArgumentParser()
																				A__		   =							pl.Trainer.add_argparse_args(__UpperCAmelCase   )
																				A__		   =							SummarizationDistiller.add_model_specific_args(__UpperCAmelCase						,os.getcwd()   )
																				A__		   =							parser.parse_args()
																				# assert args.gpus == gpus THIS BREAKS for multi_gpu

																				A__		   =							distill_main(__UpperCAmelCase   )

													# Check metrics
													A__		   =							load_json(model.metrics_save_path   )
													A__		   =							metrics['val'][0]
													A__		   =							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}''']						,__UpperCAmelCase   )

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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["ChineseCLIPFeatureExtractor"]
				__lowerCamelCase							 =     ["ChineseCLIPImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ChineseCLIPModel",
				    "ChineseCLIPPreTrainedModel",
				    "ChineseCLIPTextModel",
				    "ChineseCLIPVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_chinese_clip import (
				    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ChineseCLIPConfig,
				    ChineseCLIPOnnxConfig,
				    ChineseCLIPTextConfig,
				    ChineseCLIPVisionConfig,
				)
				from .processing_chinese_clip import ChineseCLIPProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_chinese_clip import (
								    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ChineseCLIPModel,
								    ChineseCLIPPreTrainedModel,
								    ChineseCLIPTextModel,
								    ChineseCLIPVisionModel,
								)

else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





