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


snake_case__				= 'sshleifer/mar_enro_6_3_student'



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """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 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           MarianMTModel.from_pretrained(__A						)





     @slow
     @require_torch_gpu
     def 		UpperCamelCase_						(					self						) ->				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'\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			    =				['''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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     @timeout_decorator.timeout(6_00						)
     @slow
     @require_torch_gpu
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				F'{self.test_file_dir_str}/test_data/wmt_en_ro'
           _lowerCamelCase			    =				{
               '''--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			    =				(
               (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
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__				= {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= ['WhisperTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'WhisperForConditionalGeneration',
         'WhisperModel',
         'WhisperPreTrainedModel',
         'WhisperForAudioClassification',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFWhisperForConditionalGeneration',
         'TFWhisperModel',
         'TFWhisperPreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'FlaxWhisperForConditionalGeneration',
         'FlaxWhisperModel',
         'FlaxWhisperPreTrainedModel',
         'FlaxWhisperForAudioClassification',
     ]


if TYPE_CHECKING:
     from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
     from .feature_extraction_whisper import WhisperFeatureExtractor
     from .processing_whisper import WhisperProcessor
     from .tokenization_whisper import WhisperTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_whisper_fast import WhisperTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_whisper import (
              WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
              WhisperForAudioClassification,
              WhisperForConditionalGeneration,
              WhisperModel,
              WhisperPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_whisper import (
              TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFWhisperForConditionalGeneration,
              TFWhisperModel,
              TFWhisperPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_whisper import (
              FlaxWhisperForAudioClassification,
              FlaxWhisperForConditionalGeneration,
              FlaxWhisperModel,
              FlaxWhisperPreTrainedModel,
          )

else:
     import sys

     snake_case__				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)