
"""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


__UpperCAmelCase       =  'sshleifer/mar_enro_6_3_student'




class 			_SCREAMING_SNAKE_CASE					(			A__							):






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

						lowerCAmelCase_    :int			     =  cached_path(
						    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""       ,   extract_compressed_file=__A       ,   )
						lowerCAmelCase_    :str			     =  f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""






			@slow
			@require_torch_gpu
			def  __lowerCAmelCase (					self       )			->     Any:
						MarianMTModel.from_pretrained(__A       )






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

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

						# Check metrics
						lowerCAmelCase_    :Tuple			     =  load_json(model.metrics_save_path       )
						lowerCAmelCase_    :Union[str, Any]			     =  metrics["""val"""][0]
						lowerCAmelCase_    :Any			     =  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"""]       ,   0.0_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
						lowerCAmelCase_    :Optional[Any]			     =  os.listdir(__A       )
						lowerCAmelCase_    :Any			     =  [x for x in contents if x.endswith(""".ckpt"""       )][0]
						lowerCAmelCase_    :int			     =  os.path.join(args.output_dir       ,   __A       )
						lowerCAmelCase_    :str			     =  torch.load(__A       ,   map_location="""cpu"""       )
						lowerCAmelCase_    :Union[str, 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:
									lowerCAmelCase_    :int			     =  {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 			_SCREAMING_SNAKE_CASE					(			A__							):






			@timeout_decorator.timeout(600       )
			@slow
			@require_torch_gpu
			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :Dict			     =  f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
						lowerCAmelCase_    :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
						lowerCAmelCase_    :Dict			     =  (
						    (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_    :Optional[Any]			     =  bash_script.replace("""--fp16 """       ,   """ """       )

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

									lowerCAmelCase_    :Optional[Any]			     =  distill_main(__A       )

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

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







import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow



@is_pipeline_test
@require_torch
class   snake_case_       (		unittest.TestCase       ):






   @require_torch
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						List[Any]:
       lowercase__ :   Union[str, Any]    					=				pipeline(
           task="zero-shot-audio-classification"	,						model="hf-internal-testing/tiny-clap-htsat-unfused"  )
       lowercase__ :   List[str]    					=				load_dataset("ashraq/esc50"  )
       lowercase__ :   List[Any]    					=				dataset["train"]["audio"][-1]["array"]
       lowercase__ :   Dict    					=				audio_classifier(lowercase_	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]  )
       self.assertEqual(
           nested_simplify(lowercase_  )	,						[{"score": 0.5_01, "label": "Sound of a dog"}, {"score": 0.4_99, "label": "Sound of vaccum cleaner"}]	,						)






   @unittest.skip("No models are available in TF"  )
   def       __UpperCamelCase     (      self				:     str  )					->						Optional[int]:
       pass






   @slow
   @require_torch
   def       __UpperCamelCase     (      self				:     List[str]  )					->						int:
       lowercase__ :   Tuple    					=				pipeline(
           task="zero-shot-audio-classification"	,						model="laion/clap-htsat-unfused"	,						)
       # This is an audio of a dog
       lowercase__ :   Union[str, Any]    					=				load_dataset("ashraq/esc50"  )
       lowercase__ :   Tuple    					=				dataset["train"]["audio"][-1]["array"]
       lowercase__ :   List[Any]    					=				audio_classifier(lowercase_	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]  )

       self.assertEqual(
           nested_simplify(lowercase_  )	,						[
               {"score": 0.9_99, "label": "Sound of a dog"},
               {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
           ]	,						)

       lowercase__ :   int    					=				audio_classifier([audio] * 5	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]  )
       self.assertEqual(
           nested_simplify(lowercase_  )	,						[
               [
                   {"score": 0.9_99, "label": "Sound of a dog"},
                   {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
               ],
           ]
           * 5	,						)
       lowercase__ :   Tuple    					=				audio_classifier(
           [audio] * 5	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]	,						batch_size=5  )
       self.assertEqual(
           nested_simplify(lowercase_  )	,						[
               [
                   {"score": 0.9_99, "label": "Sound of a dog"},
                   {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
               ],
           ]
           * 5	,						)







   @unittest.skip("No models are available in TF"  )
   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Dict:
       pass



