





"""simple docstring"""





import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							Tuple									=		CLIPTokenizer
				__snake_case :							Any									=		CLIPTokenizerFast
				__snake_case :							Dict									=		True
				__snake_case :							str									=		{}
				__snake_case :							Tuple									=		False




				def        __lowercase	(  self	:Optional[Any]      ):
											super().setUp()

											# fmt: off
											__lowerCamelCase   :   Optional[int]       =['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
											# fmt: on
											__lowerCamelCase   :   int       =dict(zip(__lowercase    ,     range(len(__lowercase      )      )      )      )
											__lowerCamelCase   :   List[str]       =['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
											__lowerCamelCase   :   Any       ={'''unk_token''': '''<unk>'''}

											__lowerCamelCase   :   Union[str, Any]       =os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['''vocab_file''']      )
											__lowerCamelCase   :   Dict       =os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['''merges_file''']      )
											with open(self.vocab_file    ,     '''w'''    ,     encoding='''utf-8'''      ) as fp:
																		fp.write(json.dumps(__lowercase      ) + '''\n'''      )
											with open(self.merges_file    ,     '''w'''    ,     encoding='''utf-8'''      ) as fp:
																		fp.write('''\n'''.join(__lowercase      )      )




				def        __lowercase	(  self	:Optional[int]    ,     **__lowercase	:Optional[Any]      ):
											kwargs.update(self.special_tokens_map      )
											return CLIPTokenizer.from_pretrained(self.tmpdirname    ,     **__lowercase      )




				def        __lowercase	(  self	:int    ,     **__lowercase	:Union[str, Any]      ):
											kwargs.update(self.special_tokens_map      )
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,     **__lowercase      )




				def        __lowercase	(  self	:Optional[Any]    ,     __lowercase	:List[Any]      ):
											__lowerCamelCase   :   List[Any]       ='''lower newer'''
											__lowerCamelCase   :   Optional[int]       ='''lower newer'''
											return input_text, output_text




				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   Optional[Any]       =CLIPTokenizer(self.vocab_file    ,     self.merges_file    ,     **self.special_tokens_map      )
											__lowerCamelCase   :   Union[str, Any]       ='''lower newer'''
											__lowerCamelCase   :   int       =['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
											__lowerCamelCase   :   int       =tokenizer.tokenize(__lowercase      )
											self.assertListEqual(__lowercase    ,     __lowercase      )

											__lowerCamelCase   :   List[Any]       =tokens + [tokenizer.unk_token]
											__lowerCamelCase   :   Tuple       =[10, 2, 16, 9, 3, 2, 16, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase      )    ,     __lowercase      )




				@require_ftfy
				def        __lowercase	(  self	:Dict      ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																									__lowerCamelCase   :   Union[str, Any]       =self.tokenizer_class.from_pretrained(__lowercase    ,     **__lowercase      )
																									__lowerCamelCase   :   Tuple       =self.rust_tokenizer_class.from_pretrained(__lowercase    ,     **__lowercase      )

																									__lowerCamelCase   :   Optional[int]       ='''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
																									__lowerCamelCase   :   List[Any]       =tokenizer_s.tokenize(__lowercase      )
																									__lowerCamelCase   :   Union[str, Any]       =tokenizer_r.tokenize(__lowercase      )

																									self.assertListEqual(__lowercase    ,     __lowercase      )

																									# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																									# with Tilde) encoded in 2 different ways
																									__lowerCamelCase   :   int       ='''xa\u0303y''' + ''' ''' + '''x\xe3y'''
																									__lowerCamelCase   :   Dict       =tokenizer_s.tokenize(__lowercase      )
																									__lowerCamelCase   :   Optional[Any]       =tokenizer_r.tokenize(__lowercase      )

																									self.assertListEqual(__lowercase    ,     __lowercase      )

																									# Test that the tokenization is identical on unicode of space type
																									__lowerCamelCase   :   Optional[int]       =[
																									    '''\u0009''',  # (horizontal tab, '\t')
																									    '''\u000B''',  # (vertical tab)
																									    '''\u000C''',  # (form feed)
																									    '''\u0020''',  # (space, ' ')
																									    '''\u200E''',  # (left-to-right mark):w
																									    '''\u200F''',  # (right-to-left mark)
																									]
																									for unicode_seq in spaces_unicodes:
																																__lowerCamelCase   :   Optional[Any]       =tokenizer_s.tokenize(__lowercase      )
																																__lowerCamelCase   :   Union[str, Any]       =tokenizer_r.tokenize(__lowercase      )

																																self.assertListEqual(__lowercase    ,     __lowercase      )

																									# Test that the tokenization is identical on unicode of line break type
																									__lowerCamelCase   :   int       =[
																									    '''\u000A''',  # (line feed, '\n')
																									    '''\r\n''',  # (carriage return and line feed, '\r\n')
																									    '''\u000D''',  # (carriage return, '\r')
																									    '''\r''',  # (carriage return, '\r')
																									    '''\u000D''',  # (carriage return, '\r')
																									    '''\u2028''',  # (line separator)
																									    '''\u2029''',  # (paragraph separator)
																									    # "\u0085", # (next line)
																									]

																									# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																									# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																									# space (and thus into an empty list).

																									for unicode_seq in line_break_unicodes:
																																__lowerCamelCase   :   Any       =tokenizer_s.tokenize(__lowercase      )
																																__lowerCamelCase   :   List[Any]       =tokenizer_r.tokenize(__lowercase      )

																																self.assertListEqual(__lowercase    ,     __lowercase      )




				def        __lowercase	(  self	:Union[str, Any]      ):
											# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																									__lowerCamelCase   :   str       ='''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																									__lowerCamelCase   :   Dict       =f'{text_of_1_token} {text_of_1_token}'

																									__lowerCamelCase   :   Tuple       =self.rust_tokenizer_class.from_pretrained(
																									    __lowercase    ,     use_fast=__lowercase    ,     )
																									__lowerCamelCase   :   int       =tokenizer_r(__lowercase    ,     return_offsets_mapping=__lowercase    ,     add_special_tokens=__lowercase      )
																									self.assertEqual(encoding.offset_mapping[0]    ,     (0, len(__lowercase      ))      )
																									self.assertEqual(
																									    encoding.offset_mapping[1]    ,     (len(__lowercase      ) + 1, len(__lowercase      ) + 1 + len(__lowercase      ))    ,     )

																									__lowerCamelCase   :   List[Any]       =f' {text}'

																									__lowerCamelCase   :   List[str]       =self.rust_tokenizer_class.from_pretrained(
																									    __lowercase    ,     use_fast=__lowercase    ,     )
																									__lowerCamelCase   :   List[Any]       =tokenizer_r(__lowercase    ,     return_offsets_mapping=__lowercase    ,     add_special_tokens=__lowercase      )
																									self.assertEqual(encoding.offset_mapping[0]    ,     (1, 1 + len(__lowercase      ))      )
																									self.assertEqual(
																									    encoding.offset_mapping[1]    ,     (1 + len(__lowercase      ) + 1, 1 + len(__lowercase      ) + 1 + len(__lowercase      ))    ,     )




				def        __lowercase	(  self	:Tuple      ):
											# Test related to the breaking change introduced in transformers v4.17.0
											# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
											with self.assertRaises(__lowercase      ) as context:
																		self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer'''      )

											self.assertTrue(
											    context.exception.args[0].startswith(
											        '''The `backend_tokenizer` provided does not match the expected format.'''      )      )




				@require_ftfy
				def        __lowercase	(  self	:int      ):
											super().test_tokenization_python_rust_equals()




				def        __lowercase	(  self	:Any      ):
											# CLIP always lower cases letters
											pass











"""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_				(     snake_case__ ):
				"""simple docstring"""




				def        __lowercase	(  self	:List[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=__lowercase    ,     )
											__lowerCamelCase   :   Dict       =f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'




				@slow
				@require_torch_gpu
				def        __lowercase	(  self	:int      ):
											MarianMTModel.from_pretrained(__lowercase      )




				@slow
				@require_torch_gpu
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   Tuple       ={
											    '''$MAX_LEN''': 64,
											    '''$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   :   str       =(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''      )[1].strip()
											__lowerCamelCase   :   Union[str, Any]       =bash_script.replace('''\\\n'''    ,     ''''''      ).strip().replace('''"$@"'''    ,     ''''''      )
											for k, v in env_vars_to_replace.items():
																		__lowerCamelCase   :   Tuple       =bash_script.replace(__lowercase    ,     str(__lowercase      )      )
											__lowerCamelCase   :   Union[str, Any]       =self.get_auto_remove_tmp_dir()

											# bash_script = bash_script.replace("--fp16 ", "")
											__lowerCamelCase   :   List[Any]       =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   :   str       =['''finetune.py'''] + bash_script.split() + args
											with patch.object(__lowercase    ,     '''argv'''    ,     __lowercase      ):
																		__lowerCamelCase   :   str       =argparse.ArgumentParser()
																		__lowerCamelCase   :   Dict       =pl.Trainer.add_argparse_args(__lowercase      )
																		__lowerCamelCase   :   List[Any]       =SummarizationModule.add_model_specific_args(__lowercase    ,     os.getcwd()      )
																		__lowerCamelCase   :   int       =parser.parse_args()
																		__lowerCamelCase   :   Tuple       =main(__lowercase      )

											# Check metrics
											__lowerCamelCase   :   Union[str, Any]       =load_json(model.metrics_save_path      )
											__lowerCamelCase   :   Any       =metrics['''val'''][0]
											__lowerCamelCase   :   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''']    ,     0.01      )
											# 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   :   Any       =os.listdir(__lowercase      )
											__lowerCamelCase   :   Tuple       =[x for x in contents if x.endswith('''.ckpt'''      )][0]
											__lowerCamelCase   :   int       =os.path.join(args.output_dir    ,     __lowercase      )
											__lowerCamelCase   :   List[str]       =torch.load(__lowercase    ,     map_location='''cpu'''      )
											__lowerCamelCase   :   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:
																		__lowerCamelCase   :   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 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




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

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

																		__lowerCamelCase   :   Union[str, Any]       =distill_main(__lowercase      )

											# Check metrics
											__lowerCamelCase   :   Dict       =load_json(model.metrics_save_path      )
											__lowerCamelCase   :   List[str]       =metrics['''val'''][0]
											__lowerCamelCase   :   Tuple       =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}']    ,     __lowercase      )

											# check lightning ckpt can be loaded and has a reasonable statedict
											__lowerCamelCase   :   int       =os.listdir(__lowercase      )
											__lowerCamelCase   :   str       =[x for x in contents if x.endswith('''.ckpt'''      )][0]
											__lowerCamelCase   :   Union[str, Any]       =os.path.join(args.output_dir    ,     __lowercase      )
											__lowerCamelCase   :   Optional[int]       =torch.load(__lowercase    ,     map_location='''cpu'''      )
											__lowerCamelCase   :   int       ='''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(__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




