




'''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				):
      __magic_name__  :       Union[str, Any]   							=					CLIPTokenizer
      __magic_name__  :       int   							=					CLIPTokenizerFast
      __magic_name__  :       Optional[Any]   							=					True
      __magic_name__  :       int   							=					{}
      __magic_name__  :       List[str]   							=					False






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           super().setUp()

           # fmt: off
           a_     :							Union[str, Any]     						=  ["""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
           a_     :							int     						=  dict(zip(lowercase__						,						range(len(lowercase__       )       )       )       )
           a_     :							List[str]     						=  ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
           a_     :							Union[str, Any]     						=  {"""unk_token""": """<unk>"""}

           a_     :							Dict     						=  os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES["""vocab_file"""]       )
           a_     :							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   :  str						,						**lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map       )
           return CLIPTokenizer.from_pretrained(self.tmpdirname						,						**lowercase__       )






      def 							lowercase_						( self   :  Tuple						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map       )
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,						**lowercase__       )






      def 							lowercase_						( self   :  str						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  """lower newer"""
           a_     :							Optional[int]     						=  """lower newer"""
           return input_text, output_text






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  CLIPTokenizer(self.vocab_file						,						self.merges_file						,						**self.special_tokens_map       )
           a_     :							int     						=  """lower newer"""
           a_     :							Optional[int]     						=  ["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
           a_     :							List[str]     						=  tokenizer.tokenize(lowercase__       )
           self.assertListEqual(lowercase__						,						lowercase__       )

           a_     :							List[str]     						=  tokens + [tokenizer.unk_token]
           a_     :							Optional[int]     						=  [10, 2, 16, 9, 3, 2, 16, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__       )						,						lowercase__       )






      @require_ftfy
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
                     a_     :							Dict     						=  self.tokenizer_class.from_pretrained(lowercase__						,						**lowercase__       )
                     a_     :							Union[str, Any]     						=  self.rust_tokenizer_class.from_pretrained(lowercase__						,						**lowercase__       )

                     a_     :							Optional[int]     						=  """A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
                     a_     :							List[Any]     						=  tokenizer_s.tokenize(lowercase__       )
                     a_     :							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
                     a_     :							int     						=  """xa\u0303y""" + """ """ + """x\xe3y"""
                     a_     :							Any     						=  tokenizer_s.tokenize(lowercase__       )
                     a_     :							Dict     						=  tokenizer_r.tokenize(lowercase__       )

                     self.assertListEqual(lowercase__						,						lowercase__       )

                     # Test that the tokenization is identical on unicode of space type
                     a_     :							List[str]     						=  [
                         """\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:
                          a_     :							Union[str, Any]     						=  tokenizer_s.tokenize(lowercase__       )
                          a_     :							List[Any]     						=  tokenizer_r.tokenize(lowercase__       )

                          self.assertListEqual(lowercase__						,						lowercase__       )

                     # Test that the tokenization is identical on unicode of line break type
                     a_     :							Tuple     						=  [
                         """\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:
                          a_     :							int     						=  tokenizer_s.tokenize(lowercase__       )
                          a_     :							Tuple     						=  tokenizer_r.tokenize(lowercase__       )

                          self.assertListEqual(lowercase__						,						lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
                     a_     :							Any     						=  """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                     a_     :							Any     						=  F"{text_of_1_token} {text_of_1_token}"

                     a_     :							Tuple     						=  self.rust_tokenizer_class.from_pretrained(
                         lowercase__						,						use_fast=lowercase__						,						)
                     a_     :							str     						=  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__       ))						,						)

                     a_     :							Any     						=  F" {text}"

                     a_     :							Union[str, Any]     						=  self.rust_tokenizer_class.from_pretrained(
                         lowercase__						,						use_fast=lowercase__						,						)
                     a_     :							Optional[int]     						=  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   :  Optional[int]       ):


           '''simple docstring'''

           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   :  str       ):


           '''simple docstring'''

           super().test_tokenization_python_rust_equals()






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           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


lowerCAmelCase_      :  List[str]				     =  'sshleifer/mar_enro_6_3_student'







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           super().setUp()

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






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           MarianMTModel.from_pretrained(lowercase__       )






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  {
               """$MAX_LEN""": 64,
               """$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_     :							Optional[int]     						=  (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""       )[1].strip()
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           for k, v in env_vars_to_replace.items():
                a_     :							Optional[Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  self.get_auto_remove_tmp_dir()

           # bash_script = bash_script.replace("--fp16 ", "")
           a_     :							List[str]     						=  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

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

           # Check metrics
           a_     :							List[str]     						=  load_json(model.metrics_save_path       )
           a_     :							Any     						=  metrics["""val"""][0]
           a_     :							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
           a_     :							Dict     						=  os.listdir(lowercase__       )
           a_     :							Union[str, Any]     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Dict     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							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:
                a_     :							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







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      @timeout_decorator.timeout(600       )
      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							int     						=  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
           a_     :							Dict     						=  {
               """--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
           a_     :							List[str]     						=  (
               (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""       )[1].strip()
           )
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           a_     :							Tuple     						=  bash_script.replace("""--fp16 """						,						""" """       )

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

                a_     :							Union[str, Any]     						=  distill_main(lowercase__       )

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