


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


_SCREAMING_SNAKE_CASE      :			Dict      				=   "sshleifer/mar_enro_6_3_student"

class   A__ (   snake_case__     ):







    """simple docstring"""

    def 		a_      (	self   ):
         super().setUp()

         snake_case					=    cached_path(
             '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''      , extract_compressed_file=__snake_case      , )
         snake_case					=    F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
    @slow
    @require_torch_gpu
    def 		a_      (	self   ):
         MarianMTModel.from_pretrained(__snake_case   )




    @slow
    @require_torch_gpu
    def 		a_      (	self   ):
         snake_case					=    {
             '''$MAX_LEN''': 6_4,
             '''$BS''': 6_4,
             '''$GAS''': 1,
             '''$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
         snake_case					=    (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''   )[1].strip()
         snake_case					=    bash_script.replace('''\\\n'''      , ''''''   ).strip().replace('''"$@"'''      , ''''''   )
         for k, v in env_vars_to_replace.items():
              snake_case					=    bash_script.replace(__snake_case      , str(__snake_case   )   )
         snake_case					=    self.get_auto_remove_tmp_dir()

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

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

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

         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''']      , 1_7   )

         # 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
         snake_case					=    os.listdir(__snake_case   )
         snake_case					=    [x for x in contents if x.endswith('''.ckpt'''   )][0]
         snake_case					=    os.path.join(args.output_dir      , __snake_case   )
         snake_case					=    torch.load(__snake_case      , map_location='''cpu'''   )
         snake_case					=    '''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:
              snake_case					=    {os.path.basename(__snake_case   ) 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   A__ (   snake_case__     ):







    """simple docstring"""

    @timeout_decorator.timeout(6_0_0   )
    @slow
    @require_torch_gpu
    def 		a_      (	self   ):
         snake_case					=    F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
         snake_case					=    {
             '''--fp16_opt_level=O1''': '''''',
             '''$MAX_LEN''': 1_2_8,
             '''$BS''': 1_6,
             '''$GAS''': 1,
             '''$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
         snake_case					=    (
             (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''   )[1].strip()
         )
         snake_case					=    bash_script.replace('''\\\n'''      , ''''''   ).strip().replace('''"$@"'''      , ''''''   )
         snake_case					=    bash_script.replace('''--fp16 '''      , ''' '''   )

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

              snake_case					=    distill_main(__snake_case   )

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

         # check lightning ckpt can be loaded and has a reasonable statedict
         snake_case					=    os.listdir(__snake_case   )
         snake_case					=    [x for x in contents if x.endswith('''.ckpt'''   )][0]
         snake_case					=    os.path.join(args.output_dir      , __snake_case   )
         snake_case					=    torch.load(__snake_case      , map_location='''cpu'''   )
         snake_case					=    '''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:
              snake_case					=    {os.path.basename(__snake_case   ) 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 enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)

class   A__ (   enum.Enum     ):







    """simple docstring"""

    __magic_name__        =		0
    __magic_name__        =		1






@add_end_docstrings(snake_case__     )
class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'generated'
    def __init__(	self      , *__snake_case      , **__snake_case   ):
         super().__init__(*__snake_case      , **__snake_case   )

         self.check_model_type(
             TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
             if self.framework == '''tf'''
             else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING   )
    def 		a_      (	self      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case=None      , **__snake_case      , ):
         snake_case					=    {}
         if truncation is not None:
              snake_case					=    truncation

         snake_case					=    generate_kwargs

         snake_case					=    {}
         if return_tensors is not None and return_type is None:
              snake_case					=    ReturnType.TENSORS if return_tensors else ReturnType.TEXT
         if return_type is not None:
              snake_case					=    return_type

         if clean_up_tokenization_spaces is not None:
              snake_case					=    clean_up_tokenization_spaces

         if stop_sequence is not None:
              snake_case					=    self.tokenizer.encode(__snake_case      , add_special_tokens=__snake_case   )
              if len(__snake_case   ) > 1:
                   warnings.warn(
                       '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
                       ''' the stop sequence will be used as the stop sequence string in the interim.'''   )
              snake_case					=    stop_sequence_ids[0]

         return preprocess_params, forward_params, postprocess_params
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         return True
    def 		a_      (	self      , *__snake_case      , __snake_case   ):
         snake_case					=    self.model.config.prefix if self.model.config.prefix is not None else ''''''
         if isinstance(args[0]      , __snake_case   ):
              if self.tokenizer.pad_token_id is None:
                   raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input'''   )
              snake_case					=    ([prefix + arg for arg in args[0]],)
              snake_case					=    True

         elif isinstance(args[0]      , __snake_case   ):
              snake_case					=    (prefix + args[0],)
              snake_case					=    False
         else:
              raise ValueError(
                  F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''   )
         snake_case					=    self.tokenizer(*__snake_case      , padding=__snake_case      , truncation=__snake_case      , return_tensors=self.framework   )
         # This is produced by tokenizers but is an invalid generate kwargs
         if "token_type_ids" in inputs:
              del inputs["token_type_ids"]
         return inputs
    def __call__(	self      , *__snake_case      , **__snake_case   ):
         snake_case					=    super().__call__(*__snake_case      , **__snake_case   )
         if (
             isinstance(args[0]      , __snake_case   )
             and all(isinstance(__snake_case      , __snake_case   ) for el in args[0]   )
             and all(len(__snake_case   ) == 1 for res in result   )
         ):
              return [res[0] for res in result]
         return result
    def 		a_      (	self      , __snake_case      , __snake_case=TruncationStrategy.DO_NOT_TRUNCATE      , **__snake_case   ):
         snake_case					=    self._parse_and_tokenize(__snake_case      , truncation=__snake_case      , **__snake_case   )
         return inputs
    def 		a_      (	self      , __snake_case      , **__snake_case   ):
         if self.framework == "pt":
              snake_case			,  snake_case					=    model_inputs['''input_ids'''].shape
         elif self.framework == "tf":
              snake_case			,  snake_case					=    tf.shape(model_inputs['''input_ids''']   ).numpy()

         snake_case					=    generate_kwargs.get('''min_length'''      , self.model.config.min_length   )
         snake_case					=    generate_kwargs.get('''max_length'''      , self.model.config.max_length   )
         self.check_inputs(__snake_case      , generate_kwargs['''min_length''']      , generate_kwargs['''max_length''']   )
         snake_case					=    self.model.generate(**__snake_case      , **__snake_case   )
         snake_case					=    output_ids.shape[0]
         if self.framework == "pt":
              snake_case					=    output_ids.reshape(__snake_case      , out_b // in_b      , *output_ids.shape[1:]   )
         elif self.framework == "tf":
              snake_case					=    tf.reshape(__snake_case      , (in_b, out_b // in_b, *output_ids.shape[1:])   )
         return {"output_ids": output_ids}




    def 		a_      (	self      , __snake_case      , __snake_case=ReturnType.TEXT      , __snake_case=False   ):
         snake_case					=    []
         for output_ids in model_outputs["output_ids"][0]:
              if return_type == ReturnType.TENSORS:
                   snake_case					=    {F'''{self.return_name}_token_ids''': output_ids}
              elif return_type == ReturnType.TEXT:
                   snake_case					=    {
                       F'''{self.return_name}_text''': self.tokenizer.decode(
                           __snake_case      , skip_special_tokens=__snake_case      , clean_up_tokenization_spaces=__snake_case      , )
                   }
              records.append(__snake_case   )
         return records






@add_end_docstrings(snake_case__     )
class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'summary'
    def __call__(	self      , *__snake_case      , **__snake_case   ):
         return super().__call__(*__snake_case      , **__snake_case   )




    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         if max_length < min_length:
              logger.warning(F'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''   )

         if input_length < max_length:
              logger.warning(
                  F'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
                  '''a summarization task, where outputs shorter than the input are typically wanted, you might '''
                  F'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''   )






@add_end_docstrings(snake_case__     )
class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'translation'
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         if input_length > 0.9 * max_length:
              logger.warning(
                  F'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
                  '''increasing your max_length manually, e.g. translator(\'...\', max_length=400)'''   )
         return True
    def 		a_      (	self      , *__snake_case      , __snake_case=TruncationStrategy.DO_NOT_TRUNCATE      , __snake_case=None      , __snake_case=None   ):
         if getattr(self.tokenizer      , '''_build_translation_inputs'''      , __snake_case   ):
              return self.tokenizer._build_translation_inputs(
                  *__snake_case      , return_tensors=self.framework      , truncation=__snake_case      , src_lang=__snake_case      , tgt_lang=__snake_case   )
         else:
              return super()._parse_and_tokenize(*__snake_case      , truncation=__snake_case   )
    def 		a_      (	self      , __snake_case=None      , __snake_case=None      , **__snake_case   ):
         snake_case			,  snake_case			,  snake_case					=    super()._sanitize_parameters(**__snake_case   )
         if src_lang is not None:
              snake_case					=    src_lang
         if tgt_lang is not None:
              snake_case					=    tgt_lang
         if src_lang is None and tgt_lang is None:
              # Backward compatibility, direct arguments use is preferred.
              snake_case					=    kwargs.get('''task'''      , self.task   )
              snake_case					=    task.split('''_'''   )
              if task and len(__snake_case   ) == 4:
                   # translation, XX, to YY
                   snake_case					=    items[1]
                   snake_case					=    items[3]
         return preprocess_params, forward_params, postprocess_params




    def __call__(	self      , *__snake_case      , **__snake_case   ):
         return super().__call__(*__snake_case      , **__snake_case   )




