






'''simple docstring'''
import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


UpperCamelCase__ =			'bert-base-cased'
UpperCamelCase__ =			'google/pegasus-xsum'
UpperCamelCase__ =			[' Sam ate lunch today.', 'Sams lunch ingredients.']
UpperCamelCase__ =			['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
UpperCamelCase__ =			'patrickvonplaten/t5-tiny-random'
UpperCamelCase__ =			'sshleifer/bart-tiny-random'
UpperCamelCase__ =			'sshleifer/tiny-mbart'
UpperCamelCase__ =			'sshleifer/tiny-marian-en-de'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     "\n".join(_UpperCamelCase							)
   Path(_UpperCamelCase							).open("w"							).writelines(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(_UpperCamelCase						,		F"""{split}.source"""							)						,		_UpperCamelCase							)
      _dump_articles(os.path.join(_UpperCamelCase						,		F"""{split}.target"""							)						,		_UpperCamelCase							)
   return tmp_dir





class        _UpperCAmelCase						(		snake_case			):




    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]  ,     )
    @slow
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Dict			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     AutoTokenizer.from_pretrained(a			)
       lowercase_	:    str           =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
       lowercase_	:    Dict           =     max(len(tokenizer.encode(a			)			) for a in ARTICLES			)
       lowercase_	:    Any           =     max(len(tokenizer.encode(a			)			) for a in SUMMARIES			)
       lowercase_	:    Optional[Any]           =     4
       lowercase_	:    int           =     8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       lowercase_    ,		lowercase_	:    int           =     "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
       lowercase_	:    Union[str, Any]           =     SeqaSeqDataset(
           a  ,     data_dir=a  ,     type_path="train"  ,     max_source_length=a  ,     max_target_length=a  ,     src_lang=a  ,     tgt_lang=a  ,     )
       lowercase_	:    Optional[Any]           =     DataLoader(a  ,     batch_size=2  ,     collate_fn=train_dataset.collate_fn			)
       for batch in dataloader:
          assert isinstance(a  ,     a			)
          assert batch["attention_mask"].shape == batch["input_ids"].shape
          # show that articles were trimmed.
          assert batch["input_ids"].shape[1] == max_src_len
          # show that targets are the same len
          assert batch["labels"].shape[1] == max_tgt_len
          if tok_name != MBART_TINY:
             continue
          # check language codes in correct place
          lowercase_	:    Optional[int]           =     shift_tokens_right(batch["labels"]  ,     tokenizer.pad_token_id			)
          assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
          assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
          assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
          assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

          break  # No need to test every batch




    @parameterized.expand([BART_TINY, BERT_BASE_CASED]			)
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     AutoTokenizer.from_pretrained(a			)
       lowercase_	:    Optional[Any]           =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
       lowercase_	:    Any           =     max(len(tokenizer.encode(a			)			) for a in ARTICLES			)
       lowercase_	:    Optional[Any]           =     max(len(tokenizer.encode(a			)			) for a in SUMMARIES			)
       lowercase_	:    Any           =     4
       lowercase_	:    Tuple           =     LegacySeqaSeqDataset(
           a  ,     data_dir=a  ,     type_path="train"  ,     max_source_length=2_0  ,     max_target_length=a  ,     )
       lowercase_	:    str           =     DataLoader(a  ,     batch_size=2  ,     collate_fn=train_dataset.collate_fn			)
       for batch in dataloader:
          assert batch["attention_mask"].shape == batch["input_ids"].shape
          # show that articles were trimmed.
          assert batch["input_ids"].shape[1] == max_len_source
          assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
          # show that targets were truncated
          assert batch["labels"].shape[1] == trunc_target  # Truncated
          assert max_len_target > trunc_target  # Truncated
          break  # No need to test every batch




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Dict           =     AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"			)

       lowercase_	:    int           =     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
       lowercase_	:    Optional[Any]           =     tmp_dir.joinpath("train.source"			).open().readlines()
       lowercase_	:    str           =     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
       pack_data_dir(a  ,     a  ,     1_2_8  ,     a			)
       lowercase_	:    str           =     {x.name for x in tmp_dir.iterdir()}
       lowercase_	:    Optional[int]           =     {x.name for x in save_dir.iterdir()}
       lowercase_	:    str           =     save_dir.joinpath("train.source"			).open().readlines()
       # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
       # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
       assert len(a			) < len(a			)
       assert len(a			) == 1
       assert len(packed_examples[0]			) == sum(len(a			) for x in orig_examples			)
       assert orig_paths == new_paths




    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE  ,     reason="This test requires fairseq"			)
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       if not FAIRSEQ_AVAILABLE:
          return
       lowercase_    ,		lowercase_    ,		lowercase_	:    List[str]           =     self._get_dataset(max_len=6_4			)
       lowercase_	:    Union[str, Any]           =     6_4
       lowercase_	:    str           =     ds.make_dynamic_sampler(a  ,     required_batch_size_multiple=a			)
       lowercase_	:    Any           =     [len(a			) for x in batch_sampler]
       assert len(set(a			)			) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(a			) == len(a			)  # no dropped or added examples
       lowercase_	:    Dict           =     DataLoader(a  ,     batch_sampler=a  ,     collate_fn=ds.collate_fn  ,     num_workers=2			)
       lowercase_	:    Any           =     []
       lowercase_	:    Tuple           =     []
       for batch in data_loader:
          lowercase_	:    List[str]           =     batch["input_ids"].shape
          lowercase_	:    int           =     src_shape[0]
          assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
          lowercase_	:    Optional[Any]           =     np.product(batch["input_ids"].shape			)
          num_src_per_batch.append(a			)
          if num_src_tokens > (max_tokens * 1.1):
             failures.append(a			)
       assert num_src_per_batch[0] == max(a			)
       if failures:
          raise AssertionError(f"""too many tokens in {len(a			)} batches"""			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_    ,		lowercase_    ,		lowercase_	:    str           =     self._get_dataset(max_len=5_1_2			)
       lowercase_	:    Optional[Any]           =     2
       lowercase_	:    str           =     ds.make_sortish_sampler(a  ,     shuffle=a			)

       lowercase_	:    Any           =     DataLoader(a  ,     batch_size=a  ,     collate_fn=ds.collate_fn  ,     num_workers=2			)
       lowercase_	:    Dict           =     DataLoader(a  ,     batch_size=a  ,     collate_fn=ds.collate_fn  ,     num_workers=2  ,     sampler=a			)

       lowercase_	:    int           =     tokenizer.pad_token_id

       def count_pad_tokens(a  :       Dict  ,     a  :       Tuple="input_ids"			):
          return [batch[k].eq(a			).sum().item() for batch in data_loader]

       assert sum(count_pad_tokens(a  ,     k="labels"			)			) < sum(count_pad_tokens(a  ,     k="labels"			)			)
       assert sum(count_pad_tokens(a			)			) < sum(count_pad_tokens(a			)			)
       assert len(a			) == len(a			)




    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Optional[int]=1_0_0_0  ,     a  :       Dict=1_2_8			):






       '''simple docstring'''






       if os.getenv("USE_REAL_DATA"  ,     a			):
          lowercase_	:    Optional[int]           =     "examples/seq2seq/wmt_en_ro"
          lowercase_	:    List[str]           =     max_len * 2 * 6_4
          if not Path(a			).joinpath("train.len"			).exists():
             save_len_file(a  ,     a			)
       else:
          lowercase_	:    str           =     "examples/seq2seq/test_data/wmt_en_ro"
          lowercase_	:    Optional[Any]           =     max_len * 4
          save_len_file(a  ,     a			)

       lowercase_	:    List[Any]           =     AutoTokenizer.from_pretrained(a			)
       lowercase_	:    Optional[int]           =     SeqaSeqDataset(
           a  ,     data_dir=a  ,     type_path="train"  ,     max_source_length=a  ,     max_target_length=a  ,     n_obs=a  ,     )
       return ds, max_tokens, tokenizer




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_    ,		lowercase_	:    str           =     self._get_dataset()
       lowercase_	:    int           =     set(DistributedSortishSampler(a  ,     2_5_6  ,     num_replicas=2  ,     rank=0  ,     add_extra_examples=a			)			)
       lowercase_	:    List[Any]           =     set(DistributedSortishSampler(a  ,     2_5_6  ,     num_replicas=2  ,     rank=1  ,     add_extra_examples=a			)			)
       assert idsa.intersection(a			) == set()





    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]  ,     )
    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     AutoTokenizer.from_pretrained(a  ,     use_fast=a			)
       if tok_name == MBART_TINY:
          lowercase_	:    List[Any]           =     SeqaSeqDataset(
              a  ,     data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)  ,     type_path="train"  ,     max_source_length=4  ,     max_target_length=8  ,     src_lang="EN"  ,     tgt_lang="FR"  ,     )
          lowercase_	:    Tuple           =     train_dataset.dataset_kwargs
          assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
          lowercase_	:    List[str]           =     SeqaSeqDataset(
              a  ,     data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)  ,     type_path="train"  ,     max_source_length=4  ,     max_target_length=8  ,     )
          lowercase_	:    List[Any]           =     train_dataset.dataset_kwargs
          assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
          assert len(a			) == 1 if tok_name == BART_TINY else len(a			) == 0












'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase__ =			'scheduler_config.json'


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   int		       =    1
    __lowerCamelCase:   List[Any]		       =    2
    __lowerCamelCase:   Optional[Any]		       =    3
    __lowerCamelCase:   int		       =    4
    __lowerCamelCase:   Optional[int]		       =    5


@dataclass
class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   jnp.ndarray


class        _UpperCAmelCase						:
    __lowerCamelCase:   List[str]		       =    SCHEDULER_CONFIG_NAME
    __lowerCamelCase:   Optional[int]		       =    ['dtype']
    __lowerCamelCase:   int		       =    []
    __lowerCamelCase:   Dict		       =    True




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Dict[str, Any] = None  ,     a  :       Optional[str] = None  ,     a  :       Union[str, Any]=False  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Any           =     cls.load_config(
           pretrained_model_name_or_path=a  ,     subfolder=a  ,     return_unused_kwargs=a  ,     **a  ,     )
       lowercase_    ,		lowercase_	:    Union[str, Any]           =     cls.from_config(a  ,     return_unused_kwargs=a  ,     **a			)

       if hasattr(a  ,     "create_state"			) and getattr(a  ,     "has_state"  ,     a			):
          lowercase_	:    Tuple           =     scheduler.create_state()

       if return_unused_kwargs:
          return scheduler, state, unused_kwargs

       return scheduler, state




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Union[str, os.PathLike]  ,     a  :       bool = False  ,     **a  :       int			):






       '''simple docstring'''






       self.save_config(save_directory=a  ,     push_to_hub=a  ,     **a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       return self._get_compatibles()





    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     list(set([cls.__name__] + cls._compatibles			)			)
       lowercase_	:    str           =     importlib.import_module(__name__.split("."			)[0]			)
       lowercase_	:    Optional[Any]           =     [
           getattr(a  ,     a			) for c in compatible_classes_str if hasattr(a  ,     a			)
       ]
       return compatible_classes




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   assert len(_UpperCamelCase							) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCamelCase							) - x.ndim)							)						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=0.999						,		_UpperCamelCase=jnp.floataa							):


   """simple docstring"""




   def alpha_bar(_UpperCamelCase							):
      return math.cos((time_step + 0.008) / 1.008 * math.pi / 2							) ** 2

   lowercase_	:    int           =     []
   for i in range(_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     i / num_diffusion_timesteps
      lowercase_	:    Dict           =     (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(_UpperCamelCase							) / alpha_bar(_UpperCamelCase							)						,		_UpperCamelCase							)							)
   return jnp.array(_UpperCamelCase						,		dtype=_UpperCamelCase							)


@flax.struct.dataclass
class        _UpperCAmelCase						:
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    Any           =     scheduler.config

       if config.trained_betas is not None:
          lowercase_	:    Union[str, Any]           =     jnp.asarray(config.trained_betas  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "linear":
          lowercase_	:    List[Any]           =     jnp.linspace(config.beta_start  ,     config.beta_end  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase_	:    Tuple           =     (
              jnp.linspace(
                  config.beta_start**0.5  ,     config.beta_end**0.5  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
              ** 2
          )
       elif config.beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase_	:    Union[str, Any]           =     betas_for_alpha_bar(config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       else:
          raise NotImplementedError(
              f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""			)

       lowercase_	:    str           =     1.0 - betas

       lowercase_	:    Dict           =     jnp.cumprod(a  ,     axis=0			)

       return cls(
           alphas=a  ,     betas=a  ,     alphas_cumprod=a  ,     )




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     state.alphas_cumprod

   lowercase_	:    Optional[Any]           =     alphas_cumprod[timesteps] ** 0.5
   lowercase_	:    int           =     sqrt_alpha_prod.flatten()
   lowercase_	:    Union[str, Any]           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   lowercase_	:    Optional[int]           =     (1 - alphas_cumprod[timesteps]) ** 0.5
   lowercase_	:    Union[str, Any]           =     sqrt_one_minus_alpha_prod.flatten()
   lowercase_	:    Any           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    str           =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Any           =     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity




