
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


lowercase__          ='scheduler_config.json'



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				1
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				2
   _SCREAMING_SNAKE_CASE				:							Dict     	=				3
   _SCREAMING_SNAKE_CASE				:							int     	=				4
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				5





@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray





class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							int     	=				SCHEDULER_CONFIG_NAME
   _SCREAMING_SNAKE_CASE				:							int     	=				["dtype"]
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				[]
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				True
   @classmethod
   def 				lowerCAmelCase      (cls			: Union[str, Any] ,    snake_case_			: Dict[str, Any] = None ,    snake_case_			: Optional[str] = None ,    snake_case_			: Any=False ,    **snake_case_			: List[Any] ,    ):
    __a    ,	__a			:  Tuple		    =  cls.load_config(
        pretrained_model_name_or_path=snake_case_ ,    subfolder=snake_case_ ,    return_unused_kwargs=snake_case_ ,    **snake_case_ ,    )
    __a    ,	__a			:  Dict		    =  cls.from_config(snake_case_ ,    return_unused_kwargs=snake_case_ ,    **snake_case_       )

    if hasattr(snake_case_ ,    '''create_state'''       ) and getattr(snake_case_ ,    '''has_state''' ,    snake_case_       ):
     __a			:  str		    =  scheduler.create_state()

    if return_unused_kwargs:
     return scheduler, state, unused_kwargs

    return scheduler, state
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: Union[str, os.PathLike] ,    snake_case_			: bool = False ,    **snake_case_			: Optional[int]       ):
    self.save_config(save_directory=snake_case_ ,    push_to_hub=snake_case_ ,    **snake_case_       )
   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
    return self._get_compatibles()







   @classmethod
   def 				lowerCAmelCase      (cls			: int       ):
    __a			:  Tuple		    =  list(set([cls.__name__] + cls._compatibles       )       )
    __a			:  Union[str, Any]		    =  importlib.import_module(__name__.split('''.'''       )[0]       )
    __a			:  List[Any]		    =  [
        getattr(snake_case_ ,    snake_case_       ) for c in compatible_classes_str if hasattr(snake_case_ ,    snake_case_       )
    ]
    return compatible_classes





def        __UpperCamelCase					(    lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    Tuple[int]  ):
 assert len(lowerCAmelCase__  ) >= x.ndim
 return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__  ) - x.ndim)  )  ,       lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int=0.9_99  ,       lowerCAmelCase__							:    Any=jnp.floataa  ):

 def alpha_bar(lowerCAmelCase__							:    int  ):
  return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2  ) ** 2

 __a			:  Optional[Any]		    =  []
 for i in range(lowerCAmelCase__  ):
  __a			:  List[str]		    =  i / num_diffusion_timesteps
  __a			:  List[Any]		    =  (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar(lowerCAmelCase__  ) / alpha_bar(lowerCAmelCase__  )  ,       lowerCAmelCase__  )  )
 return jnp.array(lowerCAmelCase__  ,       dtype=lowerCAmelCase__  )



@flax.struct.dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   _SCREAMING_SNAKE_CASE				:							jnp.ndarray
   @classmethod
   def 				lowerCAmelCase      (cls			: Union[str, Any] ,    snake_case_			: str       ):
    __a			:  int		    =  scheduler.config

    if config.trained_betas is not None:
     __a			:  Tuple		    =  jnp.asarray(config.trained_betas ,    dtype=scheduler.dtype       )
    elif config.beta_schedule == "linear":
     __a			:  int		    =  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.
     __a			:  Union[str, Any]		    =  (
         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
     __a			:  int		    =  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__}"       )

    __a			:  List[str]		    =  1.0 - betas

    __a			:  Any		    =  jnp.cumprod(snake_case_ ,    axis=0       )

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





def        __UpperCamelCase					(    lowerCAmelCase__							:    CommonSchedulerState  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ):
 __a			:  Optional[Any]		    =  state.alphas_cumprod

 __a			:  str		    =  alphas_cumprod[timesteps] ** 0.5
 __a			:  List[Any]		    =  sqrt_alpha_prod.flatten()
 __a			:  Tuple		    =  broadcast_to_shape_from_left(lowerCAmelCase__  ,       original_samples.shape  )

 __a			:  Any		    =  (1 - alphas_cumprod[timesteps]) ** 0.5
 __a			:  List[Any]		    =  sqrt_one_minus_alpha_prod.flatten()
 __a			:  Any		    =  broadcast_to_shape_from_left(lowerCAmelCase__  ,       original_samples.shape  )

 return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def        __UpperCamelCase					(    lowerCAmelCase__							:    CommonSchedulerState  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ):
 __a    ,	__a			:  str		    =  get_sqrt_alpha_prod(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 __a			:  str		    =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
 return noisy_samples




def        __UpperCamelCase					(    lowerCAmelCase__							:    CommonSchedulerState  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ,       lowerCAmelCase__							:    jnp.ndarray  ):
 __a    ,	__a			:  Union[str, Any]		    =  get_sqrt_alpha_prod(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 __a			:  List[Any]		    =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
 return velocity


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


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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    list  ):
 __a			:  List[Any]		    =  '''\n'''.join(lowerCAmelCase__  )
 Path(lowerCAmelCase__  ).open('''w'''  ).writelines(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ):
 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(lowerCAmelCase__  ,       f"{split}.source"  )  ,       lowerCAmelCase__  )
  _dump_articles(os.path.join(lowerCAmelCase__  ,       f"{split}.target"  )  ,       lowerCAmelCase__  )
 return tmp_dir



class 		UpperCamelCase__  (	__lowercase   ):
   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ] ,    )
   @slow
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: int       ):
    __a			:  Optional[Any]		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  Optional[Any]		    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
    __a			:  Union[str, Any]		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in ARTICLES       )
    __a			:  str		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in SUMMARIES       )
    __a			:  str		    =  4
    __a			:  Dict		    =  8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __a    ,	__a			:  Any		    =  '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
    __a			:  List[Any]		    =  SeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=snake_case_ ,    max_target_length=snake_case_ ,    src_lang=snake_case_ ,    tgt_lang=snake_case_ ,    )
    __a			:  Dict		    =  DataLoader(snake_case_ ,    batch_size=2 ,    collate_fn=train_dataset.collate_fn       )
    for batch in dataloader:
     assert isinstance(snake_case_ ,    snake_case_       )
     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
     __a			:  Dict		    =  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			: Optional[Any] ,    snake_case_			: str       ):
    __a			:  Union[str, Any]		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  str		    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
    __a			:  Any		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in ARTICLES       )
    __a			:  Any		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in SUMMARIES       )
    __a			:  Dict		    =  4
    __a			:  Optional[int]		    =  LegacySeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=2_0 ,    max_target_length=snake_case_ ,    )
    __a			:  Optional[Any]		    =  DataLoader(snake_case_ ,    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			: List[str]       ):
    __a			:  int		    =  AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''       )

    __a			:  Any		    =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
    __a			:  Optional[int]		    =  tmp_dir.joinpath('''train.source'''       ).open().readlines()
    __a			:  List[Any]		    =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
    pack_data_dir(snake_case_ ,    snake_case_ ,    1_2_8 ,    snake_case_       )
    __a			:  Optional[Any]		    =  {x.name for x in tmp_dir.iterdir()}
    __a			:  Union[str, Any]		    =  {x.name for x in save_dir.iterdir()}
    __a			:  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(snake_case_       ) < len(snake_case_       )
    assert len(snake_case_       ) == 1
    assert len(packed_examples[0]       ) == sum(len(snake_case_       ) for x in orig_examples       )
    assert orig_paths == new_paths
   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,    reason='''This test requires fairseq'''       )
   def 				lowerCAmelCase      (self			: Any       ):
    if not FAIRSEQ_AVAILABLE:
     return
    __a    ,	__a    ,	__a			:  Any		    =  self._get_dataset(max_len=6_4       )
    __a			:  int		    =  6_4
    __a			:  List[str]		    =  ds.make_dynamic_sampler(snake_case_ ,    required_batch_size_multiple=snake_case_       )
    __a			:  List[str]		    =  [len(snake_case_       ) for x in batch_sampler]
    assert len(set(snake_case_       )       ) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(snake_case_       ) == len(snake_case_       )  # no dropped or added examples
    __a			:  Union[str, Any]		    =  DataLoader(snake_case_ ,    batch_sampler=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2       )
    __a			:  Tuple		    =  []
    __a			:  Union[str, Any]		    =  []
    for batch in data_loader:
     __a			:  Any		    =  batch['''input_ids'''].shape
     __a			:  str		    =  src_shape[0]
     assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
     __a			:  Optional[Any]		    =  np.product(batch['''input_ids'''].shape       )
     num_src_per_batch.append(snake_case_       )
     if num_src_tokens > (max_tokens * 1.1):
      failures.append(snake_case_       )
    assert num_src_per_batch[0] == max(snake_case_       )
    if failures:
     raise AssertionError(f"too many tokens in {len(snake_case_       )} batches"       )
   def 				lowerCAmelCase      (self			: int       ):
    __a    ,	__a    ,	__a			:  Optional[int]		    =  self._get_dataset(max_len=5_1_2       )
    __a			:  Union[str, Any]		    =  2
    __a			:  str		    =  ds.make_sortish_sampler(snake_case_ ,    shuffle=snake_case_       )

    __a			:  Tuple		    =  DataLoader(snake_case_ ,    batch_size=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2       )
    __a			:  Tuple		    =  DataLoader(snake_case_ ,    batch_size=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2 ,    sampler=snake_case_       )

    __a			:  Optional[int]		    =  tokenizer.pad_token_id

    def count_pad_tokens(snake_case_			: Union[str, Any] ,    snake_case_			: List[str]="input_ids"       ):
     return [batch[k].eq(snake_case_       ).sum().item() for batch in data_loader]

    assert sum(count_pad_tokens(snake_case_ ,    k='''labels'''       )       ) < sum(count_pad_tokens(snake_case_ ,    k='''labels'''       )       )
    assert sum(count_pad_tokens(snake_case_       )       ) < sum(count_pad_tokens(snake_case_       )       )
    assert len(snake_case_       ) == len(snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: int=1_0_0_0 ,    snake_case_			: Optional[Any]=1_2_8       ):
    if os.getenv('''USE_REAL_DATA''' ,    snake_case_       ):
     __a			:  Optional[int]		    =  '''examples/seq2seq/wmt_en_ro'''
     __a			:  List[Any]		    =  max_len * 2 * 6_4
     if not Path(snake_case_       ).joinpath('''train.len'''       ).exists():
      save_len_file(snake_case_ ,    snake_case_       )
    else:
     __a			:  int		    =  '''examples/seq2seq/test_data/wmt_en_ro'''
     __a			:  List[str]		    =  max_len * 4
     save_len_file(snake_case_ ,    snake_case_       )

    __a			:  str		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  Optional[int]		    =  SeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=snake_case_ ,    max_target_length=snake_case_ ,    n_obs=snake_case_ ,    )
    return ds, max_tokens, tokenizer
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a    ,	__a    ,	__a			:  str		    =  self._get_dataset()
    __a			:  Optional[Any]		    =  set(DistributedSortishSampler(snake_case_ ,    2_5_6 ,    num_replicas=2 ,    rank=0 ,    add_extra_examples=snake_case_       )       )
    __a			:  Tuple		    =  set(DistributedSortishSampler(snake_case_ ,    2_5_6 ,    num_replicas=2 ,    rank=1 ,    add_extra_examples=snake_case_       )       )
    assert idsa.intersection(snake_case_       ) == set()







   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ] ,    )
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Union[str, Any]       ):
    __a			:  Union[str, Any]		    =  AutoTokenizer.from_pretrained(snake_case_ ,    use_fast=snake_case_       )
    if tok_name == MBART_TINY:
     __a			:  Any		    =  SeqaSeqDataset(
         snake_case_ ,    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''' ,    )
     __a			:  Tuple		    =  train_dataset.dataset_kwargs
     assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
     __a			:  Optional[Any]		    =  SeqaSeqDataset(
         snake_case_ ,    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 ,    )
     __a			:  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(snake_case_       ) == 1 if tok_name == BART_TINY else len(snake_case_       ) == 0
