

"""simple docstring"""






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=True ,   _lowerCamelCase="pt"   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[Any]  =    {'add_prefix_space': True} if isinstance(UpperCAmelCase__ ,   UpperCAmelCase__   ) and not line.startswith(' '   ) else {}
    _lowerCAmelCase       :   Union[str, Any]  =    padding_side
    return tokenizer(
        [line] ,   max_length=UpperCAmelCase__ ,   padding='max_length' if pad_to_max_length else None ,   truncation=UpperCAmelCase__ ,   return_tensors=UpperCAmelCase__ ,   add_special_tokens=UpperCAmelCase__ ,   **UpperCAmelCase__ ,   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=None ,   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    input_ids.ne(UpperCAmelCase__   ).any(dim=0   )
    if attention_mask is None:
        return input_ids[:, keep_column_mask]
    else:
        return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class 	__UpperCamelCase    (    _a						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A="train"    ,_A=None    ,_A=None    ,_A=None    ,_A=""    ,):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    Path(_A      ).joinpath(type_path + '.source'      )
      _lowerCAmelCase       :   Union[str, Any]  =    Path(_A      ).joinpath(type_path + '.target'      )
      _lowerCAmelCase       :   Optional[int]  =    self.get_char_lens(self.src_file      )
      _lowerCAmelCase       :   Union[str, Any]  =    max_source_length
      _lowerCAmelCase       :   Optional[int]  =    max_target_length
      assert min(self.src_lens      ) > 0, F"""found empty line in {self.src_file}"""
      _lowerCAmelCase       :   Dict  =    tokenizer
      _lowerCAmelCase       :   List[Any]  =    prefix
      if n_obs is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.src_lens[:n_obs]
      _lowerCAmelCase       :   Tuple  =    src_lang
      _lowerCAmelCase       :   List[str]  =    tgt_lang

  def __len__(		self      ):
      '''simple docstring'''

      return len(self.src_lens      )

  def __getitem__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    index + 1  # linecache starts at 1
      _lowerCAmelCase       :   Tuple  =    self.prefix + linecache.getline(str(self.src_file      )    ,_A      ).rstrip('\n'      )
      _lowerCAmelCase       :   Tuple  =    linecache.getline(str(self.tgt_file      )    ,_A      ).rstrip('\n'      )
      assert source_line, F"""empty source line for index {index}"""
      assert tgt_line, F"""empty tgt line for index {index}"""

      # Need to add eos token manually for T5
      if isinstance(self.tokenizer    ,_A      ):
          source_line += self.tokenizer.eos_token
          tgt_line += self.tokenizer.eos_token

      # Pad source and target to the right
      _lowerCAmelCase       :   Tuple  =    (
          self.tokenizer.question_encoder if isinstance(self.tokenizer    ,_A      ) else self.tokenizer
      )
      _lowerCAmelCase       :   Optional[Any]  =    self.tokenizer.generator if isinstance(self.tokenizer    ,_A      ) else self.tokenizer

      _lowerCAmelCase       :   Optional[int]  =    encode_line(_A    ,_A    ,self.max_source_length    ,'right'      )
      _lowerCAmelCase       :   Tuple  =    encode_line(_A    ,_A    ,self.max_target_length    ,'right'      )

      _lowerCAmelCase       :   Optional[int]  =    source_inputs['input_ids'].squeeze()
      _lowerCAmelCase       :   Optional[Any]  =    target_inputs['input_ids'].squeeze()
      _lowerCAmelCase       :   Union[str, Any]  =    source_inputs['attention_mask'].squeeze()
      return {
          "input_ids": source_ids,
          "attention_mask": src_mask,
          "decoder_input_ids": target_ids,
      }

  @staticmethod
  def __lowerCamelCase		(		_A      ):
      '''simple docstring'''

      return [len(_A      ) for x in Path(_A      ).open().readlines()]







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    torch.stack([x['input_ids'] for x in batch]      )
      _lowerCAmelCase       :   List[str]  =    torch.stack([x['attention_mask'] for x in batch]      )
      _lowerCAmelCase       :   Tuple  =    torch.stack([x['decoder_input_ids'] for x in batch]      )
      _lowerCAmelCase       :   Optional[Any]  =    (
          self.tokenizer.generator.pad_token_id
          if isinstance(self.tokenizer    ,_A      )
          else self.tokenizer.pad_token_id
      )
      _lowerCAmelCase       :   List[str]  =    (
          self.tokenizer.question_encoder.pad_token_id
          if isinstance(self.tokenizer    ,_A      )
          else self.tokenizer.pad_token_id
      )
      _lowerCAmelCase       :   Any  =    trim_batch(_A    ,_A      )
      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    trim_batch(_A    ,_A    ,attention_mask=_A      )
      _lowerCAmelCase       :   Any  =    {
          'input_ids': source_ids,
          'attention_mask': source_mask,
          'decoder_input_ids': y,
      }
      return batch





_lowerCAmelCase						=		getLogger(__name__)

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return list(itertools.chain.from_iterable(UpperCAmelCase__   )   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    get_git_info()
    save_json(UpperCAmelCase__ ,   os.path.join(UpperCAmelCase__ ,   'git_log.json'   )   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=4 ,   **_lowerCamelCase   ):

    '''simple docstring'''



    with open(UpperCAmelCase__ ,   'w'   ) as f:
        json.dump(UpperCAmelCase__ ,   UpperCAmelCase__ ,   indent=UpperCAmelCase__ ,   **UpperCAmelCase__   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    with open(UpperCAmelCase__   ) as f:
        return json.load(UpperCAmelCase__   )

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    git.Repo(search_parent_directories=UpperCAmelCase__   )
    _lowerCAmelCase       :   Union[str, Any]  =    {
        'repo_id': str(UpperCAmelCase__   ),
        'repo_sha': str(repo.head.object.hexsha   ),
        'repo_branch': str(repo.active_branch   ),
        'hostname': str(socket.gethostname()   ),
    }
    return repo_infos

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    return list(map(UpperCAmelCase__ ,   UpperCAmelCase__   )   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    with open(UpperCAmelCase__ ,   'wb'   ) as f:
        return pickle.dump(UpperCAmelCase__ ,   UpperCAmelCase__   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    def remove_articles(_lowerCamelCase   ):
        return re.sub(R'\b(a|an|the)\b' ,   ' ' ,   UpperCAmelCase__   )

    def white_space_fix(_lowerCamelCase   ):
        return " ".join(text.split()   )

    def remove_punc(_lowerCamelCase   ):
        _lowerCAmelCase       :   List[str]  =    set(string.punctuation   )
        return "".join(ch for ch in text if ch not in exclude   )

    def lower(_lowerCamelCase   ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__   )   )   )   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Union[str, Any]  =    normalize_answer(UpperCAmelCase__   ).split()
    _lowerCAmelCase       :   Tuple  =    normalize_answer(UpperCAmelCase__   ).split()
    _lowerCAmelCase       :   Tuple  =    Counter(UpperCAmelCase__   ) & Counter(UpperCAmelCase__   )
    _lowerCAmelCase       :   str  =    sum(common.values()   )
    if num_same == 0:
        return 0
    _lowerCAmelCase       :   Tuple  =    1.0 * num_same / len(UpperCAmelCase__   )
    _lowerCAmelCase       :   str  =    1.0 * num_same / len(UpperCAmelCase__   )
    _lowerCAmelCase       :   List[str]  =    (2 * precision * recall) / (precision + recall)
    return fa

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    return normalize_answer(UpperCAmelCase__   ) == normalize_answer(UpperCAmelCase__   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    assert len(UpperCAmelCase__   ) == len(UpperCAmelCase__   )
    _lowerCAmelCase       :   str  =    0
    for hypo, pred in zip(UpperCAmelCase__ ,   UpperCAmelCase__   ):
        em += exact_match_score(UpperCAmelCase__ ,   UpperCAmelCase__   )
    if len(UpperCAmelCase__   ) > 0:
        em /= len(UpperCAmelCase__   )
    return {"em": em}

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return model_prefix.startswith('rag'   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    {p: p for p in extra_params}
    # T5 models don't have `dropout` param, they have `dropout_rate` instead
    _lowerCAmelCase       :   Tuple  =    'dropout_rate'
    for p in extra_params:
        if getattr(UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__   ):
            if not hasattr(UpperCAmelCase__ ,   UpperCAmelCase__   ) and not hasattr(UpperCAmelCase__ ,   equivalent_param[p]   ):
                logger.info('config doesn\'t have a `{}` attribute'.format(UpperCAmelCase__   )   )
                delattr(UpperCAmelCase__ ,   UpperCAmelCase__   )
                continue
            _lowerCAmelCase       :   str  =    p if hasattr(UpperCAmelCase__ ,   UpperCAmelCase__   ) else equivalent_param[p]
            setattr(UpperCAmelCase__ ,   UpperCAmelCase__ ,   getattr(UpperCAmelCase__ ,   UpperCAmelCase__   )   )
            delattr(UpperCAmelCase__ ,   UpperCAmelCase__   )
    return hparams, config




"""simple docstring"""






import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		DanceDiffusionPipeline
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "callback",
      "latents",
      "callback_steps",
      "output_type",
      "num_images_per_prompt",
  }
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      torch.manual_seed(0      )
      _lowerCAmelCase       :   List[Any]  =    UNetaDModel(
          block_out_channels=(32, 32, 64)    ,extra_in_channels=16    ,sample_size=512    ,sample_rate=1_6000    ,in_channels=2    ,out_channels=2    ,flip_sin_to_cos=_A    ,use_timestep_embedding=_A    ,time_embedding_type='fourier'    ,mid_block_type='UNetMidBlock1D'    ,down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')    ,up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')    ,)
      _lowerCAmelCase       :   int  =    IPNDMScheduler()

      _lowerCAmelCase       :   Union[str, Any]  =    {
          'unet': unet,
          'scheduler': scheduler,
      }
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   str  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   int  =    {
          'batch_size': 1,
          'generator': generator,
          'num_inference_steps': 4,
      }
      return inputs

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    'cpu'  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase       :   int  =    self.get_dummy_components()
      _lowerCAmelCase       :   Optional[Any]  =    DanceDiffusionPipeline(**_A      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.get_dummy_inputs(_A      )
      _lowerCAmelCase       :   List[str]  =    pipe(**_A      )
      _lowerCAmelCase       :   List[Any]  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, components["unet"].sample_size)
      _lowerCAmelCase       :   Optional[Any]  =    np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]      )
      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_save_load_local()

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_save_load_optional_components()

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_attention_slicing_forward_pass()







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







@slow
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    torch_device

      _lowerCAmelCase       :   int  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Optional[int]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   str  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   str  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   Union[str, Any]  =    np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]      )

      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    torch_device

      _lowerCAmelCase       :   Tuple  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'    ,torch_dtype=torch.floataa      )
      _lowerCAmelCase       :   Optional[int]  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   Optional[int]  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   Union[str, Any]  =    output.audios

      _lowerCAmelCase       :   int  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   List[str]  =    np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]      )

      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2





