




import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
     import bitsandbytes as bnb
     import torch
     import torch.nn as nn

     from ..pytorch_utils import ConvaD

if is_accelerate_available():
     from accelerate import init_empty_weights
     from accelerate.utils import find_tied_parameters

_UpperCAmelCase	:						Optional[int]			=     logging.get_logger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None   ):
  # Recurse if needed
  if "." in tensor_name:
    lowercase    :List[str]      	=						tensor_name.split("."   )
    for split in splits[:-1]:
      lowercase    :Any      	=						getattr(lowerCamelCase,			lowerCamelCase   )
      if new_module is None:
        raise ValueError(F"{module} has no attribute {split}."   )
      lowercase    :Dict      	=						new_module
    lowercase    :int      	=						splits[-1]

  if tensor_name not in module._parameters and tensor_name not in module._buffers:
    raise ValueError(F"{module} does not have a parameter or a buffer named {tensor_name}."   )
  lowercase    :Optional[int]      	=						tensor_name in module._buffers
  lowercase    :Optional[Any]      	=						getattr(lowerCamelCase,			lowerCamelCase   )

  if old_value.device == torch.device("meta"   ) and device not in ["meta", torch.device("meta"   )] and value is None:
    raise ValueError(F"{tensor_name} is on the meta device, we need a `value` to put in on {device}."   )

  lowercase    :Optional[int]      	=						False
  lowercase    :List[str]      	=						False
  if is_buffer or not is_bitsandbytes_available():
    lowercase    :Dict      	=						False
    lowercase    :List[Any]      	=						False
  else:
    lowercase    :Dict      	=						hasattr(bnb.nn,			"Params4bit"   ) and isinstance(module._parameters[tensor_name],			bnb.nn.Paramsabit   )
    lowercase    :str      	=						isinstance(module._parameters[tensor_name],			bnb.nn.IntaParams   )

  if is_abit or is_abit:
    lowercase    :Union[str, Any]      	=						module._parameters[tensor_name]
    if param.device.type != "cuda":
      if value is None:
        lowercase    :Dict      	=						old_value.to(lowerCamelCase   )
      elif isinstance(lowerCamelCase,			torch.Tensor   ):
        lowercase    :Dict      	=						value.to("cpu"   )
        if value.dtype == torch.inta:
          lowercase    :Dict      	=						version.parse(importlib.metadata.version("bitsandbytes"   )   ) > version.parse(
              "0.37.2"   )
          if not is_abit_serializable:
            raise ValueError(
                "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
                "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."   )
      else:
        lowercase    :List[Any]      	=						torch.tensor(lowerCamelCase,			device="cpu"   )

      # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
      # Since weights are saved in the correct "orientation", we skip transposing when loading.
      if issubclass(module.source_cls,			lowerCamelCase   ) and fpaa_statistics is None:
        lowercase    :Tuple      	=						new_value.T

      lowercase    :Tuple      	=						old_value.__dict__
      if is_abit:
        lowercase    :int      	=						bnb.nn.IntaParams(lowerCamelCase,			requires_grad=lowerCamelCase,			**lowerCamelCase   ).to(lowerCamelCase   )
      elif is_abit:
        lowercase    :Any      	=						bnb.nn.Paramsabit(lowerCamelCase,			requires_grad=lowerCamelCase,			**lowerCamelCase   ).to(lowerCamelCase   )

      lowercase    :List[str]      	=						new_value
      if fpaa_statistics is not None:
        setattr(module.weight,			"SCB",			fpaa_statistics.to(lowerCamelCase   )   )

  else:
    if value is None:
      lowercase    :List[str]      	=						old_value.to(lowerCamelCase   )
    elif isinstance(lowerCamelCase,			torch.Tensor   ):
      lowercase    :str      	=						value.to(lowerCamelCase   )
    else:
      lowercase    :Any      	=						torch.tensor(lowerCamelCase,			device=lowerCamelCase   )

    if is_buffer:
      lowercase    :int      	=						new_value
    else:
      lowercase    :Optional[Any]      	=						nn.Parameter(lowerCamelCase,			requires_grad=old_value.requires_grad   )
      lowercase    :Optional[int]      	=						new_value



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=False   ):
  for name, module in model.named_children():
    if current_key_name is None:
      lowercase    :Optional[int]      	=						[]
    current_key_name.append(lowerCamelCase   )

    if (isinstance(lowerCamelCase,			nn.Linear   ) or isinstance(lowerCamelCase,			lowerCamelCase   )) and name not in modules_to_not_convert:
      # Check if the current key is not in the `modules_to_not_convert`
      if not any(key in ".".join(lowerCamelCase   ) for key in modules_to_not_convert   ):
        with init_empty_weights():
          if isinstance(lowerCamelCase,			lowerCamelCase   ):
            lowercase	,    lowercase    :List[str]      	=						module.weight.shape
          else:
            lowercase    :Tuple      	=						module.in_features
            lowercase    :Tuple      	=						module.out_features

          if quantization_config.quantization_method() == "llm_int8":
            lowercase    :Tuple      	=						bnb.nn.LinearabitLt(
                lowerCamelCase,			lowerCamelCase,			module.bias is not None,			has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight,			threshold=quantization_config.llm_inta_threshold,			)
            lowercase    :Optional[int]      	=						True
          else:
            if (
                quantization_config.llm_inta_skip_modules is not None
                and name in quantization_config.llm_inta_skip_modules
            ):
              pass
            else:
              lowercase    :Any      	=						bnb.nn.Linearabit(
                  lowerCamelCase,			lowerCamelCase,			module.bias is not None,			quantization_config.bnb_abit_compute_dtype,			compress_statistics=quantization_config.bnb_abit_use_double_quant,			quant_type=quantization_config.bnb_abit_quant_type,			)
              lowercase    :List[Any]      	=						True
                    # Store the module class in case we need to transpose the weight later
          lowercase    :Union[str, Any]      	=						type(lowerCamelCase   )
          # Force requires grad to False to avoid unexpected errors
          model._modules[name].requires_grad_(lowerCamelCase   )
    if len(list(module.children()   )   ) > 0:
      lowercase	,    lowercase    :List[str]      	=						_replace_with_bnb_linear(
          lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			has_been_replaced=lowerCamelCase,			)
    # Remove the last key for recursion
    current_key_name.pop(-1   )
  return model, has_been_replaced



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=None   ):
  lowercase    :Any      	=						["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
  lowercase	,    lowercase    :Any      	=						_replace_with_bnb_linear(
      lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase   )

  if not has_been_replaced:
    logger.warning(
        "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
        " Please double check your model architecture, or submit an issue on github if you think this is"
        " a bug."   )

  return model



def       UpperCAmelCase__  (     *lowerCamelCase,			**lowerCamelCase   ):
  warnings.warn(
      "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead",			lowerCamelCase,			)
  return replace_with_bnb_linear(*lowerCamelCase,			**lowerCamelCase   )



def       UpperCAmelCase__  (     *lowerCamelCase,			**lowerCamelCase   ):
  warnings.warn(
      "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead",			lowerCamelCase,			)
  return set_module_quantized_tensor_to_device(*lowerCamelCase,			**lowerCamelCase   )







def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :int      	=						deepcopy(lowerCamelCase   )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
  tied_model.tie_weights()

  lowercase    :Union[str, Any]      	=						find_tied_parameters(lowerCamelCase   )
  # For compatibility with Accelerate < 0.18
  if isinstance(lowerCamelCase,			lowerCamelCase   ):
    lowercase    :Union[str, Any]      	=						sum(list(tied_params.values()   ),			[]   ) + list(tied_params.keys()   )
  else:
    lowercase    :Tuple      	=						sum(lowerCamelCase,			[]   )
  lowercase    :Any      	=						len(lowerCamelCase   ) > 0

  # Check if it is a base model
  lowercase    :str      	=						not hasattr(lowerCamelCase,			model.base_model_prefix   )

  # Ignore this for base models (BertModel, GPT2Model, etc.)
  if (not has_tied_params) and is_base_model:
    return []

  # otherwise they have an attached head
  lowercase    :Union[str, Any]      	=						list(model.named_children()   )
  lowercase    :Tuple      	=						[list_modules[-1][0]]

  # add last module together with tied weights
  lowercase    :str      	=						set(lowerCamelCase   ) - set(lowerCamelCase   )
  lowercase    :List[Any]      	=						list(set(lowerCamelCase   )   ) + list(lowerCamelCase   )

  # remove ".weight" from the keys
  lowercase    :List[Any]      	=						[".weight", ".bias"]
  lowercase    :List[str]      	=						[]
  for name in list_untouched:
    for name_to_remove in names_to_remove:
      if name_to_remove in name:
        lowercase    :str      	=						name.replace(lowerCamelCase,			""   )
    filtered_module_names.append(lowerCamelCase   )

  return filtered_module_names









import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch

if is_datasets_available():
     from datasets import load_dataset

_UpperCAmelCase	:						List[str]			=     random.Random()



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=1.0,			lowerCamelCase=None,			lowerCamelCase=None   ):
  if rng is None:
    lowercase    :Union[str, Any]      	=						global_rng

  lowercase    :Any      	=						[]
  for batch_idx in range(shape[0]   ):
    values.append([]   )
    for _ in range(shape[1]   ):
      values[-1].append(rng.random() * scale   )

  return values




class    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Union[str, Any]=7       ,					_lowerCAmelCase:					str=4_00       ,					_lowerCAmelCase:					List[str]=20_00       ,					_lowerCAmelCase:					Dict=20_48       ,					_lowerCAmelCase:					Any=1_28       ,					_lowerCAmelCase:					Any=1       ,					_lowerCAmelCase:					List[Any]=5_12       ,					_lowerCAmelCase:					Optional[int]=30       ,					_lowerCAmelCase:					List[Any]=4_41_00       ,					):
         lowercase    :Any      	=						parent
         lowercase    :Any      	=						batch_size
         lowercase    :Optional[int]      	=						min_seq_length
         lowercase    :Optional[Any]      	=						max_seq_length
         lowercase    :Dict      	=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         lowercase    :List[str]      	=						spectrogram_length
         lowercase    :int      	=						feature_size
         lowercase    :Union[str, Any]      	=						num_audio_channels
         lowercase    :Optional[int]      	=						hop_length
         lowercase    :str      	=						chunk_length
         lowercase    :List[str]      	=						sampling_rate






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					int=False       ,					_lowerCAmelCase:					int=False				):
         def _flatten(_lowerCAmelCase:					Optional[Any]				):
           return list(itertools.chain(*_lowerCAmelCase				)				)

         if equal_length:
           lowercase    :Any      	=						[floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
         else:
           # make sure that inputs increase in size
           lowercase    :Tuple      	=						[
               floats_list((x, self.feature_size)				)
               for x in range(self.min_seq_length       ,					self.max_seq_length       ,					self.seq_length_diff				)
           ]
         if numpify:
           lowercase    :Optional[int]      	=						[np.asarray(_lowerCAmelCase				) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class    __lowerCAmelCase       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						TvltFeatureExtractor






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Dict      	=						TvltFeatureExtractionTester(self				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :List[Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"spectrogram_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"feature_size"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"num_audio_channels"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"hop_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"chunk_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"sampling_rate"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Any      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Tuple      	=						feat_extract_first.save_pretrained(_lowerCAmelCase				)[0]
           check_json_file_has_correct_format(_lowerCAmelCase				)
           lowercase    :Tuple      	=						self.feature_extraction_class.from_pretrained(_lowerCAmelCase				)

         lowercase    :Optional[Any]      	=						feat_extract_first.to_dict()
         lowercase    :List[str]      	=						feat_extract_second.to_dict()
         lowercase    :Any      	=						dict_first.pop("mel_filters"				)
         lowercase    :Any      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Union[str, Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Any      	=						os.path.join(_lowerCAmelCase       ,					"feat_extract.json"				)
           feat_extract_first.to_json_file(_lowerCAmelCase				)
           lowercase    :List[Any]      	=						self.feature_extraction_class.from_json_file(_lowerCAmelCase				)

         lowercase    :Tuple      	=						feat_extract_first.to_dict()
         lowercase    :int      	=						feat_extract_second.to_dict()
         lowercase    :Tuple      	=						dict_first.pop("mel_filters"				)
         lowercase    :Tuple      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         # Initialize feature_extractor
         lowercase    :int      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         # create three inputs of length 800, 1000, and 1200
         lowercase    :List[Any]      	=						[floats_list((1, x)				)[0] for x in range(8_00       ,					14_00       ,					2_00				)]
         lowercase    :Union[str, Any]      	=						[np.asarray(_lowerCAmelCase				) for speech_input in speech_inputs]

         # Test not batched input
         lowercase    :Any      	=						feature_extractor(np_speech_inputs[0]       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test batched
         lowercase    :Optional[int]      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test audio masking
         lowercase    :Dict      	=						feature_extractor(
             _lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00       ,					mask_audio=_lowerCAmelCase				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test 2-D numpy arrays are batched.
         lowercase    :Optional[int]      	=						[floats_list((1, x)				)[0] for x in (8_00, 8_00, 8_00)]
         lowercase    :Union[str, Any]      	=						np.asarray(_lowerCAmelCase				)
         lowercase    :Dict      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values
         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					Optional[Any]				):
         lowercase    :str      	=						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,					"clean"       ,					split="validation"				)
         # automatic decoding with librispeech
         lowercase    :Any      	=						ds.sort("id"				).select(range(_lowerCAmelCase				)				)[:num_samples]["audio"]

         return [x["array"] for x in speech_samples]






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :Optional[int]      	=						self._load_datasamples(1				)
         lowercase    :Union[str, Any]      	=						TvltFeatureExtractor()
         lowercase    :str      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="pt"				).audio_values

         self.assertEquals(audio_values.shape       ,					(1, 1, 1_92, 1_28)				)

         lowercase    :Optional[int]      	=						torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]				)
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,					_lowerCAmelCase       ,					atol=1e-4				)				)



