






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowerCamelCase      :       List[Any]    =						logging.get_logger(__name__)



class 				_UpperCamelCase      (a_      ):


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[Any]:
           __lowerCAmelCase             =       feature_size
           __lowerCAmelCase             =       sampling_rate
           __lowerCAmelCase             =       padding_value

           __lowerCAmelCase             =       kwargs.pop("padding_side"       ,    "right"						)
           __lowerCAmelCase             =       kwargs.pop("return_attention_mask"       ,    __UpperCamelCase						)

           super().__init__(**__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = True       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    )->						BatchFeature:
           # If we have a list of dicts, let's convert it in a dict of lists
           # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
           if isinstance(__UpperCamelCase       ,    (list, tuple)						) and isinstance(processed_features[0]       ,    (dict, BatchFeature)						):
                __lowerCAmelCase             =       {
                    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
                }

           # The model's main input name, usually `input_values`, has be passed for padding
           if self.model_input_names[0] not in processed_features:
                raise ValueError(
                    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
                    F""" to this method that includes {self.model_input_names[0]}, but you provided"""
                    F""" {list(processed_features.keys()						)}"""						)

           __lowerCAmelCase             =       processed_features[self.model_input_names[0]]
           __lowerCAmelCase             =       (
               return_attention_mask if return_attention_mask is not None else self.return_attention_mask
           )

           if len(__UpperCamelCase						) == 0:
                if return_attention_mask:
                     __lowerCAmelCase             =       []
                return processed_features

           # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
           # and rebuild them afterwards if no return_tensors is specified
           # Note that we lose the specific device the tensor may be on for PyTorch

           __lowerCAmelCase             =       required_input[0]
           if isinstance(__UpperCamelCase       ,    (list, tuple)						):
                # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
                __lowerCAmelCase             =       0
                while len(required_input[index]						) == 0:
                     index += 1
                if index < len(__UpperCamelCase						):
                     __lowerCAmelCase             =       required_input[index][0]

           if return_tensors is None:
                if is_tf_tensor(__UpperCamelCase						):
                     __lowerCAmelCase             =       "tf"
                elif is_torch_tensor(__UpperCamelCase						):
                     __lowerCAmelCase             =       "pt"
                elif isinstance(__UpperCamelCase       ,    (int, float, list, tuple, np.ndarray)						):
                     __lowerCAmelCase             =       "np"
                else:
                     raise ValueError(
                         F"""type of {first_element} unknown: {type(__UpperCamelCase						)}. """
                         "Should be one of a python, numpy, pytorch or tensorflow object."						)

           for key, value in processed_features.items():
                if isinstance(value[0]       ,    (int, float)						):
                     __lowerCAmelCase             =       to_numpy(__UpperCamelCase						)
                else:
                     __lowerCAmelCase             =       [to_numpy(__UpperCamelCase						) for v in value]

        # Convert padding_strategy in PaddingStrategy
           __lowerCAmelCase             =       self._get_padding_strategies(padding=__UpperCamelCase       ,    max_length=__UpperCamelCase						)

           __lowerCAmelCase             =       processed_features[self.model_input_names[0]]

           __lowerCAmelCase             =       len(__UpperCamelCase						)
           if not all(len(__UpperCamelCase						) == batch_size for v in processed_features.values()						):
                raise ValueError("Some items in the output dictionary have a different batch size than others."						)

           __lowerCAmelCase             =       []
           for i in range(__UpperCamelCase						):
                __lowerCAmelCase             =       {k: v[i] for k, v in processed_features.items()}
                # truncation
                __lowerCAmelCase             =       self._truncate(
                    __UpperCamelCase       ,    max_length=__UpperCamelCase       ,    pad_to_multiple_of=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    )
                truncated_inputs.append(__UpperCamelCase						)

           if padding_strategy == PaddingStrategy.LONGEST:
                # make sure that `max_length` cannot be longer than the longest truncated length
                __lowerCAmelCase             =       max(len(input_slice[self.model_input_names[0]]						) for input_slice in truncated_inputs						)
                __lowerCAmelCase             =       PaddingStrategy.MAX_LENGTH

           __lowerCAmelCase             =       {}
           for i in range(__UpperCamelCase						):
                # padding
                __lowerCAmelCase             =       self._pad(
                    truncated_inputs[i]       ,    max_length=__UpperCamelCase       ,    padding_strategy=__UpperCamelCase       ,    pad_to_multiple_of=__UpperCamelCase       ,    return_attention_mask=__UpperCamelCase       ,    )

                for key, value in outputs.items():
                     if key not in batch_outputs:
                          __lowerCAmelCase             =       []
                     if value.dtype is np.dtype(np.floataa						):
                          __lowerCAmelCase             =       value.astype(np.floataa						)
                     batch_outputs[key].append(__UpperCamelCase						)

           return BatchFeature(__UpperCamelCase       ,    tensor_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = None       ,    __UpperCamelCase = PaddingStrategy.DO_NOT_PAD       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    )->						dict:
           __lowerCAmelCase             =       processed_features[self.model_input_names[0]]

           if padding_strategy == PaddingStrategy.LONGEST:
                __lowerCAmelCase             =       len(__UpperCamelCase						)

           if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                __lowerCAmelCase             =       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

           __lowerCAmelCase             =       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCamelCase						) < max_length

           if return_attention_mask and "attention_mask" not in processed_features:
                __lowerCAmelCase             =       np.ones(len(__UpperCamelCase						)       ,    dtype=np.intaa						)

           if needs_to_be_padded:
                __lowerCAmelCase             =       max_length - len(__UpperCamelCase						)
                if self.padding_side == "right":
                     if return_attention_mask:
                          __lowerCAmelCase             =       np.pad(
                              processed_features["attention_mask"]       ,    (0, difference)						)
                     __lowerCAmelCase             =       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                     __lowerCAmelCase             =       np.pad(
                         __UpperCamelCase       ,    __UpperCamelCase       ,    "constant"       ,    constant_values=self.padding_value						)
                elif self.padding_side == "left":
                     if return_attention_mask:
                          __lowerCAmelCase             =       np.pad(
                              processed_features["attention_mask"]       ,    (difference, 0)						)
                     __lowerCAmelCase             =       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                     __lowerCAmelCase             =       np.pad(
                         __UpperCamelCase       ,    __UpperCamelCase       ,    "constant"       ,    constant_values=self.padding_value						)
                else:
                     raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

           return processed_features


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    )->						Optional[Any]:
           if not truncation:
                return processed_features
           elif truncation and max_length is None:
                raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."						)

           __lowerCAmelCase             =       processed_features[self.model_input_names[0]]

           # find `max_length` that fits `pad_to_multiple_of`
           if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                __lowerCAmelCase             =       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

           __lowerCAmelCase             =       len(__UpperCamelCase						) > max_length

           if needs_to_be_truncated:
                __lowerCAmelCase             =       processed_features[self.model_input_names[0]][:max_length]
                if "attention_mask" in processed_features:
                     __lowerCAmelCase             =       processed_features["attention_mask"][:max_length]

           return processed_features


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=False       ,    __UpperCamelCase=None						)->						str:

           # Get padding strategy
           if padding is not False:
                if padding is True:
                     __lowerCAmelCase             =       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                elif not isinstance(__UpperCamelCase       ,    __UpperCamelCase						):
                     __lowerCAmelCase             =       PaddingStrategy(__UpperCamelCase						)
                elif isinstance(__UpperCamelCase       ,    __UpperCamelCase						):
                     __lowerCAmelCase             =       padding
           else:
                __lowerCAmelCase             =       PaddingStrategy.DO_NOT_PAD

           # Set max length if needed
           if max_length is None:
                if padding_strategy == PaddingStrategy.MAX_LENGTH:
                     raise ValueError(
                         F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""						)

        # Test if we have a padding value
           if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
                raise ValueError(
                    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
                    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."						)

           return padding_strategy














# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase      :       Dict    =						abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)





def    __lowerCAmelCase   (    __snake_case							):
     from diffusers.utils.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(__snake_case							)





def    __lowerCAmelCase   (    __snake_case							):
     from diffusers.utils.testing_utils import pytest_terminal_summary_main

     __lowerCAmelCase             =       terminalreporter.config.getoption("--make-reports"							)
     if make_reports:
          pytest_terminal_summary_main(__snake_case       ,			id=__snake_case							)






