


"""simple docstring"""







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       from .features import FeatureType


lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_       :       Dict      						=  False, False, False






@dataclass
class 				__A    :






 """simple docstring"""




 __lowerCAmelCase			 =  None
 __lowerCAmelCase			 =  True
 __lowerCAmelCase			 =  True
 __lowerCAmelCase			 =  None
 # Automatically constructed
 __lowerCAmelCase			 =  "dict"
 __lowerCAmelCase			 =  pa.struct({"bytes": pa.binary(), "path": pa.string()}      )
 __lowerCAmelCase			 =  field(default="Audio",   init=_SCREAMING_SNAKE_CASE,   repr=_SCREAMING_SNAKE_CASE      )


 def __call__(		self						) ->					List[str]:
     return self.pa_type


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					dict:
     try:
         import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
     except ImportError as err:
         raise ImportError('''To support encoding audio data, please install \'soundfile\'.'''						) from err
     if isinstance(__A			,   __A						):
         return {"bytes": None, "path": value}
     elif isinstance(__A			,   __A						):
         return {"bytes": value, "path": None}
     elif "array" in value:
         # convert the audio array to wav bytes
         a					=BytesIO()
         sf.write(__A			,   value['''array''']			,   value['''sampling_rate''']			,   format='''wav'''						)
         return {"bytes": buffer.getvalue(), "path": None}
     elif value.get('''path'''						) is not None and os.path.isfile(value['''path''']						):
         # we set "bytes": None to not duplicate the data if they're already available locally
         if value["path"].endswith('''pcm'''						):
             # "PCM" only has raw audio bytes
             if value.get('''sampling_rate'''						) is None:
                 # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
                 raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object'''						)
             if value.get('''bytes'''						):
                 # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
                 a					=np.frombuffer(value['''bytes''']			,   dtype=np.intaa						).astype(np.floataa						) / 3_2767
             else:
                 a					=np.memmap(value['''path''']			,   dtype='''h'''			,   mode='''r'''						).astype(np.floataa						) / 3_2767

             a					=BytesIO(bytes()						)
             sf.write(__A			,   __A			,   value['''sampling_rate''']			,   format='''wav'''						)
             return {"bytes": buffer.getvalue(), "path": None}
         else:
             return {"bytes": None, "path": value.get('''path'''						)}
     elif value.get('''bytes'''						) is not None or value.get('''path'''						) is not None:
         # store the audio bytes, and path is used to infer the audio format using the file extension
         return {"bytes": value.get('''bytes'''						), "path": value.get('''path'''						)}
     else:
         raise ValueError(
             f'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					dict:
     if not self.decode:
         raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.'''						)

     a		,				a					=(value['''path'''], BytesIO(value['''bytes''']						)) if value['''bytes'''] is not None else (value['''path'''], None)
     if path is None and file is None:
         raise ValueError(f'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'''						)

     try:
         import librosa
         import soundfile as sf
     except ImportError as err:
         raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.'''						) from err

     a					=xsplitext(__A						)[1][1:].lower() if path is not None else None
     if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
         raise RuntimeError(
             '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
             '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''						)
     elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
         raise RuntimeError(
             '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
             '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''						)

     if file is None:
         a					=token_per_repo_id or {}
         a					=path.split('''::'''						)[-1]
         try:
             a					=string_to_dict(__A			,   config.HUB_DATASETS_URL						)['''repo_id''']
             a					=token_per_repo_id[repo_id]
         except (ValueError, KeyError):
             a					=None

         with xopen(__A			,   '''rb'''			,   use_auth_token=__A						) as f:
             a		,				a					=sf.read(__A						)

     else:
         a		,				a					=sf.read(__A						)

     a					=array.T
     if self.mono:
         a					=librosa.to_mono(__A						)
     if self.sampling_rate and self.sampling_rate != sampling_rate:
         a					=librosa.resample(__A			,   orig_sr=__A			,   target_sr=self.sampling_rate						)
         a					=self.sampling_rate

     return {"path": path, "array": array, "sampling_rate": sampling_rate}


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union["FeatureType", Dict[str, "FeatureType"]]:
     from .features import Value

     if self.decode:
         raise ValueError('''Cannot flatten a decoded Audio feature.'''						)
     return {
         "bytes": Value('''binary'''						),
         "path": Value('''string'''						),
     }


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					pa.StructArray:
     if pa.types.is_string(storage.type						):
         a					=pa.array([None] * len(__A						)			,   type=pa.binary()						)
         a					=pa.StructArray.from_arrays([bytes_array, storage]			,   ['''bytes''', '''path''']			,   mask=storage.is_null()						)
     elif pa.types.is_binary(storage.type						):
         a					=pa.array([None] * len(__A						)			,   type=pa.string()						)
         a					=pa.StructArray.from_arrays([storage, path_array]			,   ['''bytes''', '''path''']			,   mask=storage.is_null()						)
     elif pa.types.is_struct(storage.type						) and storage.type.get_all_field_indices('''array'''						):
         a					=pa.array([Audio().encode_example(__A						) if x is not None else None for x in storage.to_pylist()]						)
     elif pa.types.is_struct(storage.type						):
         if storage.type.get_field_index('''bytes'''						) >= 0:
             a					=storage.field('''bytes'''						)
         else:
             a					=pa.array([None] * len(__A						)			,   type=pa.binary()						)
         if storage.type.get_field_index('''path'''						) >= 0:
             a					=storage.field('''path'''						)
         else:
             a					=pa.array([None] * len(__A						)			,   type=pa.string()						)
         a					=pa.StructArray.from_arrays([bytes_array, path_array]			,   ['''bytes''', '''path''']			,   mask=storage.is_null()						)
     return array_cast(__A			,   self.pa_type						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					pa.StructArray:

     @no_op_if_value_is_null
     def path_to_bytes(__A						):
         with xopen(__A			,   '''rb'''						) as f:
             a					=f.read()
         return bytes_

     a					=pa.array(
         [
             (path_to_bytes(x['''path''']						) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
             for x in storage.to_pylist()
         ]			,   type=pa.binary()			,   )
     a					=pa.array(
         [os.path.basename(__A						) if path is not None else None for path in storage.field('''path'''						).to_pylist()]			,   type=pa.string()			,   )
     a					=pa.StructArray.from_arrays([bytes_array, path_array]			,   ['''bytes''', '''path''']			,   mask=bytes_array.is_null()						)
     return array_cast(__A			,   self.pa_type						)



"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  IFInpaintingSuperResolutionPipeline
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}      )
 __lowerCAmelCase			 =  PipelineTesterMixin.required_optional_params - {"latents"}


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     return self._get_superresolution_dummy_components()


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0						) ->					Optional[int]:
     if str(__A						).startswith('''mps'''						):
         a					=torch.manual_seed(__A						)
     else:
         a					=torch.Generator(device=__A						).manual_seed(__A						)

     a					=floats_tensor((1, 3, 16, 16)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)

     a					={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''original_image''': original_image,
         '''mask_image''': mask_image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }

     return inputs


 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()			,   reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,   )
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_save_load_optional_components()


 @unittest.skipIf(torch_device != '''cuda'''			,   reason='''float16 requires CUDA'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
     super().test_save_load_floataa(expected_max_diff=1E-1						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     self._test_save_load_local()





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2			,   )