

import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch





class __SCREAMING_SNAKE_CASE     (	_snake_case		):






    @require_torch
    def 						__lowerCAmelCase    (       self )   ->					int:
       # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
       # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
       # while running an external program

       # python one-liner segments

       # this must be loaded before socket.socket is monkey-patched
       __SCREAMING_SNAKE_CASE       =				'''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

       __SCREAMING_SNAKE_CASE       =				'''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

       __SCREAMING_SNAKE_CASE       =				'''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
        '''

       # Force fetching the files so that we can use the cache
       __SCREAMING_SNAKE_CASE       =				'''hf-internal-testing/tiny-random-bert'''
       BertConfig.from_pretrained(__lowerCamelCase )
       BertModel.from_pretrained(__lowerCamelCase )
       BertTokenizer.from_pretrained(__lowerCamelCase )
       pipeline(task="fill-mask",		model=__lowerCamelCase )

       # baseline - just load from_pretrained with normal network
       __SCREAMING_SNAKE_CASE       =				[sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]

       # should succeed
       __SCREAMING_SNAKE_CASE       =				self.get_env()
       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       __SCREAMING_SNAKE_CASE       =				'''1'''
       __SCREAMING_SNAKE_CASE       =				subprocess.run(__lowerCamelCase,		env=__lowerCamelCase,		check=__lowerCamelCase,		capture_output=__lowerCamelCase )
       self.assertEqual(result.returncode,		0,		result.stderr )
       self.assertIn("success",		result.stdout.decode() )






    @require_torch
    def 						__lowerCAmelCase    (       self )   ->					int:
       # python one-liner segments
       # this must be loaded before socket.socket is monkey-patched
       __SCREAMING_SNAKE_CASE       =				'''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

       __SCREAMING_SNAKE_CASE       =				'''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

       __SCREAMING_SNAKE_CASE       =				'''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
        '''

       # Force fetching the files so that we can use the cache
       __SCREAMING_SNAKE_CASE       =				'''hf-internal-testing/tiny-random-bert'''
       BertConfig.from_pretrained(__lowerCamelCase )
       BertModel.from_pretrained(__lowerCamelCase )
       BertTokenizer.from_pretrained(__lowerCamelCase )
       pipeline(task="fill-mask",		model=__lowerCamelCase )

       # baseline - just load from_pretrained with normal network
       __SCREAMING_SNAKE_CASE       =				[sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]

       # should succeed
       __SCREAMING_SNAKE_CASE       =				self.get_env()
       __SCREAMING_SNAKE_CASE       =				subprocess.run(__lowerCamelCase,		env=__lowerCamelCase,		check=__lowerCamelCase,		capture_output=__lowerCamelCase )
       self.assertEqual(result.returncode,		0,		result.stderr )
       self.assertIn("success",		result.stdout.decode() )






    @require_torch
    def 						__lowerCAmelCase    (       self )   ->					Tuple:
       # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
       # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
       # while running an external program

       # python one-liner segments

       # this must be loaded before socket.socket is monkey-patched
       __SCREAMING_SNAKE_CASE       =				'''
from transformers import BertConfig, BertModel, BertTokenizer
        '''

       __SCREAMING_SNAKE_CASE       =				'''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
        '''

       __SCREAMING_SNAKE_CASE       =				'''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
        '''

       # baseline - just load from_pretrained with normal network
       __SCREAMING_SNAKE_CASE       =				[sys.executable, '''-c''', '''\n'''.join([load, run] )]

       # should succeed
       __SCREAMING_SNAKE_CASE       =				self.get_env()
       __SCREAMING_SNAKE_CASE       =				subprocess.run(__lowerCamelCase,		env=__lowerCamelCase,		check=__lowerCamelCase,		capture_output=__lowerCamelCase )
       self.assertEqual(result.returncode,		0,		result.stderr )
       self.assertIn("success",		result.stdout.decode() )

       # next emulate no network
       __SCREAMING_SNAKE_CASE       =				[sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]

       # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
       # env["TRANSFORMERS_OFFLINE"] = "0"
       # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
       # self.assertEqual(result.returncode, 1, result.stderr)

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       __SCREAMING_SNAKE_CASE       =				'''1'''
       __SCREAMING_SNAKE_CASE       =				subprocess.run(__lowerCamelCase,		env=__lowerCamelCase,		check=__lowerCamelCase,		capture_output=__lowerCamelCase )
       self.assertEqual(result.returncode,		0,		result.stderr )
       self.assertIn("success",		result.stdout.decode() )






    @require_torch
    def 						__lowerCAmelCase    (       self )   ->					Union[str, Any]:
       __SCREAMING_SNAKE_CASE       =				'''
from transformers import pipeline
        '''
       __SCREAMING_SNAKE_CASE       =				'''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
        '''

       __SCREAMING_SNAKE_CASE       =				'''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
        '''
       __SCREAMING_SNAKE_CASE       =				self.get_env()
       __SCREAMING_SNAKE_CASE       =				'''1'''
       __SCREAMING_SNAKE_CASE       =				[sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
       __SCREAMING_SNAKE_CASE       =				subprocess.run(__lowerCamelCase,		env=__lowerCamelCase,		check=__lowerCamelCase,		capture_output=__lowerCamelCase )
       self.assertEqual(result.returncode,		1,		result.stderr )
       self.assertIn(
           "You cannot infer task automatically within `pipeline` when using offline mode",		result.stderr.decode().replace("\n",		"" ),		)






    @require_torch
    def 						__lowerCAmelCase    (       self )   ->					int:
       __SCREAMING_SNAKE_CASE       =				'''
from transformers import AutoModel
        '''
       __SCREAMING_SNAKE_CASE       =				'''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
        '''

       # baseline - just load from_pretrained with normal network
       __SCREAMING_SNAKE_CASE       =				[sys.executable, '''-c''', '''\n'''.join([load, run] )]

       # should succeed
       __SCREAMING_SNAKE_CASE       =				self.get_env()
       __SCREAMING_SNAKE_CASE       =				subprocess.run(__lowerCamelCase,		env=__lowerCamelCase,		check=__lowerCamelCase,		capture_output=__lowerCamelCase )
       self.assertEqual(result.returncode,		0,		result.stderr )
       self.assertIn("success",		result.stdout.decode() )

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       __SCREAMING_SNAKE_CASE       =				'''1'''
       __SCREAMING_SNAKE_CASE       =				subprocess.run(__lowerCamelCase,		env=__lowerCamelCase,		check=__lowerCamelCase,		capture_output=__lowerCamelCase )
       self.assertEqual(result.returncode,		0,		result.stderr )
       self.assertIn("success",		result.stdout.decode() )





'''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												=						False, False, False




@dataclass
class 				UpperCAmelCase			:
 UpperCAmelCase				=					None
 UpperCAmelCase				=					True
 UpperCAmelCase				=					True
 UpperCAmelCase				=					None
 # Automatically constructed
 UpperCAmelCase				=					"dict"
 UpperCAmelCase				=					pa.struct({"bytes": pa.binary(), "path": pa.string()}						)
 UpperCAmelCase				=					field(default="Audio"						, init=_snake_case						, repr=_snake_case						)


 def __call__(      self							:	Any ):
    return self.pa_type


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	Union[str, bytes, 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(__lowerCamelCase			,       __lowerCamelCase ):
       return {"bytes": None, "path": value}
    elif isinstance(__lowerCamelCase			,       __lowerCamelCase ):
       return {"bytes": value, "path": None}
    elif "array" in value:
       # convert the audio array to wav bytes
       UpperCAmelCase__	:int      =      BytesIO()
       sf.write(__lowerCamelCase			,       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!)
             UpperCAmelCase__	:List[Any]      =      np.frombuffer(value['''bytes''']			,       dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
          else:
             UpperCAmelCase__	:Optional[Any]      =      np.memmap(value['''path''']			,       dtype='''h'''			,       mode='''r''' ).astype(np.floataa ) / 3_2_7_6_7

          UpperCAmelCase__	:Optional[Any]      =      BytesIO(bytes() )
          sf.write(__lowerCamelCase			,       __lowerCamelCase			,       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							:	List[Any]			,       __lowerCamelCase							:	dict			,       __lowerCamelCase							:	Optional[Dict[str, Union[str, bool, None]]] = None ):
    if not self.decode:
       raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )

    UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      (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

    UpperCAmelCase__	:List[str]      =      xsplitext(__lowerCamelCase )[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:
       UpperCAmelCase__	:Optional[Any]      =      token_per_repo_id or {}
       UpperCAmelCase__	:str      =      path.split('''::''' )[-1]
       try:
          UpperCAmelCase__	:Tuple      =      string_to_dict(__lowerCamelCase			,       config.HUB_DATASETS_URL )['''repo_id''']
          UpperCAmelCase__	:str      =      token_per_repo_id[repo_id]
       except (ValueError, KeyError):
          UpperCAmelCase__	:Tuple      =      None

       with xopen(__lowerCamelCase			,       '''rb'''			,       use_auth_token=__lowerCamelCase ) as f:
          UpperCAmelCase__   ,						UpperCAmelCase__	:Union[str, Any]      =      sf.read(__lowerCamelCase )

    else:
       UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      sf.read(__lowerCamelCase )

    UpperCAmelCase__	:Optional[int]      =      array.T
    if self.mono:
       UpperCAmelCase__	:Any      =      librosa.to_mono(__lowerCamelCase )
    if self.sampling_rate and self.sampling_rate != sampling_rate:
       UpperCAmelCase__	:Union[str, Any]      =      librosa.resample(__lowerCamelCase			,       orig_sr=__lowerCamelCase			,       target_sr=self.sampling_rate )
       UpperCAmelCase__	:List[str]      =      self.sampling_rate

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    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							:	Any			,       __lowerCamelCase							:	Union[pa.StringArray, pa.StructArray] ):
    if pa.types.is_string(storage.type ):
       UpperCAmelCase__	:List[str]      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.binary() )
       UpperCAmelCase__	:Tuple      =      pa.StructArray.from_arrays([bytes_array, storage]			,       ['''bytes''', '''path''']			,       mask=storage.is_null() )
    elif pa.types.is_binary(storage.type ):
       UpperCAmelCase__	:str      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.string() )
       UpperCAmelCase__	:int      =      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''' ):
       UpperCAmelCase__	:Any      =      pa.array([Audio().encode_example(__lowerCamelCase ) 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:
          UpperCAmelCase__	:str      =      storage.field('''bytes''' )
       else:
          UpperCAmelCase__	:List[str]      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.binary() )
       if storage.type.get_field_index('''path''' ) >= 0:
          UpperCAmelCase__	:Optional[int]      =      storage.field('''path''' )
       else:
          UpperCAmelCase__	:Optional[int]      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.string() )
       UpperCAmelCase__	:List[str]      =      pa.StructArray.from_arrays([bytes_array, path_array]			,       ['''bytes''', '''path''']			,       mask=storage.is_null() )
    return array_cast(__lowerCamelCase			,       self.pa_type )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	pa.StructArray ):

    @no_op_if_value_is_null
    def path_to_bytes(__lowerCamelCase							:	Dict ):
       with xopen(__lowerCamelCase			,       '''rb''' ) as f:
          UpperCAmelCase__	:Any      =      f.read()
       return bytes_

    UpperCAmelCase__	:Union[str, Any]      =      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()			,       )
    UpperCAmelCase__	:Optional[int]      =      pa.array(
        [os.path.basename(__lowerCamelCase ) if path is not None else None for path in storage.field('''path''' ).to_pylist()]			,       type=pa.string()			,       )
    UpperCAmelCase__	:Optional[int]      =      pa.StructArray.from_arrays([bytes_array, path_array]			,       ['''bytes''', '''path''']			,       mask=bytes_array.is_null() )
    return array_cast(__lowerCamelCase			,       self.pa_type )



