import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np







def      _a					(   lowercase__	:      bytes  ,	lowercase__	:      int			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					str      =		f'''{sampling_rate}'''
     SCREAMING_SNAKE_CASE__					:					Any      =		'1'
     SCREAMING_SNAKE_CASE__					:					int      =		'f32le'
     SCREAMING_SNAKE_CASE__					:					Dict      =		[
         'ffmpeg',
         '-i',
         'pipe:0',
         '-ac',
         ac,
         '-ar',
         ar,
         '-f',
         format_for_conversion,
         '-hide_banner',
         '-loglevel',
         'quiet',
         'pipe:1',
     ]

     try:
          with subprocess.Popen(lowercase__  ,	stdin=subprocess.PIPE  ,	stdout=subprocess.PIPE			) as ffmpeg_process:
               SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		ffmpeg_process.communicate(lowercase__			)
     except FileNotFoundError as error:
          raise ValueError('ffmpeg was not found but is required to load audio files from filename'			) from error
     SCREAMING_SNAKE_CASE__					:					Any      =		output_stream[0]
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		np.frombuffer(lowercase__  ,	np.floataa			)
     if audio.shape[0] == 0:
          raise ValueError('Malformed soundfile'			)
     return audio







def      _a					(   lowercase__	:      int  ,	lowercase__	:      float  ,	lowercase__	:      str = "f32le"  ,	):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[str]      =		f'''{sampling_rate}'''
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		'1'
     if format_for_conversion == "s16le":
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		2
     elif format_for_conversion == "f32le":
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		4
     else:
          raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''			)

     SCREAMING_SNAKE_CASE__					:					Dict      =		platform.system()
     if system == "Linux":
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		'alsa'
          SCREAMING_SNAKE_CASE__					:					Any      =		'default'
     elif system == "Darwin":
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		'avfoundation'
          SCREAMING_SNAKE_CASE__					:					Dict      =		':0'
     elif system == "Windows":
          SCREAMING_SNAKE_CASE__					:					int      =		'dshow'
          SCREAMING_SNAKE_CASE__					:					Dict      =		'default'

     SCREAMING_SNAKE_CASE__					:					int      =		[
         'ffmpeg',
         '-f',
         format_,
         '-i',
         input_,
         '-ac',
         ac,
         '-ar',
         ar,
         '-f',
         format_for_conversion,
         '-fflags',
         'nobuffer',
         '-hide_banner',
         '-loglevel',
         'quiet',
         'pipe:1',
     ]
     SCREAMING_SNAKE_CASE__					:					List[str]      =		int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
     SCREAMING_SNAKE_CASE__					:					int      =		_ffmpeg_stream(lowercase__  ,	lowercase__			)
     for item in iterator:
          yield item







def      _a					(   lowercase__	:      int  ,	lowercase__	:      float  ,	lowercase__	:      Optional[int] = None  ,	lowercase__	:      Optional[Union[Tuple[float, float], float]] = None  ,	lowercase__	:      str = "f32le"  ,	):







     '''simple docstring'''


     if stream_chunk_s is not None:
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		stream_chunk_s
     else:
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		chunk_length_s

     SCREAMING_SNAKE_CASE__					:					Any      =		ffmpeg_microphone(lowercase__  ,	lowercase__  ,	format_for_conversion=lowercase__			)
     if format_for_conversion == "s16le":
          SCREAMING_SNAKE_CASE__					:					int      =		np.intaa
          SCREAMING_SNAKE_CASE__					:					Any      =		2
     elif format_for_conversion == "f32le":
          SCREAMING_SNAKE_CASE__					:					int      =		np.floataa
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		4
     else:
          raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''			)

     if stride_length_s is None:
          SCREAMING_SNAKE_CASE__					:					str      =		chunk_length_s / 6
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
     if isinstance(lowercase__  ,	(int, float)			):
          SCREAMING_SNAKE_CASE__					:					List[str]      =		[stride_length_s, stride_length_s]

     SCREAMING_SNAKE_CASE__					:					Any      =		int(round(sampling_rate * stride_length_s[0]			)			) * size_of_sample
     SCREAMING_SNAKE_CASE__					:					Tuple      =		int(round(sampling_rate * stride_length_s[1]			)			) * size_of_sample
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		datetime.datetime.now()
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		datetime.timedelta(seconds=lowercase__			)
     for item in chunk_bytes_iter(lowercase__  ,	lowercase__  ,	stride=(stride_left, stride_right)  ,	stream=lowercase__			):
          # Put everything back in numpy scale
          SCREAMING_SNAKE_CASE__					:					Tuple      =		np.frombuffer(item['raw']  ,	dtype=lowercase__			)
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		(
              item['stride'][0] // size_of_sample,
              item['stride'][1] // size_of_sample,
          )
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		sampling_rate
          audio_time += delta
          if datetime.datetime.now() > audio_time + 10 * delta:
               # We're late !! SKIP
               continue
          yield item







def      _a					(   lowercase__	:      Optional[int]  ,	lowercase__	:      int  ,	lowercase__	:      Tuple[int, int]  ,	lowercase__	:      bool = False			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		b''
     SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Any      =		stride
     if stride_left + stride_right >= chunk_len:
          raise ValueError(
              f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'''			)
     SCREAMING_SNAKE_CASE__					:					int      =		0
     for raw in iterator:
          acc += raw
          if stream and len(lowercase__			) < chunk_len:
               SCREAMING_SNAKE_CASE__					:					int      =		(_stride_left, 0)
               yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
          else:
               while len(lowercase__			) >= chunk_len:
                    # We are flushing the accumulator
                    SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		(_stride_left, stride_right)
                    SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		{'raw': acc[:chunk_len], 'stride': stride}
                    if stream:
                         SCREAMING_SNAKE_CASE__					:					Optional[int]      =		False
                    yield item
                    SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		stride_left
                    SCREAMING_SNAKE_CASE__					:					Tuple      =		acc[chunk_len - stride_left - stride_right :]
    # Last chunk
     if len(lowercase__			) > stride_left:
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		{'raw': acc, 'stride': (_stride_left, 0)}
          if stream:
               SCREAMING_SNAKE_CASE__					:					Optional[int]      =		False
          yield item







def      _a					(   lowercase__	:      List[Any]  ,	lowercase__	:      int			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					int      =		2**24  # 16Mo
     try:
          with subprocess.Popen(lowercase__  ,	stdout=subprocess.PIPE  ,	bufsize=lowercase__			) as ffmpeg_process:
               while True:
                    SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		ffmpeg_process.stdout.read(lowercase__			)
                    if raw == b"":
                         break
                    yield raw
     except FileNotFoundError as error:
          raise ValueError('ffmpeg was not found but is required to stream audio files from filename'			) from error








import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers import AutoModelForCausalLM



@require_torch
class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     List[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											A      						=    TextIteratorStreamer(__UpperCamelCase       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()
											A      						=    ''
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    greedy_ids[:, input_ids.shape[1] :]
											A      						=    tokenizer.decode(new_greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_prompt=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Tuple:
											# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
											# with actual models -- the dummy models' tokenizers are not aligned with their models, and
											# `skip_special_tokens=True` has no effect on them
											A      						=    AutoTokenizer.from_pretrained('distilgpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('distilgpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    torch.ones((1, 5)						, device=__UpperCamelCase       ).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_special_tokens=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=1						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )

											# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
											# re-tokenized, must only contain one token
											A      						=    cs.out[:-1]  # Remove the final "\n"
											A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )
											self.assertEqual(streamer_text_tokenized.input_ids.shape						, (1, 1)       )


				def __UpperCamelCase						(							self      :  Any       )		->     Dict:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    TextIteratorStreamer(__UpperCamelCase						, timeout=0.0_0_1       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()

											# The streamer will timeout after 0.001 seconds, so an exception will be raised
											with self.assertRaises(__UpperCamelCase       ):
																		A      						=    ''
																		for new_text in streamer:
																									streamer_text += new_text