






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
			import tensorflow as tf

if is_keras_nlp_available():
			from transformers.models.gpta import TFGPTaTokenizer


A_		:		Union[str, Any]		     =			['gpt2']
A_		:		Any		     =			'gpt2'

if is_tf_available():




			class     _lowerCAmelCase(	tf.Module ):





										"""simple docstring"""







										def __init__( self  ,      _lowerCamelCase							):
												super().__init__()
												UpperCamelCase_: Optional[Any]   				=    tokenizer
												UpperCamelCase_: List[Any]   				=    AutoConfig.from_pretrained(_UpperCAmelCase							)
												UpperCamelCase_: List[str]   				=    TFGPTaLMHeadModel.from_config(_UpperCAmelCase							)





										@tf.function(input_signature=(tf.TensorSpec((None,)  ,      tf.string  ,      name='text'							),)							)
										def       _a   ( self  ,      _lowerCamelCase							):
												UpperCamelCase_: Optional[int]   				=    self.tokenizer(_UpperCAmelCase							)
												UpperCamelCase_: List[Any]   				=    tokenized['input_ids'].to_tensor()

												UpperCamelCase_: List[Any]   				=    tf.cast(input_ids_dense > 0  ,      tf.intaa							)
												# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

												UpperCamelCase_: Optional[int]   				=    self.model(input_ids=_UpperCAmelCase  ,      attention_mask=_UpperCAmelCase							)['logits']

												return outputs




@require_tf
@require_keras_nlp
class     _lowerCAmelCase(	unittest.TestCase ):





							"""simple docstring"""







							def       _a   ( self							):
									super().setUp()

									UpperCamelCase_: str   				=    [GPTaTokenizer.from_pretrained(_UpperCAmelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
									UpperCamelCase_: int   				=    [TFGPTaTokenizer.from_pretrained(_UpperCAmelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
									assert len(self.tokenizers							) == len(self.tf_tokenizers							)

									UpperCamelCase_: str   				=    [
									    'This is a straightforward English test sentence.',
									    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
									    'Now we\'re going to add some Chinese: 一 二 三 一二三',
									    'And some much more rare Chinese: 齉 堃 齉堃',
									    'Je vais aussi écrire en français pour tester les accents',
									    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
									]
									UpperCamelCase_: Tuple   				=    list(zip(self.test_sentences  ,      self.test_sentences[::-1]							)							)





							def       _a   ( self							):
									for tokenizer, tf_tokenizer in zip(self.tokenizers  ,      self.tf_tokenizers							):
											for test_inputs in self.test_sentences:
													UpperCamelCase_: Union[str, Any]   				=    tokenizer([test_inputs]  ,      return_tensors='tf'							)
													UpperCamelCase_: Any   				=    tf_tokenizer([test_inputs]							)

													for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															UpperCamelCase_: List[str]   				=    python_outputs[key].numpy()
															UpperCamelCase_: Optional[int]   				=    tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
															self.assertTrue(tf.reduce_all(tf.cast(_UpperCAmelCase  ,      tf.intaa							) == tf_outputs_values							)							)





							@slow
							def       _a   ( self							):
									for tf_tokenizer in self.tf_tokenizers:
											UpperCamelCase_: str   				=    tf.function(_UpperCAmelCase							)
											for test_inputs in self.test_sentences:
													UpperCamelCase_: List[str]   				=    tf.constant(_UpperCAmelCase							)
													UpperCamelCase_: Optional[int]   				=    compiled_tokenizer(_UpperCAmelCase							)
													UpperCamelCase_: Tuple   				=    tf_tokenizer(_UpperCAmelCase							)

													for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)





							@slow
							def       _a   ( self							):
									for tf_tokenizer in self.tf_tokenizers:
											UpperCamelCase_: str   				=    ModelToSave(tokenizer=_UpperCAmelCase							)
											UpperCamelCase_: List[Any]   				=    tf.convert_to_tensor([self.test_sentences[0]]							)
											UpperCamelCase_: List[Any]   				=    model.serving(_UpperCAmelCase							)  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
													UpperCamelCase_: Tuple   				=    Path(_UpperCAmelCase							) / 'saved.model'
													tf.saved_model.save(_UpperCAmelCase  ,      _UpperCAmelCase  ,      signatures={'serving_default': model.serving}							)
													UpperCamelCase_: List[str]   				=    tf.saved_model.load(_UpperCAmelCase							)
											UpperCamelCase_: List[str]   				=    loaded_model.signatures['serving_default'](_UpperCAmelCase							)['output_0']
											# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
											self.assertTrue(tf.reduce_all(out == loaded_output							)							)





							@slow
							def       _a   ( self							):
									for tf_tokenizer in self.tf_tokenizers:
											UpperCamelCase_: str   				=    tf.convert_to_tensor([self.test_sentences[0]]							)
											UpperCamelCase_: Optional[Any]   				=    tf_tokenizer(_UpperCAmelCase							)  # Build model with some sample inputs

											UpperCamelCase_: Tuple   				=    tf_tokenizer.get_config()
											UpperCamelCase_: List[str]   				=    TFGPTaTokenizer.from_config(_UpperCAmelCase							)
											UpperCamelCase_: int   				=    model_from_config(_UpperCAmelCase							)

											for key in from_config_output.keys():
													self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





							@slow
							def       _a   ( self							):
									for tf_tokenizer in self.tf_tokenizers:
											# for the test to run
											UpperCamelCase_: List[str]   				=    1_2_3_1_2_3

											for max_length in [3, 5, 1_0_2_4]:
													UpperCamelCase_: List[Any]   				=    tf.convert_to_tensor([self.test_sentences[0]]							)
													UpperCamelCase_: List[Any]   				=    tf_tokenizer(_UpperCAmelCase  ,      max_length=_UpperCAmelCase							)

													UpperCamelCase_: Tuple   				=    out['input_ids'].numpy().shape[1]

													assert out_length == max_length







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

import numpy as np

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.array:
  UpperCamelCase_: Dict   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Any   				=    '1'
  UpperCamelCase_: Any   				=    'f32le'
  UpperCamelCase_: Union[str, Any]   				=    [
      'ffmpeg',
      '-i',
      'pipe:0',
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Tuple:
  UpperCamelCase_: Any   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Union[str, Any]   				=    '1'
  if format_for_conversion == "s16le":
    UpperCamelCase_: Optional[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: Any   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

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

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Any:
  if stream_chunk_s is not None:
    UpperCamelCase_: List[Any]   				=    stream_chunk_s
  else:
    UpperCamelCase_: Dict   				=    chunk_length_s

  UpperCamelCase_: List[str]   				=    ffmpeg_microphone(UpperCAmelCase__     ,   UpperCAmelCase__     ,   format_for_conversion=UpperCAmelCase__  )
  if format_for_conversion == "s16le":
    UpperCamelCase_: Union[str, Any]   				=    np.intaa
    UpperCamelCase_: List[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: str   				=    np.floataa
    UpperCamelCase_: Tuple   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

  if stride_length_s is None:
    UpperCamelCase_: int   				=    chunk_length_s / 6
  UpperCamelCase_: Tuple   				=    int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
  if isinstance(UpperCAmelCase__     ,   (int, float)  ):
    UpperCamelCase_: Union[str, Any]   				=    [stride_length_s, stride_length_s]

  UpperCamelCase_: Any   				=    int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
  UpperCamelCase_: Dict   				=    int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
  UpperCamelCase_: Optional[int]   				=    datetime.datetime.now()
  UpperCamelCase_: Optional[int]   				=    datetime.timedelta(seconds=UpperCAmelCase__  )
  for item in chunk_bytes_iter(UpperCAmelCase__     ,   UpperCAmelCase__     ,   stride=(stride_left, stride_right)     ,   stream=UpperCAmelCase__  ):
    # Put everything back in numpy scale
    UpperCamelCase_: Tuple   				=    np.frombuffer(item['raw']     ,   dtype=UpperCAmelCase__  )
    UpperCamelCase_: Optional[int]   				=    (
        item['stride'][0] // size_of_sample,
        item['stride'][1] // size_of_sample,
    )
    UpperCamelCase_: int   				=    sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 1_0 * delta:
      # We're late !! SKIP
      continue
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = False  )	->    int:
  UpperCamelCase_: str   				=    b''
  UpperCamelCase_				,UpperCamelCase_: Union[str, 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}'''  )
  UpperCamelCase_: List[str]   				=    0
  for raw in iterator:
    acc += raw
    if stream and len(UpperCAmelCase__  ) < chunk_len:
      UpperCamelCase_: Optional[Any]   				=    (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(UpperCAmelCase__  ) >= chunk_len:
        # We are flushing the accumulator
        UpperCamelCase_: int   				=    (_stride_left, stride_right)
        UpperCamelCase_: Optional[Any]   				=    {'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          UpperCamelCase_: Any   				=    False
        yield item
        UpperCamelCase_: Optional[int]   				=    stride_left
        UpperCamelCase_: Optional[Any]   				=    acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(UpperCAmelCase__  ) > stride_left:
    UpperCamelCase_: int   				=    {'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      UpperCamelCase_: Optional[Any]   				=    False
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Any   				=    2**2_4  # 16Mo
  try:
    with subprocess.Popen(UpperCAmelCase__     ,   stdout=subprocess.PIPE     ,   bufsize=UpperCAmelCase__  ) as ffmpeg_process:
      while True:
        UpperCamelCase_: Any   				=    ffmpeg_process.stdout.read(UpperCAmelCase__  )
        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