



"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel




@require_tf
class       snake_case_    :
							__lowerCAmelCase            =       MBartConfig
							__lowerCAmelCase            =       {}
							__lowerCAmelCase            =       "gelu"






							def __init__( self      ,  a_      ,  a_=1_3      ,  a_=7      ,  a_=True      ,  a_=False      ,  a_=9_9      ,  a_=3_2      ,  a_=2      ,  a_=4      ,  a_=3_7      ,  a_=0.1      ,  a_=0.1      ,  a_=2_0      ,  a_=2      ,  a_=1      ,  a_=0      ,  ):
									a_  :   List[str]			    =  parent
									a_  :   Tuple			    =  batch_size
									a_  :   Optional[Any]			    =  seq_length
									a_  :   Optional[Any]			    =  is_training
									a_  :   int			    =  use_labels
									a_  :   Optional[int]			    =  vocab_size
									a_  :   int			    =  hidden_size
									a_  :   List[Any]			    =  num_hidden_layers
									a_  :   int			    =  num_attention_heads
									a_  :   Optional[int]			    =  intermediate_size
									a_  :   int			    =  hidden_dropout_prob
									a_  :   Union[str, Any]			    =  attention_probs_dropout_prob
									a_  :   Union[str, Any]			    =  max_position_embeddings
									a_  :   Any			    =  eos_token_id
									a_  :   int			    =  pad_token_id
									a_  :   Tuple			    =  bos_token_id






							def snake_case_	( self   ):
									a_  :   Union[str, Any]			    =  ids_tensor([self.batch_size, self.seq_length - 1]      ,  self.vocab_size   )
									a_  :   List[Any]			    =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )      ,  1   )
									a_  :   Union[str, Any]			    =  tf.concat([input_ids, eos_tensor]      ,  axis=1   )

									a_  :   int			    =  ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size   )

									a_  :   Dict			    =  self.config_cls(
									    vocab_size=self.vocab_size      ,  d_model=self.hidden_size      ,  encoder_layers=self.num_hidden_layers      ,  decoder_layers=self.num_hidden_layers      ,  encoder_attention_heads=self.num_attention_heads      ,  decoder_attention_heads=self.num_attention_heads      ,  encoder_ffn_dim=self.intermediate_size      ,  decoder_ffn_dim=self.intermediate_size      ,  dropout=self.hidden_dropout_prob      ,  attention_dropout=self.attention_probs_dropout_prob      ,  max_position_embeddings=self.max_position_embeddings      ,  eos_token_ids=[2]      ,  bos_token_id=self.bos_token_id      ,  pad_token_id=self.pad_token_id      ,  decoder_start_token_id=self.pad_token_id      ,  **self.config_updates      ,  )
									a_  :   Optional[Any]			    =  prepare_mbart_inputs_dict(a_      ,  a_      ,  a_   )
									return config, inputs_dict






							def snake_case_	( self      ,  a_      ,  a_   ):
									a_  :   List[str]			    =  TFMBartModel(config=a_   ).get_decoder()
									a_  :   List[str]			    =  inputs_dict["input_ids"]

									a_  :   int			    =  input_ids[:1, :]
									a_  :   Optional[Any]			    =  inputs_dict["attention_mask"][:1, :]
									a_  :   int			    =  inputs_dict["head_mask"]
									a_  :   Dict			    =  1

									# first forward pass
									a_  :   Tuple			    =  model(a_      ,  attention_mask=a_      ,  head_mask=a_      ,  use_cache=a_   )

									a_    ,					a_  :   Tuple			    =  outputs.to_tuple()
									a_  :   Union[str, Any]			    =  past_key_values[1]



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__=None,			SCREAMING_SNAKE_CASE__=None,			SCREAMING_SNAKE_CASE__=None,			SCREAMING_SNAKE_CASE__=None,			SCREAMING_SNAKE_CASE__=None,			)      ->   str:






		if attention_mask is None:
				a_  :   List[str]			    =  tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__,			config.pad_token_id						),			tf.inta						)
		if decoder_attention_mask is None:
				a_  :   Dict			    =  tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape,			dtype=tf.inta						),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],			config.pad_token_id						),			tf.inta						),
				    ],			axis=-1,			)
		if head_mask is None:
				a_  :   Optional[Any]			    =  tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
		if decoder_head_mask is None:
				a_  :   str			    =  tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
		if cross_attn_head_mask is None:
				a_  :   Optional[Any]			    =  tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}




@require_tf
class       snake_case_    (    __lowercase  ,__lowercase  ,unittest.TestCase					):
							__lowerCAmelCase            =       (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							__lowerCAmelCase            =       (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							__lowerCAmelCase            =       (
							    {
							        "conversational": TFMBartForConditionalGeneration,
							        "feature-extraction": TFMBartModel,
							        "summarization": TFMBartForConditionalGeneration,
							        "text2text-generation": TFMBartForConditionalGeneration,
							        "translation": TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							__lowerCAmelCase            =       True
							__lowerCAmelCase            =       False
							__lowerCAmelCase            =       False






							def snake_case_	( self      ,  a_      ,  a_      ,  a_      ,  a_      ,  a_   ):

									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
											# Exception encountered when calling layer '...'
											return True

									return False






							def snake_case_	( self   ):
									a_  :   Optional[int]			    =  TFMBartModelTester(self   )
									a_  :   Union[str, Any]			    =  ConfigTester(self      ,  config_class=a_   )






							def snake_case_	( self   ):
									self.config_tester.run_common_tests()






							def snake_case_	( self   ):
									a_  :   Optional[int]			    =  self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*a_   )




@require_sentencepiece
@require_tokenizers
@require_tf
class       snake_case_    (    unittest.TestCase					):
							__lowerCAmelCase            =       [
							    " UN Chief Says There Is No Military Solution in Syria",
							]
							__lowerCAmelCase            =       [
							    "Şeful ONU declară că nu există o soluţie militară în Siria",
							]
							__lowerCAmelCase            =       "facebook/mbart-large-en-ro"






							@cached_property
							def snake_case_	( self   ):
									return AutoTokenizer.from_pretrained(self.model_name   )






							@cached_property
							def snake_case_	( self   ):
									a_  :   Optional[Any]			    =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
									return model






							def snake_case_	( self      ,  **a_   ):
									a_  :   Optional[Any]			    =  self.translate_src_text(**a_   )
									self.assertListEqual(self.expected_text      ,  a_   )






							def snake_case_	( self      ,  **a_   ):
									a_  :   Tuple			    =  self.tokenizer(self.src_text      ,  **a_      ,  return_tensors="tf"   )
									a_  :   Any			    =  self.model.generate(
									    model_inputs.input_ids      ,  attention_mask=model_inputs.attention_mask      ,  num_beams=2   )
									a_  :   str			    =  self.tokenizer.batch_decode(a_      ,  skip_special_tokens=a_   )
									return generated_words






							@slow
							def snake_case_	( self   ):
									self._assert_generated_batch_equal_expected()

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

import numpy as np


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					np.array:

		"""simple docstring"""






		_UpperCamelCase         =						F'{sampling_rate}'
		_UpperCamelCase         =						"""1"""
		_UpperCamelCase         =						"""f32le"""
		_UpperCamelCase         =						[
		    """ffmpeg""",
		    """-i""",
		    """pipe:0""",
		    """-ac""",
		    ac,
		    """-ar""",
		    ar,
		    """-f""",
		    format_for_conversion,
		    """-hide_banner""",
		    """-loglevel""",
		    """quiet""",
		    """pipe:1""",
		]

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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = "f32le"    ,	)		->					Union[str, Any]:

		"""simple docstring"""






		_UpperCamelCase         =						F'{sampling_rate}'
		_UpperCamelCase         =						"""1"""
		if format_for_conversion == "s16le":
				_UpperCamelCase         =						2
		elif format_for_conversion == "f32le":
				_UpperCamelCase         =						4
		else:
				raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'			)

		_UpperCamelCase         =						platform.system()
		if system == "Linux":
				_UpperCamelCase         =						"""alsa"""
				_UpperCamelCase         =						"""default"""
		elif system == "Darwin":
				_UpperCamelCase         =						"""avfoundation"""
				_UpperCamelCase         =						""":0"""
		elif system == "Windows":
				_UpperCamelCase         =						"""dshow"""
				_UpperCamelCase         =						"""default"""

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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = "f32le"    ,	)		->					Any:

		"""simple docstring"""






		if stream_chunk_s is not None:
				_UpperCamelCase         =						stream_chunk_s
		else:
				_UpperCamelCase         =						chunk_length_s

		_UpperCamelCase         =						ffmpeg_microphone(lowerCAmelCase    ,	lowerCAmelCase    ,	format_for_conversion=lowerCAmelCase			)
		if format_for_conversion == "s16le":
				_UpperCamelCase         =						np.intaa
				_UpperCamelCase         =						2
		elif format_for_conversion == "f32le":
				_UpperCamelCase         =						np.floataa
				_UpperCamelCase         =						4
		else:
				raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'			)

		if stride_length_s is None:
				_UpperCamelCase         =						chunk_length_s / 6
		_UpperCamelCase         =						int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
		if isinstance(lowerCAmelCase    ,	(int, float)			):
				_UpperCamelCase         =						[stride_length_s, stride_length_s]

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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = False			)		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						b""""""
		_UpperCamelCase     ,						_UpperCamelCase         =						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         =						0
		for raw in iterator:
				acc += raw
				if stream and len(lowerCAmelCase			) < chunk_len:
						_UpperCamelCase         =						(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(lowerCAmelCase			) >= chunk_len:
								# We are flushing the accumulator
								_UpperCamelCase         =						(_stride_left, stride_right)
								_UpperCamelCase         =						{"""raw""": acc[:chunk_len], """stride""": stride}
								if stream:
										_UpperCamelCase         =						False
								yield item
								_UpperCamelCase         =						stride_left
								_UpperCamelCase         =						acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(lowerCAmelCase			) > stride_left:
				_UpperCamelCase         =						{"""raw""": acc, """stride""": (_stride_left, 0)}
				if stream:
						_UpperCamelCase         =						False
				yield item







def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						2**2_4  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase    ,	stdout=subprocess.PIPE    ,	bufsize=lowerCAmelCase			) as ffmpeg_process:
						while True:
								_UpperCamelCase         =						ffmpeg_process.stdout.read(lowerCAmelCase			)
								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



