import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments

@require_tf
class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase						)				->				List[str]:
												for model_result in results.values():
																		for batch_size, sequence_length in zip(model_result["""bs"""]			,     model_result["""ss"""]						):
																								UpperCamelCase_		:							List[Any]  =		model_result["""result"""][batch_size][sequence_length]
																								self.assertIsNotNone(__UpperCamelCase						)





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							Union[str, Any]  =		"""sshleifer/tiny-gpt2"""
												UpperCamelCase_		:							Tuple  =		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,     training=__UpperCamelCase			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     eager_mode=__UpperCamelCase			,     multi_process=__UpperCamelCase			,     )
												UpperCamelCase_		:							str  =		TensorFlowBenchmark(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							str  =		"""sgugger/tiny-distilbert-classification"""
												UpperCamelCase_		:							Dict  =		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,     training=__UpperCamelCase			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=__UpperCamelCase			,     only_pretrain_model=__UpperCamelCase			,     )
												UpperCamelCase_		:							Dict  =		TensorFlowBenchmark(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							Dict  =		"""sshleifer/tiny-gpt2"""
												UpperCamelCase_		:							Any  =		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,     training=__UpperCamelCase			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=__UpperCamelCase			,     )
												UpperCamelCase_		:							Any  =		TensorFlowBenchmark(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)





						def        A_							(self						)				->				Optional[int]:
												UpperCamelCase_		:							Union[str, Any]  =		"""sshleifer/tiny-gpt2"""
												UpperCamelCase_		:							Dict  =		AutoConfig.from_pretrained(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,     training=__UpperCamelCase			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     eager_mode=__UpperCamelCase			,     multi_process=__UpperCamelCase			,     )
												UpperCamelCase_		:							Optional[int]  =		TensorFlowBenchmark(__UpperCamelCase			,     [config]						)
												UpperCamelCase_		:							List[Any]  =		benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							List[Any]  =		"""sshleifer/tiny-gpt2"""
												UpperCamelCase_		:							Dict  =		AutoConfig.from_pretrained(__UpperCamelCase						)
												UpperCamelCase_		:							int  =		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,     training=__UpperCamelCase			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=__UpperCamelCase			,     )
												UpperCamelCase_		:							Tuple  =		TensorFlowBenchmark(__UpperCamelCase			,     [config]						)
												UpperCamelCase_		:							int  =		benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							str  =		"""sshleifer/tiny-gpt2"""
												UpperCamelCase_		:							Optional[int]  =		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,     training=__UpperCamelCase			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=__UpperCamelCase			,     )
												UpperCamelCase_		:							Dict  =		TensorFlowBenchmark(__UpperCamelCase						)
												UpperCamelCase_		:							Optional[int]  =		benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							int  =		"""sshleifer/tiny-gpt2"""
												UpperCamelCase_		:							int  =		AutoConfig.from_pretrained(__UpperCamelCase						)
												UpperCamelCase_		:							str  =		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,     training=__UpperCamelCase			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[str]  =		TensorFlowBenchmark(__UpperCamelCase			,     [config]						)
												UpperCamelCase_		:							int  =		benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)





						def        A_							(self						)				->				Optional[int]:
												UpperCamelCase_		:							str  =		"""patrickvonplaten/t5-tiny-random"""
												UpperCamelCase_		:							Dict  =		AutoConfig.from_pretrained(__UpperCamelCase						)
												UpperCamelCase_		:							Optional[Any]  =		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,     training=__UpperCamelCase			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=__UpperCamelCase			,     )
												UpperCamelCase_		:							Union[str, Any]  =		TensorFlowBenchmark(__UpperCamelCase			,     configs=[config]						)
												UpperCamelCase_		:							Optional[int]  =		benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)





						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""						)						) == 0			,     """Cannot do xla on CPU."""						)
						def        A_							(self						)				->				Any:
												UpperCamelCase_		:							List[Any]  =		"""sshleifer/tiny-gpt2"""
												UpperCamelCase_		:							str  =		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,     training=__UpperCamelCase			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     use_xla=__UpperCamelCase			,     multi_process=__UpperCamelCase			,     )
												UpperCamelCase_		:							str  =		TensorFlowBenchmark(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_		:							int  =		"""sshleifer/tiny-gpt2"""
												with tempfile.TemporaryDirectory() as tmp_dir:
																		UpperCamelCase_		:							Optional[Any]  =		TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]			,     inference=__UpperCamelCase			,     save_to_csv=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     inference_time_csv_file=os.path.join(__UpperCamelCase			,     """inf_time.csv"""						)			,     inference_memory_csv_file=os.path.join(__UpperCamelCase			,     """inf_mem.csv"""						)			,     env_info_csv_file=os.path.join(__UpperCamelCase			,     """env.csv"""						)			,     multi_process=__UpperCamelCase			,     )
																		UpperCamelCase_		:							List[str]  =		TensorFlowBenchmark(__UpperCamelCase						)
																		benchmark.run()
																		self.assertTrue(Path(os.path.join(__UpperCamelCase			,     """inf_time.csv"""						)						).exists()						)
																		self.assertTrue(Path(os.path.join(__UpperCamelCase			,     """inf_mem.csv"""						)						).exists()						)
																		self.assertTrue(Path(os.path.join(__UpperCamelCase			,     """env.csv"""						)						).exists()						)





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							str  =		"""sshleifer/tiny-gpt2"""

												def _check_summary_is_not_empty(__UpperCamelCase						):
																		self.assertTrue(hasattr(__UpperCamelCase			,     """sequential"""						)						)
																		self.assertTrue(hasattr(__UpperCamelCase			,     """cumulative"""						)						)
																		self.assertTrue(hasattr(__UpperCamelCase			,     """current"""						)						)
																		self.assertTrue(hasattr(__UpperCamelCase			,     """total"""						)						)

												with tempfile.TemporaryDirectory() as tmp_dir:
																		UpperCamelCase_		:							List[Any]  =		TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]			,     inference=__UpperCamelCase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     log_filename=os.path.join(__UpperCamelCase			,     """log.txt"""						)			,     log_print=__UpperCamelCase			,     trace_memory_line_by_line=__UpperCamelCase			,     eager_mode=__UpperCamelCase			,     multi_process=__UpperCamelCase			,     )
																		UpperCamelCase_		:							Optional[Any]  =		TensorFlowBenchmark(__UpperCamelCase						)
																		UpperCamelCase_		:							List[str]  =		benchmark.run()
																		_check_summary_is_not_empty(result.inference_summary						)
																		self.assertTrue(Path(os.path.join(__UpperCamelCase			,     """log.txt"""						)						).exists()						)







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Tuple         =	{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:int      =			'''encodec'''





						def __init__(self			,     __UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]			,     __UpperCamelCase=24_000			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=128			,     __UpperCamelCase=32			,     __UpperCamelCase=1			,     __UpperCamelCase=[8, 5, 4, 2]			,     __UpperCamelCase="weight_norm"			,     __UpperCamelCase=7			,     __UpperCamelCase=7			,     __UpperCamelCase=3			,     __UpperCamelCase=2			,     __UpperCamelCase=True			,     __UpperCamelCase="reflect"			,     __UpperCamelCase=2			,     __UpperCamelCase=2			,     __UpperCamelCase=1.0			,     __UpperCamelCase=1_024			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				Dict:
												UpperCamelCase_		:							str  =		target_bandwidths
												UpperCamelCase_		:							Tuple  =		sampling_rate
												UpperCamelCase_		:							str  =		audio_channels
												UpperCamelCase_		:							Any  =		normalize
												UpperCamelCase_		:							Union[str, Any]  =		chunk_length_s
												UpperCamelCase_		:							Optional[Any]  =		overlap
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							Dict  =		num_filters
												UpperCamelCase_		:							List[str]  =		num_residual_layers
												UpperCamelCase_		:							Optional[int]  =		upsampling_ratios
												UpperCamelCase_		:							str  =		norm_type
												UpperCamelCase_		:							str  =		kernel_size
												UpperCamelCase_		:							Tuple  =		last_kernel_size
												UpperCamelCase_		:							int  =		residual_kernel_size
												UpperCamelCase_		:							str  =		dilation_growth_rate
												UpperCamelCase_		:							str  =		use_causal_conv
												UpperCamelCase_		:							str  =		pad_mode
												UpperCamelCase_		:							Union[str, Any]  =		compress
												UpperCamelCase_		:							Dict  =		num_lstm_layers
												UpperCamelCase_		:							Optional[int]  =		trim_right_ratio
												UpperCamelCase_		:							int  =		codebook_size
												UpperCamelCase_		:							str  =		codebook_dim if codebook_dim is not None else hidden_size
												UpperCamelCase_		:							Optional[int]  =		use_conv_shortcut

												if self.norm_type not in ["weight_norm", "time_group_norm"]:
																		raise ValueError(
																		    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''						)

												super().__init__(**__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1			,     int((1.0 - self.overlap) * self.chunk_length						)						)





						@property
						def        A_							(self						)				->				int:
												UpperCamelCase_		:							int  =		np.prod(self.upsampling_ratios						)
												return math.ceil(self.sampling_rate / hop_length						)





						@property
						def        A_							(self						)				->				int:
												return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)






