import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
		import tensorflow as tf
		from tensorflow.python.framework.errors_impl import ResourceExhaustedError

		from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
		import pyanvml.pyanvml as nvml

__lowerCamelCase					:	Any       =  logging.get_logger(__name__)




def       _snake_case	(   lowerCAmelCase							:   bool   , lowerCAmelCase							:   bool	):
		"""simple docstring"""







		def run_func(lowerCAmelCase							:   int	):
				@wraps(lowerCAmelCase	)
				def run_in_eager_mode(*lowerCAmelCase							:   Tuple   , **lowerCAmelCase							:   Any	):
						return func(*lowerCAmelCase   , **lowerCAmelCase	)

				@wraps(lowerCAmelCase	)
				@tf.function(experimental_compile=lowerCAmelCase	)
				def run_in_graph_mode(*lowerCAmelCase							:   Union[str, Any]   , **lowerCAmelCase							:   List[str]	):
						return func(*lowerCAmelCase   , **lowerCAmelCase	)

				if do_eager_mode is True:
						if use_xla is not False:
								raise ValueError(
								    "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."	)
						return run_in_eager_mode
				else:
						return run_in_graph_mode

		return run_func




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    random.Random()
		SCREAMING_SNAKE_CASE_  :		str				      =    [rng.randint(0   , vocab_size - 1	) for i in range(batch_size * sequence_length	)]
		return tf.constant(lowerCAmelCase   , shape=(batch_size, sequence_length)   , dtype=tf.intaa	)







class 		a__						(					A__					):
					A	       =							42
					A	       =							42
					A	       =							"TensorFlow"

					@property
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							return tf.__version__

					def 				__UpperCamelCase   (    self      :       int,_A      :       str,_A      :       int,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.args.strategy
							if strategy is None:
									raise ValueError("A device strategy has to be initialized before using TensorFlow."    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    self._prepare_inference_func(_A,_A,_A    )
							return self._measure_speed(_inference    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       str,_A      :       int,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.args.strategy
							if strategy is None:
									raise ValueError("A device strategy has to be initialized before using TensorFlow."    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self._prepare_train_func(_A,_A,_A    )
							return self._measure_speed(_train    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       str,_A      :       int,_A      :       int    ):







							"""simple docstring"""






							if self.args.is_gpu:
									tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx],_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.args.strategy
							if strategy is None:
									raise ValueError("A device strategy has to be initialized before using TensorFlow."    )
							SCREAMING_SNAKE_CASE_  :		str				      =    self._prepare_inference_func(_A,_A,_A    )
							return self._measure_memory(_inference    )

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       str,_A      :       int,_A      :       int    ):







							"""simple docstring"""






							if self.args.is_gpu:
									tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx],_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.args.strategy
							if strategy is None:
									raise ValueError("A device strategy has to be initialized before using TensorFlow."    )

							SCREAMING_SNAKE_CASE_  :		str				      =    self._prepare_train_func(_A,_A,_A    )
							return self._measure_memory(_train    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       str,_A      :       int,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.config_dict[model_name]

							if self.args.fpaa:
									raise NotImplementedError("Mixed precision is currently not supported."    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    (
							    hasattr(_A,"architectures"    )
							    and isinstance(config.architectures,_A    )
							    and len(config.architectures    ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
									try:
											SCREAMING_SNAKE_CASE_  :		List[Any]				      =    "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
											SCREAMING_SNAKE_CASE_  :		List[Any]				      =    __import__("transformers",fromlist=[model_class]    )
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    getattr(_A,_A    )
											SCREAMING_SNAKE_CASE_  :		List[str]				      =    model_cls(_A    )
									except ImportError:
											raise ImportError(
											    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
											    " set `--only_pretrain_model` or `args.only_pretrain_model=True`."    )
							else:
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TF_MODEL_MAPPING[config.__class__](_A    )

							# encoder-decoder has vocab size saved differently
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    config.vocab_size if hasattr(_A,"vocab_size"    ) else config.encoder.vocab_size
							SCREAMING_SNAKE_CASE_  :		str				      =    random_input_ids(_A,_A,_A    )

							@run_with_tf_optimizations(self.args.eager_mode,self.args.use_xla    )
							def encoder_decoder_forward():
									return model(_A,decoder_input_ids=_A,training=_A    )

							@run_with_tf_optimizations(self.args.eager_mode,self.args.use_xla    )
							def encoder_forward():
									return model(_A,training=_A    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

							return _inference

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       str,_A      :       int,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.config_dict[model_name]

							if self.args.eager_mode is not False:
									raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."    )

							if self.args.fpaa:
									raise NotImplementedError("Mixed precision is currently not supported."    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    (
							    hasattr(_A,"architectures"    )
							    and isinstance(config.architectures,_A    )
							    and len(config.architectures    ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
									try:
											SCREAMING_SNAKE_CASE_  :		Dict				      =    "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    __import__("transformers",fromlist=[model_class]    )
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    getattr(_A,_A    )
											SCREAMING_SNAKE_CASE_  :		List[Any]				      =    model_cls(_A    )
									except ImportError:
											raise ImportError(
											    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
											    " set `--only_pretrain_model` or `args.only_pretrain_model=True`."    )
							else:
									SCREAMING_SNAKE_CASE_  :		Any				      =    TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_A    )

							# encoder-decoder has vocab size saved differently
							SCREAMING_SNAKE_CASE_  :		Any				      =    config.vocab_size if hasattr(_A,"vocab_size"    ) else config.encoder.vocab_size
							SCREAMING_SNAKE_CASE_  :		int				      =    random_input_ids(_A,_A,_A    )

							@run_with_tf_optimizations(self.args.eager_mode,self.args.use_xla    )
							def encoder_decoder_train():
									SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A,decoder_input_ids=_A,labels=_A,training=_A    )[0]
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tf.gradients(_A,model.trainable_variables    )
									return gradients

							@run_with_tf_optimizations(self.args.eager_mode,self.args.use_xla    )
							def encoder_train():
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    model(_A,labels=_A,training=_A    )[0]
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    tf.gradients(_A,model.trainable_variables    )
									return gradients

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    encoder_decoder_train if config.is_encoder_decoder else encoder_train

							return _train

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any    ):







							"""simple docstring"""






							with self.args.strategy.scope():
									try:
											if self.args.is_tpu or self.args.use_xla:
													# run additional 10 times to stabilize compilation for tpu
													logger.info("Do inference on TPU. Running model 5 times to stabilize compilation"    )
													timeit.repeat(_A,repeat=1,number=5    )

											# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    timeit.repeat(
											    _A,repeat=self.args.repeat,number=10,)

											return min(_A    ) / 10.0
									except ResourceExhaustedError as e:
											self.print_fn(F'Doesn\'t fit on GPU. {e}'    )






					def 				__UpperCamelCase   (    self      :       List[str],_A      :       Callable[[], None]    ):







							"""simple docstring"""






							logger.info(
							    "Note that TensorFlow allocates more memory than "
							    "it might need to speed up computation. "
							    "The memory reported here corresponds to the memory "
							    "reported by `nvidia-smi`, which can vary depending "
							    "on total available memory on the GPU that is used."    )
							with self.args.strategy.scope():
									try:
											if self.args.trace_memory_line_by_line:
													if not self.args.eager_mode:
															raise ValueError(
															    "`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
															    " consumption line by line."    )
													SCREAMING_SNAKE_CASE_  :		List[str]				      =    start_memory_tracing("transformers"    )

											if self.args.is_tpu:
													# tpu
													raise NotImplementedError(
													    "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
													    " with `args.memory=False`"    )
											elif self.args.is_gpu:
													# gpu
													if not is_pyanvml_available():
															logger.warning(
															    "py3nvml not installed, we won't log GPU memory usage. "
															    "Install py3nvml (pip install py3nvml) to log information about GPU."    )
															SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "N/A"
													else:
															logger.info(
															    "Measuring total GPU usage on GPU device. Make sure to not have additional processes"
															    " running on the same GPU."    )
															# init nvml
															nvml.nvmlInit()
															func()
															SCREAMING_SNAKE_CASE_  :		str				      =    nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
															SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    nvml.nvmlDeviceGetMemoryInfo(_A    )
															SCREAMING_SNAKE_CASE_  :		Any				      =    meminfo.used
															SCREAMING_SNAKE_CASE_  :		List[Any]				      =    Memory(_A    )
															# shutdown nvml
															nvml.nvmlShutdown()
											else:
													# cpu
													if self.args.trace_memory_line_by_line:
															logger.info(
															    "When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
															    " TensorFlow."    )
															SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    None
													else:
															SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    measure_peak_memory_cpu(_A    )
															SCREAMING_SNAKE_CASE_  :		str				      =    Memory(_A    ) if isinstance(_A,_A    ) else memory_bytes
											if self.args.trace_memory_line_by_line:
													SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    stop_memory_tracing(_A    )
													if memory is None:
															SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    summary.total
											else:
													SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    None

											return memory, summary
									except ResourceExhaustedError as e:
											self.print_fn(F'Doesn\'t fit on GPU. {e}'    )
											return "N/A", None




'''simple docstring'''

import unittest

from transformers import DonutProcessor


lowerCamelCase							:   Tuple  						=	'naver-clova-ix/donut-base'
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= DonutProcessor.from_pretrained(UpperCamelCase )






      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= {
                 '''name''': '''John Doe''',
                 '''age''': '''99''',
                 '''city''': '''Atlanta''',
                 '''state''': '''GA''',
                 '''zip''': '''30301''',
                 '''phone''': '''123-4567''',
                 '''nicknames''': [{'''nickname''': '''Johnny'''}, {'''nickname''': '''JD'''}],
             }

             lowercase__      			= (
                 '''<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'''
                 '''<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'''
                 '''<s_nicknames><s_nickname>Johnny</s_nickname>'''
                 '''<sep/><s_nickname>JD</s_nickname></s_nicknames>'''
             )
             lowercase__      			= self.processor.tokenajson(UpperCamelCase )

             self.assertDictEqual(UpperCamelCase   ,						UpperCamelCase )




