






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

A        =     logging.get_logger(__name__)



def 					_lowerCamelCase(			lowerCAmelCase__				:		bool    ,    lowerCAmelCase__				:		bool      ):





				'''simple docstring'''







				def run_func(lowerCAmelCase__				:		int      ):
								@wraps(lowerCAmelCase__      )
								def run_in_eager_mode(*lowerCAmelCase__				:		Tuple    ,    **lowerCAmelCase__				:		str      ):
												return func(*lowerCAmelCase__    ,    **lowerCAmelCase__      )

								@wraps(lowerCAmelCase__      )
								@tf.function(experimental_compile=lowerCAmelCase__      )
								def run_in_graph_mode(*lowerCAmelCase__				:		Union[str, Any]    ,    **lowerCAmelCase__				:		Union[str, Any]      ):
												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 					_lowerCamelCase(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   random.Random()
				SCREAMING_SNAKE_CASE_   :	Tuple													=   [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						):





		'''simple docstring'''



		UpperCAmelCase__      :				TensorFlowBenchmarkArguments
		UpperCAmelCase__      :				PretrainedConfig
		UpperCAmelCase__      :				str				    =						"TensorFlow"


		@property
		def   __snake_case			(						self       ):
						return tf.__version__


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						# initialize GPU on separate process
						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_   :	Tuple													=   self._prepare_inference_func(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
						return self._measure_speed(_inference       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						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_   :	Union[str, Any]													=   self._prepare_train_func(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
						return self._measure_speed(_train       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						# initialize GPU on separate process
						if self.args.is_gpu:
										tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.args.strategy
						if strategy is None:
										raise ValueError('A device strategy has to be initialized before using TensorFlow.'       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self._prepare_inference_func(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
						return self._measure_memory(_inference       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if self.args.is_gpu:
										tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,    UpperCamelCase__       )
						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_   :	List[Any]													=   self._prepare_train_func(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
						return self._measure_memory(_train       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.config_dict[model_name]

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

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   (
						    hasattr(UpperCamelCase__ ,    'architectures'       )
						    and isinstance(config.architectures ,    UpperCamelCase__       )
						    and len(config.architectures       ) > 0
						)
						if not self.args.only_pretrain_model and has_model_class_in_config:
										try:
														SCREAMING_SNAKE_CASE_   :	List[str]													=   'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
														SCREAMING_SNAKE_CASE_   :	Tuple													=   __import__('transformers' ,    fromlist=[model_class]       )
														SCREAMING_SNAKE_CASE_   :	List[str]													=   getattr(UpperCamelCase__ ,    UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Tuple													=   model_cls(UpperCamelCase__       )
										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_   :	int													=   TF_MODEL_MAPPING[config.__class__](UpperCamelCase__       )

						# encoder-decoder has vocab size saved differently
						SCREAMING_SNAKE_CASE_   :	Any													=   config.vocab_size if hasattr(UpperCamelCase__ ,    'vocab_size'       ) else config.encoder.vocab_size
						SCREAMING_SNAKE_CASE_   :	str													=   random_input_ids(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )

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

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

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

						return _inference


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   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_   :	Tuple													=   (
						    hasattr(UpperCamelCase__ ,    'architectures'       )
						    and isinstance(config.architectures ,    UpperCamelCase__       )
						    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_   :	str													=   __import__('transformers' ,    fromlist=[model_class]       )
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   getattr(UpperCamelCase__ ,    UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Dict													=   model_cls(UpperCamelCase__       )
										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_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase__       )

						# encoder-decoder has vocab size saved differently
						SCREAMING_SNAKE_CASE_   :	int													=   config.vocab_size if hasattr(UpperCamelCase__ ,    'vocab_size'       ) else config.encoder.vocab_size
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random_input_ids(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )

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

						@run_with_tf_optimizations(self.args.eager_mode ,    self.args.use_xla       )
						def encoder_train():
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model(UpperCamelCase__ ,    labels=UpperCamelCase__ ,    training=UpperCamelCase__       )[0]
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tf.gradients(UpperCamelCase__ ,    model.trainable_variables       )
										return gradients

						SCREAMING_SNAKE_CASE_   :	Tuple													=   encoder_decoder_train if config.is_encoder_decoder else encoder_train

						return _train


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						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(UpperCamelCase__ ,    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(
														    UpperCamelCase__ ,    repeat=self.args.repeat ,    number=10 ,    )

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






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						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_   :	Tuple													=   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_   :	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_   :	Tuple													=   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
																						SCREAMING_SNAKE_CASE_   :	int													=   nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__       )
																						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   meminfo.used
																						SCREAMING_SNAKE_CASE_   :	Tuple													=   Memory(UpperCamelCase__       )
																						# 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_   :	Any													=   None
																		else:
																						SCREAMING_SNAKE_CASE_   :	Any													=   measure_peak_memory_cpu(UpperCamelCase__       )
																						SCREAMING_SNAKE_CASE_   :	Any													=   Memory(UpperCamelCase__       ) if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ) else memory_bytes
														if self.args.trace_memory_line_by_line:
																		SCREAMING_SNAKE_CASE_   :	Optional[int]													=   stop_memory_tracing(UpperCamelCase__       )
																		if memory is None:
																						SCREAMING_SNAKE_CASE_   :	str													=   summary.total
														else:
																		SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   None

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







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A        =			input('Enter image url: ').strip()
				print(f"""Downloading image from {url} ...""")
				A        =			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A        =			soup.find('meta', {'property': 'og:image'})['content']
				A        =			requests.get(image_url).content
				A        =			f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
				with open(file_name, 'wb') as fp:
								fp.write(image_data)
				print(f"""Done. Image saved to disk as {file_name}.""")