


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

_lowercase							:   int						=						logging.get_logger(__name__)





def 	_lowerCAmelCase					( UpperCamelCase__:  bool				,       UpperCamelCase__:  bool    )			-> Optional[int]:





	"""simple docstring"""







	def run_func(UpperCamelCase__:  Optional[int]    ):
		@wraps(UpperCamelCase__    )
		def run_in_eager_mode(*UpperCamelCase__:  Tuple				,       **UpperCamelCase__:  str    ):
			return func(*UpperCamelCase__				,       **UpperCamelCase__    )

		@wraps(UpperCamelCase__    )
		@tf.function(experimental_compile=UpperCamelCase__    )
		def run_in_graph_mode(*UpperCamelCase__:  Optional[Any]				,       **UpperCamelCase__:  Tuple    ):
			return func(*UpperCamelCase__				,       **UpperCamelCase__    )

		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					( UpperCamelCase__:  int				,       UpperCamelCase__:  int				,       UpperCamelCase__:  int    )			-> ["tf.Tensor"]:





	"""simple docstring"""







	A             =					random.Random()
	A             =					[rng.randint(0				,       vocab_size - 1    ) for i in range(batch_size * sequence_length    )]
	return tf.constant(UpperCamelCase__				,       shape=(batch_size, sequence_length)				,       dtype=tf.intaa    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			42
				lowerCAmelCase					=			42
				lowerCAmelCase					=			"TensorFlow"

				@property
				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					return tf.__version__

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					float:
					# initialize GPU on separate process
					A             =					self.args.strategy
					if strategy is None:
						raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
					A             =					self._prepare_inference_func(a__					, a__					, a__ )
					return self._measure_speed(_inference )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					float:
					A             =					self.args.strategy
					if strategy is None:
						raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
					A             =					self._prepare_train_func(a__					, a__					, a__ )
					return self._measure_speed(_train )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					[Memory, Optional[MemorySummary]]:
					# initialize GPU on separate process
					if self.args.is_gpu:
						tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					, a__ )
					A             =					self.args.strategy
					if strategy is None:
						raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
					A             =					self._prepare_inference_func(a__					, a__					, a__ )
					return self._measure_memory(_inference )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					[Memory, Optional[MemorySummary]]:
					if self.args.is_gpu:
						tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					, a__ )
					A             =					self.args.strategy
					if strategy is None:
						raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )

					A             =					self._prepare_train_func(a__					, a__					, a__ )
					return self._measure_memory(_train )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					Callable[[], None]:
					A             =					self.config_dict[model_name]

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

					A             =					(
					    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:
							A             =					"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
							A             =					__import__("""transformers"""					, fromlist=[model_class] )
							A             =					getattr(a__					, a__ )
							A             =					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:
						A             =					TF_MODEL_MAPPING[config.__class__](a__ )

					# encoder-decoder has vocab size saved differently
					A             =					config.vocab_size if hasattr(a__					, """vocab_size""" ) else config.encoder.vocab_size
					A             =					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__ )

					A             =					encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

					return _inference

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					Callable[[], None]:
					A             =					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.""" )

					A             =					(
					    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:
							A             =					"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
							A             =					__import__("""transformers"""					, fromlist=[model_class] )
							A             =					getattr(a__					, a__ )
							A             =					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:
						A             =					TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a__ )

					# encoder-decoder has vocab size saved differently
					A             =					config.vocab_size if hasattr(a__					, """vocab_size""" ) else config.encoder.vocab_size
					A             =					random_input_ids(a__					, a__					, a__ )

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

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

					A             =					encoder_decoder_train if config.is_encoder_decoder else encoder_train

					return _train

				def   _UpperCAmelCase       (    self					, a__ )						->					float:
					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
							A             =					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					, a__ )						->					[Memory, MemorySummary]:
					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.""" )
								A             =					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.""" )
									A             =					"""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()
									A             =					nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
									A             =					nvml.nvmlDeviceGetMemoryInfo(a__ )
									A             =					meminfo.used
									A             =					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.""" )
									A             =					None
								else:
									A             =					measure_peak_memory_cpu(a__ )
									A             =					Memory(a__ ) if isinstance(a__					, a__ ) else memory_bytes
							if self.args.trace_memory_line_by_line:
								A             =					stop_memory_tracing(a__ )
								if memory is None:
									A             =					summary.total
							else:
								A             =					None

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







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowercase							:   Union[str, Any]						=						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class    _UpperCamelCase     (       nn.Module					):

				"""simple docstring"""







				def __init__(    self					, a__ )						->					Dict:
					super().__init__()
					A             =					torchvision.models.resnetaaa(pretrained=a__ )
					A             =					list(model.children() )[:-2]
					A             =					nn.Sequential(*a__ )
					A             =					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )

				def   _UpperCAmelCase       (    self					, a__ )						->					List[str]:
					# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
					A             =					self.pool(self.model(a__ ) )
					A             =					torch.flatten(a__					, start_dim=2 )
					A             =					out.transpose(1					, 2 ).contiguous()
					return out  # BxNx2048






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					[json.loads(a__ ) for l in open(a__ )]
					A             =					os.path.dirname(a__ )
					A             =					tokenizer
					A             =					labels
					A             =					len(a__ )
					A             =					max_seq_length

					A             =					transforms

				def __len__(    self )						->					Tuple:
					return len(self.data )

				def __getitem__(    self					, a__ )						->					Optional[int]:
					A             =					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					, add_special_tokens=a__ ) )
					A       ,      A       ,      A             =					sentence[0], sentence[1:-1], sentence[-1]
					A             =					sentence[: self.max_seq_length]

					A             =					torch.zeros(self.n_classes )
					A             =					1

					A             =					Image.open(os.path.join(self.data_dir					, self.data[index]["""img"""] ) ).convert("""RGB""" )
					A             =					self.transforms(a__ )

					return {
					    "image_start_token": start_token,
					    "image_end_token": end_token,
					    "sentence": sentence,
					    "image": image,
					    "label": label,
					}

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					Counter()
					for row in self.data:
						label_freqs.update(row["""label"""] )
					return label_freqs





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> List[str]:





	"""simple docstring"""







	A             =					[len(row["""sentence"""]    ) for row in batch]
	A       ,      A             =					len(UpperCamelCase__    ), max(UpperCamelCase__    )

	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )
	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )

	for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__				,       UpperCamelCase__    )    ):
		A             =					input_row["""sentence"""]
		A             =					1

	A             =					torch.stack([row["""image"""] for row in batch]    )
	A             =					torch.stack([row["""label"""] for row in batch]    )
	A             =					torch.stack([row["""image_start_token"""] for row in batch]    )
	A             =					torch.stack([row["""image_end_token"""] for row in batch]    )

	return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 	_lowerCAmelCase					( )			-> Union[str, Any]:





	"""simple docstring"""







	return [
	    "Crime",
	    "Drama",
	    "Thriller",
	    "Action",
	    "Comedy",
	    "Romance",
	    "Documentary",
	    "Short",
	    "Mystery",
	    "History",
	    "Family",
	    "Adventure",
	    "Fantasy",
	    "Sci-Fi",
	    "Western",
	    "Horror",
	    "Sport",
	    "War",
	    "Music",
	    "Musical",
	    "Animation",
	    "Biography",
	    "Film-Noir",
	]





def 	_lowerCAmelCase					( )			-> Optional[Any]:





	"""simple docstring"""







	return transforms.Compose(
	    [
	        transforms.Resize(2_56    ),
	        transforms.CenterCrop(2_24    ),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]				,       std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]				,       ),
	    ]    )



