

"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import EfficientNetImageProcessor


class       _snake_case  (    unittest.TestCase							):







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=1_3  ,				UpperCAmelCase_=3  ,				UpperCAmelCase_=1_8  ,				UpperCAmelCase_=3_0  ,				UpperCAmelCase_=4_0_0  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=[0.5, 0.5, 0.5]  ,				UpperCAmelCase_=[0.5, 0.5, 0.5]  ,				)      ->    Any:
											__lowercase        =      size if size is not None else {'''height''': 1_8, '''width''': 1_8}
											__lowercase        =      parent
											__lowercase        =      batch_size
											__lowercase        =      num_channels
											__lowercase        =      image_size
											__lowercase        =      min_resolution
											__lowercase        =      max_resolution
											__lowercase        =      do_resize
											__lowercase        =      size
											__lowercase        =      do_normalize
											__lowercase        =      image_mean
											__lowercase        =      image_std







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											}



@require_torch
@require_vision
class       _snake_case  (    lowerCamelCase_    ,      unittest.TestCase							):
						__snake_case			      =  EfficientNetImageProcessor if is_vision_available() else None







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											__lowercase        =      EfficientNetImageProcessorTester(self				)







						@property
						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											return self.image_processor_tester.prepare_image_processor_dict()







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''image_mean'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''image_std'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''do_normalize'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''do_resize'''				)				)
											self.assertTrue(hasattr(UpperCAmelCase_  ,				'''size'''				)				)







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      self.image_processing_class.from_dict(self.image_processor_dict				)
											self.assertEqual(image_processor.size  ,				{'''height''': 1_8, '''width''': 1_8}				)

											__lowercase        =      self.image_processing_class.from_dict(self.image_processor_dict  ,				size=4_2				)
											self.assertEqual(image_processor.size  ,				{'''height''': 4_2, '''width''': 4_2}				)







						def 							_UpperCamelCase     (self				)      ->    Any:
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											__lowercase        =      prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=UpperCAmelCase_				)
											for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_  ,				Image.Image				)

											# Test not batched input
											__lowercase        =      image_processing(image_inputs[0]  ,				return_tensors='''pt'''				).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )  ,				)

											# Test batched
											__lowercase        =      image_processing(UpperCAmelCase_  ,				return_tensors='''pt'''				).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )  ,				)







						def 							_UpperCamelCase     (self				)      ->    List[str]:
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											# create random numpy tensors
											__lowercase        =      prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=UpperCAmelCase_  ,				numpify=UpperCAmelCase_				)
											for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_  ,				np.ndarray				)

											# Test not batched input
											__lowercase        =      image_processing(image_inputs[0]  ,				return_tensors='''pt'''				).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )  ,				)

											# Test batched
											__lowercase        =      image_processing(UpperCAmelCase_  ,				return_tensors='''pt'''				).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )  ,				)







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											# create random PyTorch tensors
											__lowercase        =      prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=UpperCAmelCase_  ,				torchify=UpperCAmelCase_				)
											for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_  ,				torch.Tensor				)

											# Test not batched input
											__lowercase        =      image_processing(image_inputs[0]  ,				return_tensors='''pt'''				).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )  ,				)

											# Test batched
											__lowercase        =      image_processing(UpperCAmelCase_  ,				return_tensors='''pt'''				).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )  ,				)








'''simple docstring'''






import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
      import torch

      from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
      import pyanvml.pyanvml as nvml


lowercase						:   List[Any]      						=							logging.get_logger(__name__)

class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  PyTorchBenchmarkArguments
 __snake_case						:  PretrainedConfig
 __snake_case						:  str       =   "PyTorch"



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return torch.__version__



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		str    =   self._prepare_inference_func(A ,		A ,		A			)
        return self._measure_speed(_inference			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		int    =   self._prepare_inference_func(A ,		A ,		A			)
        return self._measure_memory(_inference			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Union[str, Any]    =   self._prepare_train_func(A ,		A ,		A			)
        return self._measure_speed(_train			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Any    =   self._prepare_train_func(A ,		A ,		A			)
        return self._measure_memory(_train			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Any    =   self.config_dict[model_name]

        if self.args.torchscript:
               lowercase	:		Any    =   True

        lowercase	:		Union[str, Any]    =   (
            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:
                      lowercase	:		Any    =   config.architectures[0]
                      lowercase	:		Dict    =   __import__('''transformers''' ,		fromlist=[model_class]			)
                      lowercase	:		List[str]    =   getattr(A ,		A			)
                      lowercase	:		Dict    =   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:
               lowercase	:		Optional[int]    =   MODEL_MAPPING[config.__class__](A			)

        model.eval()
        model.to(self.args.device			)

        # encoder-decoder has vocab size saved differently
        lowercase	:		str    =   config.vocab_size if hasattr(A ,		'''vocab_size'''			) else config.encoder.vocab_size
        lowercase	:		Union[str, Any]    =   torch.randint(A ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device			)

        if self.args.fpaa:
               logger.info('''Running training in Mixed Precision...'''			)
               if not self.args.is_gpu:
                      raise ValueError('''Mixed precision is possible only for GPU.'''			)
               # amp seems to have memory leaks so that memory usage
               # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
               model.half()

        if self.args.torchscript:
               with torch.no_grad():
                      lowercase	:		int    =   torch.jit.trace(A ,		A			)
        else:
               lowercase	:		Any    =   model

        def encoder_decoder_forward():
               with torch.no_grad():
                      lowercase	:		int    =   inference_model(A ,		decoder_input_ids=A			)
               return outputs

        def encoder_forward():
               with torch.no_grad():
                      lowercase	:		Tuple    =   inference_model(A			)
               return outputs

        lowercase	:		int    =   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




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

        lowercase	:		Optional[Any]    =   (
            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:
                      lowercase	:		str    =   config.architectures[0]
                      lowercase	:		List[str]    =   __import__('''transformers''' ,		fromlist=[model_class]			)
                      lowercase	:		int    =   getattr(A ,		A			)
                      lowercase	:		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:
               lowercase	:		Union[str, Any]    =   MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A			)

        if self.args.torchscript:
               raise NotImplementedError('''Training for torchscript is currently not implemented'''			)
        else:
               lowercase	:		Dict    =   model

        model.train()
        model.to(self.args.device			)

        # encoder-decoder has vocab size saved differently
        lowercase	:		Union[str, Any]    =   config.vocab_size if hasattr(A ,		'''vocab_size'''			) else config.encoder.vocab_size
        lowercase	:		List[Any]    =   torch.randint(A ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device			)

        if self.args.fpaa:
               logger.info('''Running training in Mixed Precision...'''			)
               if not self.args.is_gpu:
                      raise ValueError('''Mixed precision is possible only for GPU.'''			)

               # amp seems to have memory leaks so that memory usage
               # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
               model.half()

        def compute_loss_and_backprob_encoder():
               lowercase	:		Optional[Any]    =   train_model(A ,		labels=A			)[0]
               loss.backward()
               return loss

        def compute_loss_and_backprob_encoder_decoder():
               lowercase	:		List[Any]    =   train_model(A ,		decoder_input_ids=A ,		labels=A			)[0]
               loss.backward()
               return loss

        lowercase	:		Any    =   (
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        try:
               if self.args.is_tpu or self.args.torchscript:
                      # run additional 10 times to stabilize compilation for tpu and torchscript
                      logger.info('''Do inference on TPU or torchscript. 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
               lowercase	:		str    =   timeit.repeat(
                   A ,		repeat=self.args.repeat ,		number=10 ,		)

               if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                      import torch_xla.debug.metrics as met

                      self.print_fn(met.metrics_report()			)

               return min(A			) / 1_0.0
        except RuntimeError as e:
               self.print_fn(f"""Doesn't fit on GPU. {e}"""			)
               return "N/A"





 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        try:
               if self.args.trace_memory_line_by_line:
                      lowercase	:		int    =   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'''
                          ''' `--no-memory` or `args.memory=False`'''			)
               elif self.args.is_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.'''			)
                             lowercase	:		str    =   '''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()
                             lowercase	:		List[Any]    =   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
                             lowercase	:		List[Any]    =   nvml.nvmlDeviceGetMemoryInfo(A			)
                             lowercase	:		int    =   meminfo.used
                             lowercase	:		Dict    =   Memory(A			)
                             # shutdown nvml
                             nvml.nvmlShutdown()
               else:
                      # cpu
                      lowercase	:		Optional[Any]    =   measure_peak_memory_cpu(A			)
                      lowercase	:		Union[str, Any]    =   Memory(A			) if isinstance(A ,		A			) else memory_bytes

               if self.args.trace_memory_line_by_line:
                      lowercase	:		Tuple    =   stop_memory_tracing(A			)
               else:
                      lowercase	:		int    =   None

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




