

'''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 ViltImageProcessor







class lowerCamelCase   (    unittest.TestCase				):

				def __init__(       self,						lowercase_,						lowercase_=7,						lowercase_=3,						lowercase_=18,						lowercase_=30,						lowercase_=400,						lowercase_=True,						lowercase_=None,						lowercase_=2,						lowercase_=True,						lowercase_=[0.5, 0.5, 0.5],						lowercase_=[0.5, 0.5, 0.5],						)    ->							Optional[Any]:
					snake_case							=					size if size is not None else {'shortest_edge': 30}
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					num_channels
					snake_case							=					image_size
					snake_case							=					min_resolution
					snake_case							=					max_resolution
					snake_case							=					do_resize
					snake_case							=					size
					snake_case							=					size_divisor
					snake_case							=					do_normalize
					snake_case							=					image_mean
					snake_case							=					image_std

				def 					_lowerCamelCase    (       self )    ->							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,
					    "size_divisor": self.size_divisor,
					}






				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_=False )    ->							Optional[int]:
					if not batched:
						snake_case							=					self.size['shortest_edge']
						snake_case							=					image_inputs[0]
						if isinstance(lowercase_,						Image.Image ):
							snake_case			,			snake_case							=					image.size
						else:
							snake_case			,			snake_case							=					image.shape[1], image.shape[2]
						snake_case							=					size / min(lowercase_,						lowercase_ )
						if h < w:
							snake_case			,			snake_case							=					size, scale * w
						else:
							snake_case			,			snake_case							=					scale * h, size

						snake_case							=					int((1333 / 800) * size )
						if max(lowercase_,						lowercase_ ) > max_size:
							snake_case							=					max_size / max(lowercase_,						lowercase_ )
							snake_case							=					newh * scale
							snake_case							=					neww * scale

						snake_case			,			snake_case							=					int(newh + 0.5 ), int(neww + 0.5 )
						snake_case			,			snake_case							=					(
						    newh // self.size_divisor * self.size_divisor,
						    neww // self.size_divisor * self.size_divisor,
						)

					else:
						snake_case							=					[]
						for image in image_inputs:
							snake_case			,			snake_case							=					self.get_expected_values([image] )
							expected_values.append((expected_height, expected_width) )
						snake_case							=					max(lowercase_,						key=lambda lowercase_ : item[0] )[0]
						snake_case							=					max(lowercase_,						key=lambda lowercase_ : item[1] )[1]

					return expected_height, expected_width







@require_torch
@require_vision
class lowerCamelCase   (    __lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = ViltImageProcessor if is_vision_available() else None

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					ViltImageProcessingTester(self )

				@property
				def 					_lowerCamelCase    (       self )    ->							int:
					return self.image_processor_tester.prepare_image_processor_dict()

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(lowercase_,						'image_mean' ) )
					self.assertTrue(hasattr(lowercase_,						'image_std' ) )
					self.assertTrue(hasattr(lowercase_,						'do_normalize' ) )
					self.assertTrue(hasattr(lowercase_,						'do_resize' ) )
					self.assertTrue(hasattr(lowercase_,						'size' ) )
					self.assertTrue(hasattr(lowercase_,						'size_divisor' ) )

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size,						{'shortest_edge': 30} )

					snake_case							=					self.image_processing_class.from_dict(self.image_processor_dict,						size=42 )
					self.assertEqual(image_processor.size,						{'shortest_edge': 42} )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					pass

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					# Initialize image_processing
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						Image.Image )

					# Test not batched input
					snake_case							=					image_processing(image_inputs[0],						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					# Initialize image_processing
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_,						numpify=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						np.ndarray )

					# Test not batched input
					snake_case							=					image_processing(image_inputs[0],						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)






				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					# Initialize image_processing
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_,						torchify=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						torch.Tensor )

					# Test not batched input
					snake_case							=					image_processing(image_inputs[0],						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)









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


__lowerCamelCase   	=  logging.get_logger(__name__)


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  PyTorchBenchmarkArguments
       lowerCamelCase__:  PretrainedConfig
       lowerCamelCase__:  str				     =  "PyTorch"

       @property
       def     UpperCAmelCase( self   :  str     ):
            return torch.__version__

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Tuple				      =     self._prepare_inference_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_speed(_inference     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  List[str]				      =     self._prepare_inference_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_memory(_inference     )

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  str				      =     self._prepare_train_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_speed(_train     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  List[Any]				      =     self._prepare_train_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_memory(_train     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Union[str, Any]				      =     self.config_dict[model_name]

            if self.args.torchscript:
                 a_     :  Optional[Any]				      =     True

            a_     :  Any				      =     (
                hasattr(lowerCamelCase_   ,				"""architectures"""     )
                and isinstance(config.architectures   ,				lowerCamelCase_     )
                and len(config.architectures     ) > 0
            )
            if not self.args.only_pretrain_model and has_model_class_in_config:
                 try:
                      a_     :  Union[str, Any]				      =     config.architectures[0]
                      a_     :  Any				      =     __import__("""transformers"""   ,				fromlist=[model_class]     )
                      a_     :  List[str]				      =     getattr(lowerCamelCase_   ,				lowerCamelCase_     )
                      a_     :  Dict				      =     model_cls(lowerCamelCase_     )
                 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_     :  str				      =     MODEL_MAPPING[config.__class__](lowerCamelCase_     )

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

            # encoder-decoder has vocab size saved differently
            a_     :  Union[str, Any]				      =     config.vocab_size if hasattr(lowerCamelCase_   ,				"""vocab_size"""     ) else config.encoder.vocab_size
            a_     :  Dict				      =     torch.randint(lowerCamelCase_   ,				(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():
                      a_     :  Optional[int]				      =     torch.jit.trace(lowerCamelCase_   ,				lowerCamelCase_     )
            else:
                 a_     :  Optional[Any]				      =     model

            def encoder_decoder_forward():
                 with torch.no_grad():
                      a_     :  Any				      =     inference_model(lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_     )
                 return outputs

            def encoder_forward():
                 with torch.no_grad():
                      a_     :  Dict				      =     inference_model(lowerCamelCase_     )
                 return outputs

            a_     :  Union[str, Any]				      =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
            return _forward

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Optional[int]				      =     self.config_dict[model_name]

            a_     :  str				      =     (
                hasattr(lowerCamelCase_   ,				"""architectures"""     )
                and isinstance(config.architectures   ,				lowerCamelCase_     )
                and len(config.architectures     ) > 0
            )
            if not self.args.only_pretrain_model and has_model_class_in_config:
                 try:
                      a_     :  Optional[int]				      =     config.architectures[0]
                      a_     :  List[Any]				      =     __import__("""transformers"""   ,				fromlist=[model_class]     )
                      a_     :  List[str]				      =     getattr(lowerCamelCase_   ,				lowerCamelCase_     )
                      a_     :  Any				      =     model_cls(lowerCamelCase_     )
                 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_     :  Union[str, Any]				      =     MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase_     )

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

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

            # encoder-decoder has vocab size saved differently
            a_     :  Any				      =     config.vocab_size if hasattr(lowerCamelCase_   ,				"""vocab_size"""     ) else config.encoder.vocab_size
            a_     :  Tuple				      =     torch.randint(lowerCamelCase_   ,				(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():
                 a_     :  Dict				      =     train_model(lowerCamelCase_   ,				labels=lowerCamelCase_     )[0]
                 loss.backward()
                 return loss

            def compute_loss_and_backprob_encoder_decoder():
                 a_     :  Optional[int]				      =     train_model(lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_   ,				labels=lowerCamelCase_     )[0]
                 loss.backward()
                 return loss

            a_     :  Optional[Any]				      =     (
                compute_loss_and_backprob_encoder_decoder
                if config.is_encoder_decoder
                else compute_loss_and_backprob_encoder
            )
            return _train

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Optional[int]     ):
            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(
                          lowerCamelCase_   ,				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_     :  List[str]				      =     timeit.repeat(
                     lowerCamelCase_   ,				repeat=self.args.repeat   ,				number=1_0   ,				)

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

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  Callable[[], None]     ):
            try:
                 if self.args.trace_memory_line_by_line:
                      a_     :  Dict				      =     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."""     )
                           a_     :  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()
                           a_     :  List[Any]				      =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx     )
                           a_     :  Tuple				      =     nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase_     )
                           a_     :  Any				      =     meminfo.used
                           a_     :  Any				      =     Memory(lowerCamelCase_     )
                           # shutdown nvml
                           nvml.nvmlShutdown()
                 else:
                      # cpu
                      a_     :  Union[str, Any]				      =     measure_peak_memory_cpu(lowerCamelCase_     )
                      a_     :  Optional[Any]				      =     Memory(lowerCamelCase_     ) if isinstance(lowerCamelCase_   ,				lowerCamelCase_     ) else memory_bytes

                 if self.args.trace_memory_line_by_line:
                      a_     :  Dict				      =     stop_memory_tracing(lowerCamelCase_     )
                 else:
                      a_     :  Any				      =     None

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



