






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


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import TvltImageProcessor



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__=10					,     lowerCAmelCase__=10					,     lowerCAmelCase__=False					,     lowerCAmelCase__=False	):


		'''simple docstring'''

		__A    =							[]
		for i in range(image_processor_tester.num_frames	):
				video.append(np.random.randint(255					,     size=(image_processor_tester.num_channels, width, height)					,     dtype=np.uinta	)	)

		if not numpify and not torchify:
				# PIL expects the channel dimension as last dimension
				__A    =							[Image.fromarray(np.moveaxis(lowerCAmelCase__					,     0					,     -1	)	) for frame in video]

		if torchify:
				__A    =							[torch.from_numpy(lowerCAmelCase__	) for frame in video]

		return video



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__=False					,     lowerCAmelCase__=False					,     lowerCAmelCase__=False	):


		'''simple docstring'''

		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		__A    =							[]
		for i in range(image_processor_tester.batch_size	):
				if equal_resolution:
						__A    =							__A    =							image_processor_tester.max_resolution
				else:
						__A ,   __A    =							np.random.choice(
						    np.arange(image_processor_tester.min_resolution					,     image_processor_tester.max_resolution	)					,     2	)
						__A    =							prepare_video(
						    image_processor_tester=lowerCAmelCase__					,     width=lowerCAmelCase__					,     height=lowerCAmelCase__					,     numpify=lowerCAmelCase__					,     torchify=lowerCAmelCase__					,     )
				video_inputs.append(lowerCAmelCase__	)

		return video_inputs





class        a__      (	unittest.TestCase		):






		def __init__(      self							, lowercase__							, lowercase__=7							, lowercase__=3							, lowercase__=4							, lowercase__=18							, lowercase__=30							, lowercase__=400							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=True							, lowercase__=None							, )				->				Optional[Any]:
				__A    =							size if size is not None else {"shortest_edge": 18}
				__A    =							crop_size if crop_size is not None else {"height": 18, "width": 18}

				__A    =							parent
				__A    =							batch_size
				__A    =							num_channels
				__A    =							num_frames
				__A    =							image_size
				__A    =							min_resolution
				__A    =							max_resolution
				__A    =							do_resize
				__A    =							size
				__A    =							do_normalize
				__A    =							image_mean
				__A    =							image_std
				__A    =							do_center_crop
				__A    =							crop_size






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				}





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Tuple 					= TvltImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							TvltImageProcessorTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							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__							, "do_center_crop"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL videos
				__A    =							prepare_video_inputs(self.image_processor_tester							, equal_resolution=lowercase__						)
				for video in video_inputs:
						self.assertIsInstance(lowercase__							, lowercase__						)
						self.assertIsInstance(video[0]							, Image.Image						)

				# Test not batched input
				__A    =							image_processor(video_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_videos.shape							, (
				        1,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )

				# Test batched
				__A    =							image_processor(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_videos.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Any:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							prepare_video_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, numpify=lowercase__						)
				for video in video_inputs:
						self.assertIsInstance(lowercase__							, lowercase__						)
						self.assertIsInstance(video[0]							, np.ndarray						)

				# Test not batched input
				__A    =							image_processor(video_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_videos.shape							, (
				        1,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )

				# Test batched
				__A    =							image_processor(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_videos.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Any:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_video_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				for video in video_inputs:
						self.assertIsInstance(lowercase__							, lowercase__						)
						self.assertIsInstance(video[0]							, torch.Tensor						)

				# Test not batched input
				__A    =							image_processor(video_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_videos.shape							, (
				        1,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )

				# Test batched
				__A    =							image_processor(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_videos.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_frames,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )


'''simple docstring'''

import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def __a			(						__lowerCamelCase      :    Optional[Any]=None			,		__lowerCamelCase      :    List[str]=None					)						->				Any:
      '''simple docstring'''


      return field(default_factory=lambda: default			,		metadata=__lowerCamelCase					)





@dataclass
class 				lowercase    :
   lowerCamelCase_            =field(
       metadata={'help': 'The csv file to plot.'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Disable logarithmic scale when plotting'}			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={
           'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
       }			,					)
   lowerCamelCase_            =field(
       default=__lowerCamelCase			,					metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}			,					)
   lowerCamelCase_            =list_field(
       default=__lowerCamelCase			,					metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}			)




def __a			(						__lowerCamelCase      :    int					)						->				Tuple:
      '''simple docstring'''


      try:
            int(__lowerCamelCase					)
            return True
      except ValueError:
            return False


def __a			(						__lowerCamelCase      :    Union[str, Any]					)						->				str:
      '''simple docstring'''


      try:
            float(__lowerCamelCase					)
            return True
      except ValueError:
            return False





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Tuple)   ->       Dict:
         lowercase_     			=   args
         lowercase_     			=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

         with open(self.args.csv_file ,		newline="") as csv_file:
               lowercase_     			=   csv.DictReader(__lowerCAmelCase)
               for row in reader:
                     lowercase_     			=   row["model"]
                     self.result_dict[model_name]["bsz"].append(int(row["batch_size"]))
                     self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]))
                     if can_convert_to_int(row["result"]):
                           # value is not None
                           lowercase_     			=   int(row["result"])
                     elif can_convert_to_float(row["result"]):
                           # value is not None
                           lowercase_     			=   float(row["result"])


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Tuple:
         lowercase_			,				lowercase_     			=   plt.subplots()
         lowercase_     			=   "Time usage" if self.args.is_time else "Memory usage"
         lowercase_     			=   title_str + " for training" if self.args.is_train else title_str + " for inference"

         if not self.args.no_log_scale:
               # set logarithm scales
               ax.set_xscale("log")
               ax.set_yscale("log")

         for axis in [ax.xaxis, ax.yaxis]:
               axis.set_major_formatter(ScalarFormatter())

         for model_name_idx, model_name in enumerate(self.result_dict.keys()):
               lowercase_     			=   sorted(set(self.result_dict[model_name]["bsz"]))
               lowercase_     			=   sorted(set(self.result_dict[model_name]["seq_len"]))
               lowercase_     			=   self.result_dict[model_name]["result"]

               ((lowercase_)			,				(lowercase_))     			=   (
                   (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
               )

               lowercase_     			=   (
                   model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
               )

               for inner_loop_value in inner_loop_array:
                     if self.args.plot_along_batch:
                           lowercase_     			=   np.asarray(
                               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,		dtype=__lowerCAmelCase ,		)
                     else:
                           lowercase_     			=   np.asarray(
                               [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,		dtype=np.floataa ,		)

                     ((lowercase_)			,				(lowercase_))     			=   (
                         ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                     )

                     lowercase_     			=   np.asarray(__lowerCAmelCase ,		__lowerCAmelCase)[: len(__lowerCAmelCase)]
                     plt.scatter(
                         __lowerCAmelCase ,		__lowerCAmelCase ,		label=F'{label_model_name} - {inner_loop_label}: {inner_loop_value}')
                     plt.plot(__lowerCAmelCase ,		__lowerCAmelCase ,		"--")

               title_str += F' {label_model_name} vs.'

         lowercase_     			=   title_str[:-4]
         lowercase_     			=   "Time in s" if self.args.is_time else "Memory in MB"

         # plot
         plt.title(__lowerCAmelCase)
         plt.xlabel(__lowerCAmelCase)
         plt.ylabel(__lowerCAmelCase)
         plt.legend()

         if self.args.figure_png_file is not None:
               plt.savefig(self.args.figure_png_file)
         else:
               plt.show()




def __a			(						)						->				Tuple:
      '''simple docstring'''


      lowercase_     			=   HfArgumentParser(__lowerCamelCase					)
      lowercase_     			=   parser.parse_args_into_dataclasses()[0]
      lowercase_     			=   Plot(args=__lowerCamelCase					)
      plot.plot()


if __name__ == "__main__":
       main()






