


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 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]=None				,							UpperCamelCase__     :  int=None						):
       return field(default_factory=lambda: default				,							metadata=UpperCamelCase__						)







@dataclass
class      lowerCAmelCase__       :




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




def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       try:
              int(UpperCamelCase__						)
              return True
       except ValueError:
              return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       try:
              float(UpperCamelCase__						)
              return True
       except ValueError:
              return False







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase)			->				Dict:
            _A      :       Any				    = args
            _A      :       Optional[Any]				    = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

            with open(self.args.csv_file ,						newline="") as csv_file:
                   _A      :       Union[str, Any]				    = csv.DictReader(__lowerCamelCase)
                   for row in reader:
                          _A      :       Tuple				    = 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
                                 _A      :       Union[str, Any]				    = int(row["result"])
                          elif can_convert_to_float(row["result"]):
                                 # value is not None
                                 _A      :       Tuple				    = float(row["result"])


     def  _lowerCamelCase (						self)			->				int:
            _A    ,			_A      :       Union[str, Any]				    = plt.subplots()
            _A      :       str				    = "Time usage" if self.args.is_time else "Memory usage"
            _A      :       Dict				    = 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()):
                   _A      :       List[str]				    = sorted(set(self.result_dict[model_name]["bsz"]))
                   _A      :       int				    = sorted(set(self.result_dict[model_name]["seq_len"]))
                   _A      :       List[str]				    = self.result_dict[model_name]["result"]

                   ((_A)    ,			(_A))      :       Dict				    = (
                       (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                   )

                   _A      :       Any				    = (
                       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:
                                 _A      :       Optional[int]				    = np.asarray(
                                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,						dtype=__lowerCamelCase ,						)
                          else:
                                 _A      :       List[str]				    = np.asarray(
                                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,						dtype=np.floataa ,						)

                          ((_A)    ,			(_A))      :       Union[str, Any]				    = (
                              ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                          )

                          _A      :       int				    = 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."

            _A      :       str				    = title_str[:-4]
            _A      :       int				    = "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 							_UpperCAmelCase				():
       _A      :       List[str]				    = HfArgumentParser(UpperCamelCase__						)
       _A      :       Union[str, Any]				    = parser.parse_args_into_dataclasses()[0]
       _A      :       Any				    = Plot(args=UpperCamelCase__						)
       plot.plot()


if __name__ == "__main__":
    main()

'''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 MobileViTImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"center_crop"       )       )
             self.assertTrue(hasattr(A						,					"do_flip_channel_order"       )       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 20}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Dict							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  List[Any]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[Any]							      =    image_processing(A						,					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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




      def      _A	(       self						:				Union[str, Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					np.ndarray       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[int]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(A						,					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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  List[str]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Any							      =    image_processing(A						,					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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

