





import json
import os
import tempfile

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


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image






def 				_lowerCAmelCase			(	_a :       str       , _a :       Dict=False       , _a :       Tuple=False       , _a :       str=False      )						->      Tuple:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     lowerCAmelCase_       :							Tuple			 =		[]
     for i in range(image_processor_tester.batch_size      ):
          if equal_resolution:
               lowerCAmelCase_       :							str			 =		image_processor_tester.max_resolution
          else:
               # To avoid getting image width/height 0
               lowerCAmelCase_       :							str			 =		image_processor_tester.min_resolution
               if getattr(UpperCAmelCase_       , """size_divisor"""       , UpperCAmelCase_      ):
                    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                    lowerCAmelCase_       :							Dict			 =		max(image_processor_tester.size_divisor       , UpperCAmelCase_      )
               lowerCAmelCase_ ,	lowerCAmelCase_       :							Optional[int]			 =		np.random.choice(np.arange(UpperCAmelCase_       , image_processor_tester.max_resolution      )       , 2      )
          image_inputs.append(
              np.random.randint(2_55       , 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
          lowerCAmelCase_       :							Dict			 =		[Image.fromarray(np.moveaxis(UpperCAmelCase_       , 0       , -1      )      ) for image in image_inputs]

     if torchify:
          lowerCAmelCase_       :							Optional[Any]			 =		[torch.from_numpy(UpperCAmelCase_      ) for image in image_inputs]

     return image_inputs






def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       List[str]=10       , _a :       int=10       , _a :       Any=False       , _a :       Union[str, Any]=False      )						->      Optional[Any]:
     lowerCAmelCase_       :							List[str]			 =		[]
     for i in range(image_processor_tester.num_frames      ):
          video.append(np.random.randint(2_55       , 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
          lowerCAmelCase_       :							List[Any]			 =		[Image.fromarray(np.moveaxis(UpperCAmelCase_       , 0       , -1      )      ) for frame in video]

     if torchify:
          lowerCAmelCase_       :							Optional[Any]			 =		[torch.from_numpy(UpperCAmelCase_      ) for frame in video]

     return video






def 				_lowerCAmelCase			(	_a :       int       , _a :       str=False       , _a :       str=False       , _a :       Optional[int]=False      )						->      Any:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     lowerCAmelCase_       :							Optional[int]			 =		[]
     for i in range(image_processor_tester.batch_size      ):
          if equal_resolution:
               lowerCAmelCase_       :							Dict			 =		image_processor_tester.max_resolution
          else:
               lowerCAmelCase_ ,	lowerCAmelCase_       :							Union[str, Any]			 =		np.random.choice(
                   np.arange(image_processor_tester.min_resolution       , image_processor_tester.max_resolution      )       , 2      )
               lowerCAmelCase_       :							Optional[int]			 =		prepare_video(
                   image_processor_tester=UpperCAmelCase_       , width=UpperCAmelCase_       , height=UpperCAmelCase_       , numpify=UpperCAmelCase_       , torchify=UpperCAmelCase_       , )
          video_inputs.append(UpperCAmelCase_      )

     return video_inputs







class   lowercase__     :
  __UpperCamelCase						  =	None







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Dict			 =		self.image_processing_class(**self.image_processor_dict    )
       lowerCAmelCase_       :							List[Any]			 =		json.loads(image_processor.to_json_string()    )
       for key, value in self.image_processor_dict.items():
            self.assertEqual(obj[key]     ,    _lowerCAmelCase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Optional[int]			 =		self.image_processing_class(**self.image_processor_dict    )

       with tempfile.TemporaryDirectory() as tmpdirname:
            lowerCAmelCase_       :							Optional[Any]			 =		os.path.join(_lowerCAmelCase     ,    """image_processor.json"""    )
            image_processor_first.to_json_file(_lowerCAmelCase    )
            lowerCAmelCase_       :							Dict			 =		self.image_processing_class.from_json_file(_lowerCAmelCase    )

       self.assertEqual(image_processor_second.to_dict()     ,    image_processor_first.to_dict()    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							str			 =		self.image_processing_class(**self.image_processor_dict    )

       with tempfile.TemporaryDirectory() as tmpdirname:
            lowerCAmelCase_       :							Optional[Any]			 =		image_processor_first.save_pretrained(_lowerCAmelCase    )[0]
            check_json_file_has_correct_format(_lowerCAmelCase    )
            lowerCAmelCase_       :							int			 =		self.image_processing_class.from_pretrained(_lowerCAmelCase    )

       self.assertEqual(image_processor_second.to_dict()     ,    image_processor_first.to_dict()    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		self.image_processing_class()
       self.assertIsNotNone(_lowerCAmelCase    )







  @require_torch
  @require_vision
  def 						UpperCAmelCase__		(						self    ):
       if self.test_cast_dtype is not None:
            # Initialize image_processor
            lowerCAmelCase_       :							str			 =		self.image_processing_class(**self.image_processor_dict    )

            # create random PyTorch tensors
            lowerCAmelCase_       :							Optional[Any]			 =		prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=_lowerCAmelCase     ,    torchify=_lowerCAmelCase    )

            lowerCAmelCase_       :							List[str]			 =		image_processor(_lowerCAmelCase     ,    return_tensors="""pt"""    )
            # for layoutLM compatiblity
            self.assertEqual(encoding.pixel_values.device     ,    torch.device("""cpu"""    )    )
            self.assertEqual(encoding.pixel_values.dtype     ,    torch.floataa    )

            lowerCAmelCase_       :							Tuple			 =		image_processor(_lowerCAmelCase     ,    return_tensors="""pt"""    ).to(torch.floataa    )
            self.assertEqual(encoding.pixel_values.device     ,    torch.device("""cpu"""    )    )
            self.assertEqual(encoding.pixel_values.dtype     ,    torch.floataa    )

            lowerCAmelCase_       :							List[Any]			 =		image_processor(_lowerCAmelCase     ,    return_tensors="""pt"""    ).to("""cpu"""     ,    torch.bfloataa    )
            self.assertEqual(encoding.pixel_values.device     ,    torch.device("""cpu"""    )    )
            self.assertEqual(encoding.pixel_values.dtype     ,    torch.bfloataa    )

            with self.assertRaises(_lowerCAmelCase    ):
                 lowerCAmelCase_       :							str			 =		image_processor(_lowerCAmelCase     ,    return_tensors="""pt"""    ).to(torch.bfloataa     ,    """cpu"""    )

            # Try with text + image feature
            lowerCAmelCase_       :							Union[str, Any]			 =		image_processor(_lowerCAmelCase     ,    return_tensors="""pt"""    )
            encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]    )}    )
            lowerCAmelCase_       :							int			 =		encoding.to(torch.floataa    )

            self.assertEqual(encoding.pixel_values.device     ,    torch.device("""cpu"""    )    )
            self.assertEqual(encoding.pixel_values.dtype     ,    torch.floataa    )
            self.assertEqual(encoding.input_ids.dtype     ,    torch.long    )






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 							lowerCamelCase    (							UpperCAmelCase_	:      Union[str, Any]=None   ,       UpperCAmelCase_	:      Union[str, Any]=None      )->			Tuple:

       """simple docstring"""




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


@dataclass
class        UpperCAmelCase__				:



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





def 							lowerCamelCase    (							UpperCAmelCase_	:      Dict      )->			int:

       """simple docstring"""




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





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]      )->			str:

       """simple docstring"""




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


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase	):
            a			   =args
            a			   =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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






     def        lowerCAmelCase__  (				self	):
            a   ,      a			   =plt.subplots()
            a			   ="""Time usage""" if self.args.is_time else """Memory usage"""
            a			   =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			   =sorted(set(self.result_dict[model_name]["""bsz"""]	)	)
                   a			   =sorted(set(self.result_dict[model_name]["""seq_len"""]	)	)
                   a			   =self.result_dict[model_name]["""result"""]

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

                   a			   =(
                       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			   =np.asarray(
                                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,    dtype=_lowerCAmelCase ,    )
                          else:
                                 a			   =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))			   =(
                              ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                          )

                          a			   =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			   =title_str[:-4]
            a			   ="""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 							lowerCamelCase    (							)->			Optional[Any]:

       """simple docstring"""




       a			   =HfArgumentParser(UpperCAmelCase_      )
       a			   =parser.parse_args_into_dataclasses()[0]
       a			   =Plot(args=UpperCAmelCase_      )
       plot.plot()


if __name__ == "__main__":
    main()
