
import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor

class 		__magic_name__				( unittest.TestCase					):


  """simple docstring"""





  def __init__(					self	,		a__	,		a__=7	,		a__=3	,		a__=30	,		a__=4_00	,		a__=True	,		a__=None	,		a__=True	,		a__=[0.5, 0.5, 0.5]	,		a__=[0.5, 0.5, 0.5]	,		a__=False	,		):
    _lowerCamelCase      =						size if size is not None else {'''height''': 30, '''width''': 30}
    _lowerCamelCase      =						parent
    _lowerCamelCase      =						batch_size
    _lowerCamelCase      =						num_channels
    _lowerCamelCase      =						min_resolution
    _lowerCamelCase      =						max_resolution
    _lowerCamelCase      =						do_resize
    _lowerCamelCase      =						size
    _lowerCamelCase      =						do_normalize
    _lowerCamelCase      =						image_mean
    _lowerCamelCase      =						image_std
    _lowerCamelCase      =						do_reduce_labels


  def     _UpperCAmelCase  (					self		):
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_reduce_labels": self.do_reduce_labels,
    }







def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						load_dataset('''hf-internal-testing/fixtures_ade20k'''			,   split='''test'''  )

  _lowerCamelCase      =						Image.open(dataset[0]['''file''']  )
  _lowerCamelCase      =						Image.open(dataset[1]['''file''']  )

  return image, map


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						load_dataset('''hf-internal-testing/fixtures_ade20k'''			,   split='''test'''  )

  _lowerCamelCase      =						Image.open(dataset[0]['''file''']  )
  _lowerCamelCase      =						Image.open(dataset[1]['''file''']  )
  _lowerCamelCase      =						Image.open(dataset[2]['''file''']  )
  _lowerCamelCase      =						Image.open(dataset[3]['''file''']  )

  return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 		__magic_name__				( __lowerCamelCase						,unittest.TestCase					):


  """simple docstring"""





  _UpperCamelCase						 = SegformerImageProcessor if is_vision_available() else None


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						SegformerImageProcessingTester(self		)


  @property
  def     _UpperCAmelCase  (					self		):
    return self.image_processor_tester.prepare_image_processor_dict()


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)
    self.assertTrue(hasattr(__lowerCAmelCase	,		'''do_resize'''		)		)
    self.assertTrue(hasattr(__lowerCAmelCase	,		'''size'''		)		)
    self.assertTrue(hasattr(__lowerCAmelCase	,		'''do_normalize'''		)		)
    self.assertTrue(hasattr(__lowerCAmelCase	,		'''image_mean'''		)		)
    self.assertTrue(hasattr(__lowerCAmelCase	,		'''image_std'''		)		)
    self.assertTrue(hasattr(__lowerCAmelCase	,		'''do_reduce_labels'''		)		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class.from_dict(self.image_processor_dict		)
    self.assertEqual(image_processor.size	,		{'''height''': 30, '''width''': 30}		)
    self.assertEqual(image_processor.do_reduce_labels	,		__lowerCAmelCase		)

    _lowerCamelCase      =						self.image_processing_class.from_dict(self.image_processor_dict	,		size=42	,		reduce_labels=__lowerCAmelCase		)
    self.assertEqual(image_processor.size	,		{'''height''': 42, '''width''': 42}		)
    self.assertEqual(image_processor.do_reduce_labels	,		__lowerCAmelCase		)


  def     _UpperCAmelCase  (					self		):
    pass


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

    # Test not batched input
    _lowerCamelCase      =						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
    _lowerCamelCase      =						image_processing(__lowerCAmelCase	,		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		):
    # Initialize image_processing
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)
    # create random numpy tensors
    _lowerCamelCase      =						prepare_image_inputs(self.image_processor_tester	,		equal_resolution=__lowerCAmelCase	,		numpify=__lowerCAmelCase		)
    for image in image_inputs:
      self.assertIsInstance(__lowerCAmelCase	,		np.ndarray		)

    # Test not batched input
    _lowerCamelCase      =						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
    _lowerCamelCase      =						image_processing(__lowerCAmelCase	,		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		):
    # Initialize image_processing
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)
    # create random PyTorch tensors
    _lowerCamelCase      =						prepare_image_inputs(self.image_processor_tester	,		equal_resolution=__lowerCAmelCase	,		torchify=__lowerCAmelCase		)
    for image in image_inputs:
      self.assertIsInstance(__lowerCAmelCase	,		torch.Tensor		)

    # Test not batched input
    _lowerCamelCase      =						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
    _lowerCamelCase      =						image_processing(__lowerCAmelCase	,		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		):
    # Initialize image_processing
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)
    # create random PyTorch tensors
    _lowerCamelCase      =						prepare_image_inputs(self.image_processor_tester	,		equal_resolution=__lowerCAmelCase	,		torchify=__lowerCAmelCase		)
    _lowerCamelCase      =						[]
    for image in image_inputs:
      self.assertIsInstance(__lowerCAmelCase	,		torch.Tensor		)
      maps.append(torch.zeros(image.shape[-2:]		).long()		)

    # Test not batched input
    _lowerCamelCase      =						image_processing(image_inputs[0]	,		maps[0]	,		return_tensors='''pt'''		)
    self.assertEqual(
        encoding['''pixel_values'''].shape	,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['''height'''],
            self.image_processor_tester.size['''width'''],
        )	,		)
    self.assertEqual(
        encoding['''labels'''].shape	,		(
            1,
            self.image_processor_tester.size['''height'''],
            self.image_processor_tester.size['''width'''],
        )	,		)
    self.assertEqual(encoding['''labels'''].dtype	,		torch.long		)
    self.assertTrue(encoding['''labels'''].min().item() >= 0		)
    self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)

    # Test batched
    _lowerCamelCase      =						image_processing(__lowerCAmelCase	,		__lowerCAmelCase	,		return_tensors='''pt'''		)
    self.assertEqual(
        encoding['''pixel_values'''].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'''],
        )	,		)
    self.assertEqual(
        encoding['''labels'''].shape	,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.size['''height'''],
            self.image_processor_tester.size['''width'''],
        )	,		)
    self.assertEqual(encoding['''labels'''].dtype	,		torch.long		)
    self.assertTrue(encoding['''labels'''].min().item() >= 0		)
    self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)

    # Test not batched input (PIL images)
    _lowerCamelCase    ,    _lowerCamelCase      =						prepare_semantic_single_inputs()

    _lowerCamelCase      =						image_processing(__lowerCAmelCase	,		__lowerCAmelCase	,		return_tensors='''pt'''		)
    self.assertEqual(
        encoding['''pixel_values'''].shape	,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['''height'''],
            self.image_processor_tester.size['''width'''],
        )	,		)
    self.assertEqual(
        encoding['''labels'''].shape	,		(
            1,
            self.image_processor_tester.size['''height'''],
            self.image_processor_tester.size['''width'''],
        )	,		)
    self.assertEqual(encoding['''labels'''].dtype	,		torch.long		)
    self.assertTrue(encoding['''labels'''].min().item() >= 0		)
    self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)

    # Test batched input (PIL images)
    _lowerCamelCase    ,    _lowerCamelCase      =						prepare_semantic_batch_inputs()

    _lowerCamelCase      =						image_processing(__lowerCAmelCase	,		__lowerCAmelCase	,		return_tensors='''pt'''		)
    self.assertEqual(
        encoding['''pixel_values'''].shape	,		(
            2,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['''height'''],
            self.image_processor_tester.size['''width'''],
        )	,		)
    self.assertEqual(
        encoding['''labels'''].shape	,		(
            2,
            self.image_processor_tester.size['''height'''],
            self.image_processor_tester.size['''width'''],
        )	,		)
    self.assertEqual(encoding['''labels'''].dtype	,		torch.long		)
    self.assertTrue(encoding['''labels'''].min().item() >= 0		)
    self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)


  def     _UpperCAmelCase  (					self		):
    # Initialize image_processing
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)

    # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
    _lowerCamelCase    ,    _lowerCamelCase      =						prepare_semantic_single_inputs()
    _lowerCamelCase      =						image_processing(__lowerCAmelCase	,		__lowerCAmelCase	,		return_tensors='''pt'''		)
    self.assertTrue(encoding['''labels'''].min().item() >= 0		)
    self.assertTrue(encoding['''labels'''].max().item() <= 1_50		)

    _lowerCamelCase      =						True
    _lowerCamelCase      =						image_processing(__lowerCAmelCase	,		__lowerCAmelCase	,		return_tensors='''pt'''		)
    self.assertTrue(encoding['''labels'''].min().item() >= 0		)
    self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)





'''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()






