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__	(       __A=None ,			__A=None						):







    '''simple docstring'''
    return field(default_factory=lambda: default ,			metadata=_SCREAMING_SNAKE_CASE						)




@dataclass
class 						__snake_case						:





      '''simple docstring'''






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







def     A__	(       __A						):







    '''simple docstring'''


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







def     A__	(       __A						):







    '''simple docstring'''


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




class 						__snake_case						:





      '''simple docstring'''






      def __init__(       self  :					Tuple     ,	_UpperCamelCase  :					str)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    args
          _lowerCamelCase  :		List[str]			    =    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

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




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    plt.subplots()
          _lowerCamelCase  :		Tuple			    =    'Time usage' if self.args.is_time else 'Memory usage'
          _lowerCamelCase  :		List[Any]			    =    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()):
              _lowerCamelCase  :		List[Any]			    =    sorted(set(self.result_dict[model_name]["""bsz"""]))
              _lowerCamelCase  :		List[str]			    =    sorted(set(self.result_dict[model_name]["""seq_len"""]))
              _lowerCamelCase  :		Any			    =    self.result_dict[model_name]['result']

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

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

                  (_lowerCamelCase)  :		Optional[Any]			    =    (
                      ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                  )

                  _lowerCamelCase  :		List[str]			    =    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."""

          _lowerCamelCase  :		Optional[Any]			    =    title_str[:-4]
          _lowerCamelCase  :		Union[str, Any]			    =    '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__	(       ):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[int]			    =    HfArgumentParser(_SCREAMING_SNAKE_CASE						)
    _lowerCamelCase  :		Union[str, Any]			    =    parser.parse_args_into_dataclasses()[0]
    _lowerCamelCase  :		Any			    =    Plot(args=_SCREAMING_SNAKE_CASE						)
    plot.plot()


if __name__ == "__main__":
    main()





"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         __A					:					str								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_optional_components()
    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()					,       reason='XFormers attention is only available with CUDA and `xformers` installed'					,       )
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2					,       )



