





'''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   (__SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None   ):


  """simple docstring"""

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







@dataclass
class 		UpperCAmelCase   :


  """simple docstring"""







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




def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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







class 		UpperCAmelCase   :


  """simple docstring"""







  def __init__(				self    :					Optional[Any]							,	UpperCamelCase__    :					Optional[int]  )     ->						str:
    _UpperCamelCase       			=args
    _UpperCamelCase       			=defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

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




  def   UpperCamelCase__      (				self    :					str  )     ->						int:
    _UpperCamelCase			,     _UpperCamelCase       			=plt.subplots()
    _UpperCamelCase       			='''Time usage''' if self.args.is_time else '''Memory usage'''
    _UpperCamelCase       			=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()  ):
      _UpperCamelCase       			=sorted(set(self.result_dict[model_name]['''bsz''']  )  )
      _UpperCamelCase       			=sorted(set(self.result_dict[model_name]['''seq_len''']  )  )
      _UpperCamelCase       			=self.result_dict[model_name]['''result''']

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

      _UpperCamelCase       			=(
          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:
          _UpperCamelCase       			=np.asarray(
              [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,	dtype=UpperCamelCase__							,	)
        else:
          _UpperCamelCase       			=np.asarray(
              [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							,	dtype=np.floataa							,	)

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

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

      title_str += F''' {label_model_name} vs.'''

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

    # plot
    plt.title(UpperCamelCase__  )
    plt.xlabel(UpperCamelCase__  )
    plt.ylabel(UpperCamelCase__  )
    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"""

  _UpperCamelCase       			=HfArgumentParser(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=parser.parse_args_into_dataclasses()[0]
  _UpperCamelCase       			=Plot(args=__SCREAMING_SNAKE_CASE   )
  plot.plot()


if __name__ == "__main__":
      main()













'''simple docstring'''






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class 		UpperCAmelCase   :


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Union[str, Any]:
    torch.manual_seed(0  )
    _UpperCamelCase       			=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

    torch.manual_seed(0  )
    _UpperCamelCase       			=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

    torch.manual_seed(0  )
    _UpperCamelCase       			=UNetaDConditionModel(
        sample_size=32							,	layers_per_block=1							,	block_out_channels=[32, 64]							,	down_block_types=[
            '''ResnetDownsampleBlock2D''',
            '''SimpleCrossAttnDownBlock2D''',
        ]							,	mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,	up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,	in_channels=3							,	out_channels=6							,	cross_attention_dim=32							,	encoder_hid_dim=32							,	attention_head_dim=8							,	addition_embed_type='''text'''							,	addition_embed_type_num_heads=2							,	cross_attention_norm='''group_norm'''							,	resnet_time_scale_shift='''scale_shift'''							,	act_fn='''gelu'''							,	)
    unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

    torch.manual_seed(0  )
    _UpperCamelCase       			=DDPMScheduler(
        num_train_timesteps=1000							,	beta_schedule='''squaredcos_cap_v2'''							,	beta_start=0.0001							,	beta_end=0.02							,	thresholding=UpperCamelCase__							,	dynamic_thresholding_ratio=0.95							,	sample_max_value=1.0							,	prediction_type='''epsilon'''							,	variance_type='''learned_range'''							,	)

    torch.manual_seed(0  )
    _UpperCamelCase       			=IFWatermarker()

    return {
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "unet": unet,
        "scheduler": scheduler,
        "watermarker": watermarker,
        "safety_checker": None,
        "feature_extractor": None,
    }


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Optional[int]:
    torch.manual_seed(0  )
    _UpperCamelCase       			=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

    torch.manual_seed(0  )
    _UpperCamelCase       			=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

    torch.manual_seed(0  )
    _UpperCamelCase       			=UNetaDConditionModel(
        sample_size=32							,	layers_per_block=[1, 2]							,	block_out_channels=[32, 64]							,	down_block_types=[
            '''ResnetDownsampleBlock2D''',
            '''SimpleCrossAttnDownBlock2D''',
        ]							,	mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,	up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,	in_channels=6							,	out_channels=6							,	cross_attention_dim=32							,	encoder_hid_dim=32							,	attention_head_dim=8							,	addition_embed_type='''text'''							,	addition_embed_type_num_heads=2							,	cross_attention_norm='''group_norm'''							,	resnet_time_scale_shift='''scale_shift'''							,	act_fn='''gelu'''							,	class_embed_type='''timestep'''							,	mid_block_scale_factor=1.414							,	time_embedding_act_fn='''gelu'''							,	time_embedding_dim=32							,	)
    unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

    torch.manual_seed(0  )
    _UpperCamelCase       			=DDPMScheduler(
        num_train_timesteps=1000							,	beta_schedule='''squaredcos_cap_v2'''							,	beta_start=0.0001							,	beta_end=0.02							,	thresholding=UpperCamelCase__							,	dynamic_thresholding_ratio=0.95							,	sample_max_value=1.0							,	prediction_type='''epsilon'''							,	variance_type='''learned_range'''							,	)

    torch.manual_seed(0  )
    _UpperCamelCase       			=DDPMScheduler(
        num_train_timesteps=1000							,	beta_schedule='''squaredcos_cap_v2'''							,	beta_start=0.0001							,	beta_end=0.02							,	)

    torch.manual_seed(0  )
    _UpperCamelCase       			=IFWatermarker()

    return {
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "unet": unet,
        "scheduler": scheduler,
        "image_noising_scheduler": image_noising_scheduler,
        "watermarker": watermarker,
        "safety_checker": None,
        "feature_extractor": None,
    }


  def   UpperCamelCase__      (				self    :					Any  )     ->						Any:
    _UpperCamelCase       			=self.get_dummy_components()
    _UpperCamelCase       			=self.pipeline_class(**UpperCamelCase__  )
    pipe.to(UpperCamelCase__  )
    pipe.set_progress_bar_config(disable=UpperCamelCase__  )

    _UpperCamelCase       			=self.get_dummy_inputs(UpperCamelCase__  )

    _UpperCamelCase       			=inputs['''prompt''']
    _UpperCamelCase       			=inputs['''generator''']
    _UpperCamelCase       			=inputs['''num_inference_steps''']
    _UpperCamelCase       			=inputs['''output_type''']

    if "image" in inputs:
      _UpperCamelCase       			=inputs['''image''']
    else:
      _UpperCamelCase       			=None

    if "mask_image" in inputs:
      _UpperCamelCase       			=inputs['''mask_image''']
    else:
      _UpperCamelCase       			=None

    if "original_image" in inputs:
      _UpperCamelCase       			=inputs['''original_image''']
    else:
      _UpperCamelCase       			=None

    _UpperCamelCase			,     _UpperCamelCase       			=pipe.encode_prompt(UpperCamelCase__  )

    # inputs with prompt converted to embeddings
    _UpperCamelCase       			={
        '''prompt_embeds''': prompt_embeds,
        '''negative_prompt_embeds''': negative_prompt_embeds,
        '''generator''': generator,
        '''num_inference_steps''': num_inference_steps,
        '''output_type''': output_type,
    }

    if image is not None:
      _UpperCamelCase       			=image

    if mask_image is not None:
      _UpperCamelCase       			=mask_image

    if original_image is not None:
      _UpperCamelCase       			=original_image

    # set all optional components to None
    for optional_component in pipe._optional_components:
      setattr(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )

    _UpperCamelCase       			=pipe(**UpperCamelCase__  )[0]

    with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(UpperCamelCase__  )
      _UpperCamelCase       			=self.pipeline_class.from_pretrained(UpperCamelCase__  )
      pipe_loaded.to(UpperCamelCase__  )
      pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__  )

    pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

    for optional_component in pipe._optional_components:
      self.assertTrue(
          getattr(UpperCamelCase__							,	UpperCamelCase__  ) is None							,	F'''`{optional_component}` did not stay set to None after loading.'''							,	)

    _UpperCamelCase       			=self.get_dummy_inputs(UpperCamelCase__  )

    _UpperCamelCase       			=inputs['''generator''']
    _UpperCamelCase       			=inputs['''num_inference_steps''']
    _UpperCamelCase       			=inputs['''output_type''']

    # inputs with prompt converted to embeddings
    _UpperCamelCase       			={
        '''prompt_embeds''': prompt_embeds,
        '''negative_prompt_embeds''': negative_prompt_embeds,
        '''generator''': generator,
        '''num_inference_steps''': num_inference_steps,
        '''output_type''': output_type,
    }

    if image is not None:
      _UpperCamelCase       			=image

    if mask_image is not None:
      _UpperCamelCase       			=mask_image

    if original_image is not None:
      _UpperCamelCase       			=original_image

    _UpperCamelCase       			=pipe_loaded(**UpperCamelCase__  )[0]

    _UpperCamelCase       			=np.abs(to_np(UpperCamelCase__  ) - to_np(UpperCamelCase__  )  ).max()
    self.assertLess(UpperCamelCase__							,	1E-4  )




  def   UpperCamelCase__      (				self    :					int  )     ->						str:
    _UpperCamelCase       			=self.get_dummy_components()
    _UpperCamelCase       			=self.pipeline_class(**UpperCamelCase__  )
    pipe.to(UpperCamelCase__  )
    pipe.set_progress_bar_config(disable=UpperCamelCase__  )

    _UpperCamelCase       			=self.get_dummy_inputs(UpperCamelCase__  )
    _UpperCamelCase       			=pipe(**UpperCamelCase__  )[0]

    with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(UpperCamelCase__  )
      _UpperCamelCase       			=self.pipeline_class.from_pretrained(UpperCamelCase__  )
      pipe_loaded.to(UpperCamelCase__  )
      pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__  )

    pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

    _UpperCamelCase       			=self.get_dummy_inputs(UpperCamelCase__  )
    _UpperCamelCase       			=pipe_loaded(**UpperCamelCase__  )[0]

    _UpperCamelCase       			=np.abs(to_np(UpperCamelCase__  ) - to_np(UpperCamelCase__  )  ).max()
    self.assertLess(UpperCamelCase__							,	1E-4  )






