


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     Union[str, Any]							    =     (UnCLIPScheduler,)

       def 						UpperCAmelCase(   self      :     str      ,							**lowerCamelCase_      :     List[Any]    ):
            a_    :     Tuple										=    {
                "num_train_timesteps": 1_0_0_0,
                "variance_type": "fixed_small_log",
                "clip_sample": True,
                "clip_sample_range": 1.0,
                "prediction_type": "epsilon",
            }

            config.update(**UpperCamelCase_    )
            return config

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=UpperCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            for variance in ["fixed_small_log", "learned_range"]:
                 self.check_over_configs(variance_type=UpperCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=UpperCamelCase_    )

       def 						UpperCAmelCase(   self      :     int    ):
            for clip_sample_range in [1, 5, 1_0, 2_0]:
                 self.check_over_configs(clip_sample_range=UpperCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(prediction_type=UpperCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            for time_step in [0, 5_0_0, 9_9_9]:
                 for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
                      if prev_timestep is not None and prev_timestep >= time_step:
                           continue

                      self.check_over_forward(time_step=UpperCamelCase_      ,							prev_timestep=UpperCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Dict										=    self.scheduler_classes[0]
            a_    :     Optional[int]										=    self.get_scheduler_config(variance_type="""fixed_small_log"""    )
            a_    :     int										=    scheduler_class(**UpperCamelCase_    )

            assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 1.0000E-10    )    ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_8_7    ) - 0.0_5_4_9_6_2_5    )    ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_9_9    ) - 0.9_9_9_4_9_8_7    )    ) < 1E-5

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Tuple										=    self.scheduler_classes[0]
            a_    :     str										=    self.get_scheduler_config(variance_type="""learned_range"""    )
            a_    :     Any										=    scheduler_class(**UpperCamelCase_    )

            a_    :     Tuple										=    0.5

            assert scheduler._get_variance(1      ,							predicted_variance=UpperCamelCase_    ) - -1_0.1_7_1_2_7_9_0 < 1E-5
            assert scheduler._get_variance(4_8_7      ,							predicted_variance=UpperCamelCase_    ) - -5.7_9_9_8_0_5_2 < 1E-5
            assert scheduler._get_variance(9_9_9      ,							predicted_variance=UpperCamelCase_    ) - -0.0_0_1_0_0_1_1 < 1E-5

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Tuple										=    self.scheduler_classes[0]
            a_    :     Dict										=    self.get_scheduler_config()
            a_    :     Tuple										=    scheduler_class(**UpperCamelCase_    )

            a_    :     Union[str, Any]										=    scheduler.timesteps

            a_    :     Any										=    self.dummy_model()
            a_    :     List[Any]										=    self.dummy_sample_deter
            a_    :     Any										=    torch.manual_seed(0    )

            for i, t in enumerate(UpperCamelCase_    ):
                 # 1. predict noise residual
                 a_    :     Dict										=    model(UpperCamelCase_      ,							UpperCamelCase_    )

                 # 2. predict previous mean of sample x_t-1
                 a_    :     Optional[int]										=    scheduler.step(UpperCamelCase_      ,							UpperCamelCase_      ,							UpperCamelCase_      ,							generator=UpperCamelCase_    ).prev_sample

                 a_    :     List[Any]										=    pred_prev_sample

            a_    :     Tuple										=    torch.sum(torch.abs(UpperCamelCase_    )    )
            a_    :     Tuple										=    torch.mean(torch.abs(UpperCamelCase_    )    )

            assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5    ) < 1E-2
            assert abs(result_mean.item() - 0.3_2_8_4_7_4_3    ) < 1E-3

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Tuple										=    self.scheduler_classes[0]
            a_    :     List[str]										=    self.get_scheduler_config()
            a_    :     str										=    scheduler_class(**UpperCamelCase_    )

            scheduler.set_timesteps(2_5    )

            a_    :     str										=    scheduler.timesteps

            a_    :     Optional[int]										=    self.dummy_model()
            a_    :     Dict										=    self.dummy_sample_deter
            a_    :     List[str]										=    torch.manual_seed(0    )

            for i, t in enumerate(UpperCamelCase_    ):
                 # 1. predict noise residual
                 a_    :     Tuple										=    model(UpperCamelCase_      ,							UpperCamelCase_    )

                 if i + 1 == timesteps.shape[0]:
                      a_    :     Optional[int]										=    None
                 else:
                      a_    :     Dict										=    timesteps[i + 1]

                 # 2. predict previous mean of sample x_t-1
                 a_    :     Tuple										=    scheduler.step(
                     UpperCamelCase_      ,							UpperCamelCase_      ,							UpperCamelCase_      ,							prev_timestep=UpperCamelCase_      ,							generator=UpperCamelCase_    ).prev_sample

                 a_    :     Optional[int]										=    pred_prev_sample

            a_    :     str										=    torch.sum(torch.abs(UpperCamelCase_    )    )
            a_    :     Union[str, Any]										=    torch.mean(torch.abs(UpperCamelCase_    )    )

            assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3    ) < 1E-2
            assert abs(result_mean.item() - 0.3_3_6_2_0_3_8    ) < 1E-3

       def 						UpperCAmelCase(   self      :     int    ):
            pass

       def 						UpperCAmelCase(   self      :     Tuple    ):
            pass







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			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


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



def  _a			(						__UpperCamelCase ):
     try:
          int(__UpperCamelCase )
          return True
     except ValueError:
          return False



def  _a			(						__UpperCamelCase ):
     try:
          float(__UpperCamelCase )
          return True
     except ValueError:
          return False


class   a__	:

       def __init__(   self      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     Union[str, Any]										=    args
            a_    :     Optional[int]										=    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

            with open(self.args.csv_file      ,							newline=""""""    ) as csv_file:
                 a_    :     List[Any]										=    csv.DictReader(lowerCamelCase_    )
                 for row in reader:
                      a_    :     List[str]										=    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_    :     Optional[Any]										=    int(row["""result"""]    )
                      elif can_convert_to_float(row["""result"""]    ):
                           # value is not None
                           a_    :     Dict										=    float(row["""result"""]    )

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

                 ((a_)			,			(a_))    :     List[Any]										=    (
                     (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                 )

                 a_    :     str										=    (
                     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_    :     int										=    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_))    :     List[str]										=    (
                          ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                      )

                      a_    :     List[Any]										=    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_    :     int										=    title_str[:-4]
            a_    :     Tuple										=    """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			(						):
     a_    :     Tuple										=    HfArgumentParser(__UpperCamelCase )
     a_    :     Dict										=    parser.parse_args_into_dataclasses()[0]
     a_    :     Dict										=    Plot(args=__UpperCamelCase )
     plot.plot()


if __name__ == "__main__":
       main()



