from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)




@flax.struct.dataclass
class        lowercase_  :



	"""simple docstring"""





	UpperCAmelCase_  : CommonSchedulerState

	# setable values
	UpperCAmelCase_  : jnp.ndarray
	UpperCAmelCase_  : jnp.ndarray
	UpperCAmelCase_  : jnp.ndarray
	UpperCAmelCase_  : Optional[int]       =    None

	# running values
	UpperCAmelCase_  : Optional[jnp.ndarray]       =    None


	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[Any]:
								return cls(common=__SCREAMING_SNAKE_CASE		,      init_noise_sigma=__SCREAMING_SNAKE_CASE		,      timesteps=__SCREAMING_SNAKE_CASE		,      sigmas=__SCREAMING_SNAKE_CASE    )




@dataclass
class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  : LMSDiscreteSchedulerState




class        lowercase_  (  UpperCamelCase_				,					UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  : Optional[Any]       =    [e.name for e in FlaxKarrasDiffusionSchedulers]

	UpperCAmelCase_  : jnp.dtype


	@property
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								return True


	@register_to_config
	def __init__(							self		,      __SCREAMING_SNAKE_CASE = 1000		,      __SCREAMING_SNAKE_CASE = 0.0_0_0_1		,      __SCREAMING_SNAKE_CASE = 0.0_2		,      __SCREAMING_SNAKE_CASE = "linear"		,      __SCREAMING_SNAKE_CASE = None		,      __SCREAMING_SNAKE_CASE = "epsilon"		,      __SCREAMING_SNAKE_CASE = jnp.floataa		,      )						->Any:
								lowerCAmelCase     		=		dtype


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE = None    )						->LMSDiscreteSchedulerState:
								if common is None:
															lowerCAmelCase     		=		CommonSchedulerState.create(self    )

								lowerCAmelCase     		=		jnp.arange(0		,      self.config.num_train_timesteps    ).round()[::-1]
								lowerCAmelCase     		=		((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

								# standard deviation of the initial noise distribution
								lowerCAmelCase     		=		sigmas.max()

								return LMSDiscreteSchedulerState.create(
								    common=__SCREAMING_SNAKE_CASE		,      init_noise_sigma=__SCREAMING_SNAKE_CASE		,      timesteps=__SCREAMING_SNAKE_CASE		,      sigmas=__SCREAMING_SNAKE_CASE		,      )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->jnp.ndarray:
								((lowerCAmelCase)		,				)     		=		jnp.where(state.timesteps == timestep		,      size=1    )
								lowerCAmelCase     		=		step_index[0]

								lowerCAmelCase     		=		state.sigmas[step_index]
								lowerCAmelCase     		=		sample / ((sigma**2 + 1) ** 0.5)
								return sample


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[Any]:

								def lms_derivative(__SCREAMING_SNAKE_CASE    ):
															lowerCAmelCase     		=		1.0
															for k in range(__SCREAMING_SNAKE_CASE    ):
																						if current_order == k:
																													continue
																						prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
															return prod

								lowerCAmelCase     		=		integrate.quad(__SCREAMING_SNAKE_CASE		,      state.sigmas[t]		,      state.sigmas[t + 1]		,      epsrel=1e-4    )[0]

								return integrated_coeff


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE = ()    )						->LMSDiscreteSchedulerState:
								lowerCAmelCase     		=		jnp.linspace(self.config.num_train_timesteps - 1		,      0		,      __SCREAMING_SNAKE_CASE		,      dtype=self.dtype    )

								lowerCAmelCase     		=		jnp.floor(__SCREAMING_SNAKE_CASE    ).astype(jnp.intaa    )
								lowerCAmelCase     		=		jnp.ceil(__SCREAMING_SNAKE_CASE    ).astype(jnp.intaa    )

								lowerCAmelCase     		=		jnp.mod(__SCREAMING_SNAKE_CASE		,      1.0    )

								lowerCAmelCase     		=		((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
								lowerCAmelCase     		=		(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
								lowerCAmelCase     		=		jnp.concatenate([sigmas, jnp.array([0.0]		,      dtype=self.dtype    )]    )

								lowerCAmelCase     		=		timesteps.astype(jnp.intaa    )

								# initial running values
								lowerCAmelCase     		=		jnp.zeros((0,) + shape		,      dtype=self.dtype    )

								return state.replace(
								    timesteps=__SCREAMING_SNAKE_CASE		,      sigmas=__SCREAMING_SNAKE_CASE		,      num_inference_steps=__SCREAMING_SNAKE_CASE		,      derivatives=__SCREAMING_SNAKE_CASE		,      )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE = 4		,      __SCREAMING_SNAKE_CASE = True		,      )						->Union[FlaxLMSSchedulerOutput, Tuple]:
								if state.num_inference_steps is None:
															raise ValueError(
															    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''    )

								lowerCAmelCase     		=		state.sigmas[timestep]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
															lowerCAmelCase     		=		sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCAmelCase     		=		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								else:
															raise ValueError(
															    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"    )

								# 2. Convert to an ODE derivative
								lowerCAmelCase     		=		(sample - pred_original_sample) / sigma
								lowerCAmelCase     		=		state.replace(derivatives=jnp.append(state.derivatives		,      __SCREAMING_SNAKE_CASE    )    )
								if len(state.derivatives    ) > order:
															lowerCAmelCase     		=		state.replace(derivatives=jnp.delete(state.derivatives		,      0    )    )

								# 3. Compute linear multistep coefficients
								lowerCAmelCase     		=		min(timestep + 1		,      __SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		[self.get_lms_coefficient(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    ) for curr_order in range(__SCREAMING_SNAKE_CASE    )]

								# 4. Compute previous sample based on the derivatives path
								lowerCAmelCase     		=		sample + sum(
								    coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE		,      reversed(state.derivatives    )    )    )

								if not return_dict:
															return (prev_sample, state)

								return FlaxLMSSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE		,      state=__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      )						->jnp.ndarray:
								lowerCAmelCase     		=		state.sigmas[timesteps].flatten()
								lowerCAmelCase     		=		broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE		,      noise.shape    )

								lowerCAmelCase     		=		original_samples + noise * sigma

								return noisy_samples




	def __len__(							self    )						->str:
								return self.config.num_train_timesteps




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        SCREAMING_SNAKE_CASE_					(						snake_case__=None       ,  snake_case__=None							)					->     Optional[Any]:
							return field(default_factory=lambda: default       ,  metadata=snake_case__							)




@dataclass
class        lowercase_  :



	"""simple docstring"""





	UpperCAmelCase_  : str       =    field(
	    metadata={"""help""": """The csv file to plot."""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Disable logarithmic scale when plotting"""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={
	        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
	    }				,					)
	UpperCAmelCase_  : Optional[str]       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}				,					)
	UpperCAmelCase_  : 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        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Dict:
							try:
														int(snake_case__							)
														return True
							except ValueError:
														return False

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Union[str, Any]:
							try:
														float(snake_case__							)
														return True
							except ValueError:
														return False




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		args
								lowerCAmelCase     		=		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

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




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

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

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

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

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

															title_str += F" {label_model_name} vs."

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

								# plot
								plt.title(__SCREAMING_SNAKE_CASE    )
								plt.xlabel(__SCREAMING_SNAKE_CASE    )
								plt.ylabel(__SCREAMING_SNAKE_CASE    )
								plt.legend()

								if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file    )
								else:
															plt.show()

def        SCREAMING_SNAKE_CASE_					(						)					->     List[str]:
							lowerCAmelCase     		=		HfArgumentParser(snake_case__							)
							lowerCAmelCase     		=		parser.parse_args_into_dataclasses()[0]
							lowerCAmelCase     		=		Plot(args=snake_case__							)
							plot.plot()


if __name__ == "__main__":
	main()



