




"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : int=0.999      ,       snake_case__   : Tuple="cosine"      ,       )     ->  Any:


				'''simple docstring'''






				if alpha_transform_type == "cosine":

								def alpha_bar_fn(snake_case__   : Tuple ):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(snake_case__   : int ):
												return math.exp(t * -12.0 )

				else:
								raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

				lowerCAmelCase  =				[]
				for i in range(snake_case__ ):
								lowerCAmelCase  =				i / num_diffusion_timesteps
								lowerCAmelCase  =				(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(snake_case__ ) / alpha_bar_fn(snake_case__ )      ,       snake_case__ ) )
				return torch.tensor(snake_case__      ,       dtype=torch.floataa )






class       SCREAMING_SNAKE_CASE__					(					_a						,				_a					):
						_a    						=						[e.name for e in KarrasDiffusionSchedulers]
						_a    						=						2






						@register_to_config
						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			int = 1000						,						lowerCAmelCase				:			float = 0.0_0085						,						lowerCAmelCase				:			float = 0.012						,						lowerCAmelCase				:			str = "linear"						,						lowerCAmelCase				:			Optional[Union[np.ndarray, List[float]]] = None						,						lowerCAmelCase				:			str = "epsilon"						,						lowerCAmelCase				:			Optional[bool] = False						,						lowerCAmelCase				:			Optional[bool] = False						,						lowerCAmelCase				:			float = 1.0						,						lowerCAmelCase				:			str = "linspace"						,						lowerCAmelCase				:			int = 0						,						):
										if trained_betas is not None:
														lowerCAmelCase  =				torch.tensor(lowerCAmelCase						,						dtype=torch.floataa					)
										elif beta_schedule == "linear":
														lowerCAmelCase  =				torch.linspace(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						dtype=torch.floataa					)
										elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														lowerCAmelCase  =				(
														    torch.linspace(beta_start**0.5						,						beta_end**0.5						,						lowerCAmelCase						,						dtype=torch.floataa					) ** 2
														)
										elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														lowerCAmelCase  =				betas_for_alpha_bar(lowerCAmelCase						,						alpha_transform_type="""cosine"""					)
										elif beta_schedule == "exp":
														lowerCAmelCase  =				betas_for_alpha_bar(lowerCAmelCase						,						alpha_transform_type="""exp"""					)
										else:
														raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

										lowerCAmelCase  =				1.0 - self.betas
										lowerCAmelCase  =				torch.cumprod(self.alphas						,						dim=0					)

										#  set all values
										self.set_timesteps(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										lowerCAmelCase  =				use_karras_sigmas






						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Optional[int]=None					):
										if schedule_timesteps is None:
														lowerCAmelCase  =				self.timesteps

										lowerCAmelCase  =				(schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter					) == 0:
														lowerCAmelCase  =				1 if len(lowerCAmelCase					) > 1 else 0
										else:
														lowerCAmelCase  =				timestep.cpu().item() if torch.is_tensor(lowerCAmelCase					) else timestep
														lowerCAmelCase  =				self._index_counter[timestep_int]

										return indices[pos].item()






						@property
						def   __lowercase (							self				:			Optional[Any]					):
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5






						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			Union[float, torch.FloatTensor]						,						):
										lowerCAmelCase  =				self.index_for_timestep(lowerCAmelCase					)

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






						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Union[str, torch.device] = None						,						lowerCAmelCase				:			Optional[int] = None						,						):
										lowerCAmelCase  =				num_inference_steps

										lowerCAmelCase  =				num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
														lowerCAmelCase  =				np.linspace(0						,						num_train_timesteps - 1						,						lowerCAmelCase						,						dtype=lowerCAmelCase					)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
														lowerCAmelCase  =				num_train_timesteps // self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														lowerCAmelCase  =				(np.arange(0						,						lowerCAmelCase					) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase					)
														timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
														lowerCAmelCase  =				num_train_timesteps / self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														lowerCAmelCase  =				(np.arange(lowerCAmelCase						,						0						,						-step_ratio					)).round().copy().astype(lowerCAmelCase					)
														timesteps -= 1
										else:
														raise ValueError(
														    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

										lowerCAmelCase  =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
										lowerCAmelCase  =				np.log(lowerCAmelCase					)
										lowerCAmelCase  =				np.interp(lowerCAmelCase						,						np.arange(0						,						len(lowerCAmelCase					)					)						,						lowerCAmelCase					)

										if self.config.use_karras_sigmas:
														lowerCAmelCase  =				self._convert_to_karras(in_sigmas=lowerCAmelCase						,						num_inference_steps=self.num_inference_steps					)
														lowerCAmelCase  =				np.array([self._sigma_to_t(lowerCAmelCase						,						lowerCAmelCase					) for sigma in sigmas]					)

										lowerCAmelCase  =				np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
										lowerCAmelCase  =				torch.from_numpy(lowerCAmelCase					).to(device=lowerCAmelCase					)
										lowerCAmelCase  =				torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

										lowerCAmelCase  =				torch.from_numpy(lowerCAmelCase					)
										lowerCAmelCase  =				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

										if str(lowerCAmelCase					).startswith("""mps"""					):
														# mps does not support float64
														lowerCAmelCase  =				timesteps.to(lowerCAmelCase						,						dtype=torch.floataa					)
										else:
														lowerCAmelCase  =				timesteps.to(device=lowerCAmelCase					)

										# empty dt and derivative
										lowerCAmelCase  =				None
										lowerCAmelCase  =				None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										lowerCAmelCase  =				defaultdict(lowerCAmelCase					)






						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Any					):
										# get log sigma
										lowerCAmelCase  =				np.log(lowerCAmelCase					)

										# get distribution
										lowerCAmelCase  =				log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										lowerCAmelCase  =				np.cumsum((dists >= 0)						,						axis=0					).argmax(axis=0					).clip(max=log_sigmas.shape[0] - 2					)
										lowerCAmelCase  =				low_idx + 1

										lowerCAmelCase  =				log_sigmas[low_idx]
										lowerCAmelCase  =				log_sigmas[high_idx]

										# interpolate sigmas
										lowerCAmelCase  =				(low - log_sigma) / (low - high)
										lowerCAmelCase  =				np.clip(lowerCAmelCase						,						0						,						1					)

										# transform interpolation to time range
										lowerCAmelCase  =				(1 - w) * low_idx + w * high_idx
										lowerCAmelCase  =				t.reshape(sigma.shape					)
										return t






						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				in_sigmas[-1].item()
										lowerCAmelCase  =				in_sigmas[0].item()

										lowerCAmelCase  =				7.0  # 7.0 is the value used in the paper
										lowerCAmelCase  =				np.linspace(0						,						1						,						lowerCAmelCase					)
										lowerCAmelCase  =				sigma_min ** (1 / rho)
										lowerCAmelCase  =				sigma_max ** (1 / rho)
										lowerCAmelCase  =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas






						@property
						def   __lowercase (							self				:			Dict					):
										return self.dt is None






						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Union[torch.FloatTensor, np.ndarray]						,						lowerCAmelCase				:			Union[float, torch.FloatTensor]						,						lowerCAmelCase				:			Union[torch.FloatTensor, np.ndarray]						,						lowerCAmelCase				:			bool = True						,						):
										lowerCAmelCase  =				self.index_for_timestep(lowerCAmelCase					)

										# advance index counter by 1
										lowerCAmelCase  =				timestep.cpu().item() if torch.is_tensor(lowerCAmelCase					) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
														lowerCAmelCase  =				self.sigmas[step_index]
														lowerCAmelCase  =				self.sigmas[step_index + 1]
										else:
														# 2nd order / Heun's method
														lowerCAmelCase  =				self.sigmas[step_index - 1]
														lowerCAmelCase  =				self.sigmas[step_index]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										lowerCAmelCase  =				0
										lowerCAmelCase  =				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

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

										if self.config.clip_sample:
														lowerCAmelCase  =				pred_original_sample.clamp(
														    -self.config.clip_sample_range						,						self.config.clip_sample_range					)

										if self.state_in_first_order:
														# 2. Convert to an ODE derivative for 1st order
														lowerCAmelCase  =				(sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														lowerCAmelCase  =				sigma_next - sigma_hat

														# store for 2nd order step
														lowerCAmelCase  =				derivative
														lowerCAmelCase  =				dt
														lowerCAmelCase  =				sample
										else:
														# 2. 2nd order / Heun's method
														lowerCAmelCase  =				(sample - pred_original_sample) / sigma_next
														lowerCAmelCase  =				(self.prev_derivative + derivative) / 2

														# 3. take prev timestep & sample
														lowerCAmelCase  =				self.dt
														lowerCAmelCase  =				self.sample

														# free dt and derivative
														# Note, this puts the scheduler in "first order mode"
														lowerCAmelCase  =				None
														lowerCAmelCase  =				None
														lowerCAmelCase  =				None

										lowerCAmelCase  =				sample + derivative * dt

										if not return_dict:
														return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase					)






						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			torch.FloatTensor						,						):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowerCAmelCase  =				self.sigmas.to(device=original_samples.device						,						dtype=original_samples.dtype					)
										if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase					):
														# mps does not support float64
														lowerCAmelCase  =				self.timesteps.to(original_samples.device						,						dtype=torch.floataa					)
														lowerCAmelCase  =				timesteps.to(original_samples.device						,						dtype=torch.floataa					)
										else:
														lowerCAmelCase  =				self.timesteps.to(original_samples.device					)
														lowerCAmelCase  =				timesteps.to(original_samples.device					)

										lowerCAmelCase  =				[self.index_for_timestep(lowerCAmelCase						,						lowerCAmelCase					) for t in timesteps]

										lowerCAmelCase  =				sigmas[step_indices].flatten()
										while len(sigma.shape					) < len(original_samples.shape					):
														lowerCAmelCase  =				sigma.unsqueeze(-1					)

										lowerCAmelCase  =				original_samples + noise * sigma
										return noisy_samples






						def __len__(							self				:			Union[str, Any]					):
										return self.config.num_train_timesteps











"""simple docstring"""







import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
a      =							'src/transformers'
a      =							'docs/source/en'
a      =							'.'





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Optional[int]      ,       snake_case__   : Tuple )     ->  Any:


				'''simple docstring'''






				with open(snake_case__      ,       """r"""      ,       encoding="""utf-8"""      ,       newline="""\n""" ) as f:
								lowerCAmelCase  =				f.readlines()
				# Find the start prompt.
				lowerCAmelCase  =				0
				while not lines[start_index].startswith(snake_case__ ):
								start_index += 1
				start_index += 1

				lowerCAmelCase  =				start_index
				while not lines[end_index].startswith(snake_case__ ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index] ) <= 1:
								start_index += 1
				while len(lines[end_index] ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
a      =							'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
a      =							re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a      =							re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a      =							re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
a      =							direct_transformers_import(TRANSFORMERS_PATH)





def 							lowercase					(snake_case__   : int )     ->  Any:


				'''simple docstring'''






				lowerCAmelCase  =				re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""      ,       snake_case__ )
				return [m.group(0 ) for m in matches]





def 							lowercase					(snake_case__   : str      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				2 if text == """✅""" or text == """❌""" else len(snake_case__ )
				lowerCAmelCase  =				(width - text_length) // 2
				lowerCAmelCase  =				width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase  =				{
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCAmelCase  =				{name: config.replace("""Config"""      ,       """""" ) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )

				# Let's lookup through all transformers object (once).
				for attr_name in dir(snake_case__ ):
								lowerCAmelCase  =				None
								if attr_name.endswith("""Tokenizer""" ):
												lowerCAmelCase  =				slow_tokenizers
												lowerCAmelCase  =				attr_name[:-9]
								elif attr_name.endswith("""TokenizerFast""" ):
												lowerCAmelCase  =				fast_tokenizers
												lowerCAmelCase  =				attr_name[:-13]
								elif _re_tf_models.match(snake_case__ ) is not None:
												lowerCAmelCase  =				tf_models
												lowerCAmelCase  =				_re_tf_models.match(snake_case__ ).groups()[0]
								elif _re_flax_models.match(snake_case__ ) is not None:
												lowerCAmelCase  =				flax_models
												lowerCAmelCase  =				_re_flax_models.match(snake_case__ ).groups()[0]
								elif _re_pt_models.match(snake_case__ ) is not None:
												lowerCAmelCase  =				pt_models
												lowerCAmelCase  =				_re_pt_models.match(snake_case__ ).groups()[0]

								if lookup_dict is not None:
												while len(snake_case__ ) > 0:
																if attr_name in model_name_to_prefix.values():
																				lowerCAmelCase  =				True
																				break
																# Try again after removing the last word in the name
																lowerCAmelCase  =				"""""".join(camel_case_split(snake_case__ )[:-1] )

    # Let's build that table!
				lowerCAmelCase  =				list(model_name_to_config.keys() )
				model_names.sort(key=str.lower )
				lowerCAmelCase  =				["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				lowerCAmelCase  =				[len(snake_case__ ) + 2 for c in columns]
				lowerCAmelCase  =				max([len(snake_case__ ) for name in model_names] ) + 2

				# Build the table per se
				lowerCAmelCase  =				"""|""" + """|""".join([_center_text(snake_case__      ,       snake_case__ ) for c, w in zip(snake_case__      ,       snake_case__ )] ) + """|\n"""
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"

				lowerCAmelCase  =				{True: """✅""", False: """❌"""}
				for name in model_names:
								lowerCAmelCase  =				model_name_to_prefix[name]
								lowerCAmelCase  =				[
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(snake_case__      ,       snake_case__ ) for l, w in zip(snake_case__      ,       snake_case__ )] ) + "|\n"
				return table





def 							lowercase					(snake_case__   : Dict=False )     ->  Any:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				_find_text_in_file(
				    filename=os.path.join(snake_case__      ,       """index.md""" )      ,       start_prompt="""<!--This table is updated automatically from the auto modules"""      ,       end_prompt="""<!-- End table-->"""      ,       )
				lowerCAmelCase  =				get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(snake_case__      ,       """index.md""" )      ,       """w"""      ,       encoding="""utf-8"""      ,       newline="""\n""" ) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
								else:
												raise ValueError(
												    """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.""" )


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
		a      =							parser.parse_args()

		check_model_table(args.fix_and_overwrite)





