


"""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
UpperCAmelCase      		=			'''src/transformers'''
UpperCAmelCase      		=			'''docs/source/en'''
UpperCAmelCase      		=			'''.'''

def       lowerCamelCase					(a_						:Any				,				a_						:List[str]				,				a_						:Optional[int])     ->							List[str]:






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

       lowercase		:Union[str, Any]       							=				start_index
       while not lines[end_index].startswith(__UpperCamelCase):
              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 |
UpperCAmelCase      		=			'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
UpperCAmelCase      		=			re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
UpperCAmelCase      		=			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.
UpperCAmelCase      		=			re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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

def       lowerCamelCase					(a_						:int)     ->							List[str]:
       lowercase		:int       							=				re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''				,				__UpperCamelCase)
       return [m.group(0) for m in matches]

def       lowerCamelCase					(a_						:int				,				a_						:int)     ->							int:
       lowercase		:Dict       							=				2 if text == """✅""" or text == """❌""" else len(__UpperCamelCase)
       lowercase		:Tuple       							=				(width - text_length) // 2
       lowercase		:Any       							=				width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent

def       lowerCamelCase					()     ->							List[Any]:
       lowercase		:List[Any]       							=				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       lowercase		:Optional[Any]       							=				{
           name: config_maping_names[code]
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if code in config_maping_names
       }
       lowercase		:List[str]       							=				{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.
       lowercase		:Dict       							=				collections.defaultdict(__UpperCamelCase)
       lowercase		:List[Any]       							=				collections.defaultdict(__UpperCamelCase)
       lowercase		:str       							=				collections.defaultdict(__UpperCamelCase)
       lowercase		:str       							=				collections.defaultdict(__UpperCamelCase)
       lowercase		:Dict       							=				collections.defaultdict(__UpperCamelCase)

       # Let's lookup through all transformers object (once).
       for attr_name in dir(__UpperCamelCase):
              lowercase		:Union[str, Any]       							=				None
              if attr_name.endswith('''Tokenizer'''):
                     lowercase		:Dict       							=				slow_tokenizers
                     lowercase		:Any       							=				attr_name[:-9]
              elif attr_name.endswith('''TokenizerFast'''):
                     lowercase		:Optional[Any]       							=				fast_tokenizers
                     lowercase		:str       							=				attr_name[:-13]
              elif _re_tf_models.match(__UpperCamelCase) is not None:
                     lowercase		:List[Any]       							=				tf_models
                     lowercase		:Tuple       							=				_re_tf_models.match(__UpperCamelCase).groups()[0]
              elif _re_flax_models.match(__UpperCamelCase) is not None:
                     lowercase		:Dict       							=				flax_models
                     lowercase		:Tuple       							=				_re_flax_models.match(__UpperCamelCase).groups()[0]
              elif _re_pt_models.match(__UpperCamelCase) is not None:
                     lowercase		:List[Any]       							=				pt_models
                     lowercase		:Any       							=				_re_pt_models.match(__UpperCamelCase).groups()[0]

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

    # Let's build that table!
       lowercase		:Dict       							=				list(model_name_to_config.keys())
       model_names.sort(key=str.lower)
       lowercase		:str       							=				["""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).
       lowercase		:str       							=				[len(__UpperCamelCase) + 2 for c in columns]
       lowercase		:Dict       							=				max([len(__UpperCamelCase) for name in model_names]) + 2

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

       lowercase		:List[Any]       							=				{True: """✅""", False: """❌"""}
       for name in model_names:
              lowercase		:Union[str, Any]       							=				model_name_to_prefix[name]
              lowercase		:Optional[int]       							=				[
                  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(__UpperCamelCase				,				__UpperCamelCase) for l, w in zip(__UpperCamelCase				,				__UpperCamelCase)]) + "|\n"
       return table

def       lowerCamelCase					(a_						:Any=False)     ->							Any:
       lowercase		:Optional[int]       							=				_find_text_in_file(
           filename=os.path.join(__UpperCamelCase				,				'''index.md''')				,				start_prompt='''<!--This table is updated automatically from the auto modules'''				,				end_prompt='''<!-- End table-->'''				,				)
       lowercase		:int       							=				get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(__UpperCamelCase				,				'''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__":
 UpperCAmelCase      		=			argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 UpperCAmelCase      		=			parser.parse_args()

 check_model_table(args.fix_and_overwrite)





"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      torch.FloatTensor
       __A			:      torch.FloatTensor







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase     ):
       __A			:      Any        =   1






       @register_to_config
       def __init__(   self  :    int  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    float = 0.15  ,     snake_case__  :    float = 0.01  ,     snake_case__  :    float = 13_48.0  ,     snake_case__  :    float = 1e-5  ,     snake_case__  :    int = 1  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				sigma_max

              # setable values
              lowercase		:List[str]       							=				None

              self.set_sigmas(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[int] = None				):

              '''simple docstring'''






              return sample






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    Union[str, torch.device] = None				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

              lowercase		:Tuple       							=				torch.linspace(1  ,     snake_case__  ,     snake_case__  ,     device=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None				):

              '''simple docstring'''






              lowercase		:int       							=				sigma_min if sigma_min is not None else self.config.sigma_min
              lowercase		:Any       							=				sigma_max if sigma_max is not None else self.config.sigma_max
              lowercase		:Tuple       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
              if self.timesteps is None:
                     self.set_timesteps(snake_case__  ,     snake_case__				)

              lowercase		:Union[str, Any]       							=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
              lowercase		:str       							=				torch.exp(torch.linspace(math.log(snake_case__				)  ,     math.log(snake_case__				)  ,     snake_case__				)				)
              lowercase		:List[str]       							=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    int				):

              '''simple docstring'''






              return torch.where(
                  timesteps == 0  ,     torch.zeros_like(t.to(timesteps.device				)				)  ,     self.discrete_sigmas[timesteps - 1].to(timesteps.device				)  ,     )






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    int  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''simple docstring'''






              if self.timesteps is None:
                     raise ValueError(
                         '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''				)

              lowercase		:Optional[int]       							=				timestep * torch.ones(
                  sample.shape[0]  ,     device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
              lowercase		:Optional[int]       							=				(timestep * (len(self.timesteps				) - 1)).long()

              # mps requires indices to be in the same device, so we use cpu as is the default with cuda
              lowercase		:Union[str, Any]       							=				timesteps.to(self.discrete_sigmas.device				)

              lowercase		:List[Any]       							=				self.discrete_sigmas[timesteps].to(sample.device				)
              lowercase		:str       							=				self.get_adjacent_sigma(snake_case__  ,     snake_case__				).to(sample.device				)
              lowercase		:Optional[Any]       							=				torch.zeros_like(snake_case__				)
              lowercase		:List[Any]       							=				(sigma**2 - adjacent_sigma**2) ** 0.5

              # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
              # also equation 47 shows the analog from SDE models to ancestral sampling methods
              lowercase		:Union[str, Any]       							=				diffusion.flatten()
              while len(diffusion.shape				) < len(sample.shape				):
                     lowercase		:Dict       							=				diffusion.unsqueeze(-1				)
              lowercase		:List[Any]       							=				drift - diffusion**2 * model_output

              #  equation 6: sample noise for the diffusion term of
              lowercase		:List[Any]       							=				randn_tensor(
                  sample.shape  ,     layout=sample.layout  ,     generator=snake_case__  ,     device=sample.device  ,     dtype=sample.dtype				)
              lowercase		:int       							=				sample - drift  # subtract because `dt` is a small negative timestep
              # TODO is the variable diffusion the correct scaling term for the noise?
              lowercase		:Any       							=				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

              if not return_dict:
                     return (prev_sample, prev_sample_mean)

              return SdeVeOutput(prev_sample=snake_case__  ,     prev_sample_mean=snake_case__				)






       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''simple docstring'''






              if self.timesteps is None:
                     raise ValueError(
                         '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''				)

              # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
              # sample noise for correction
              lowercase		:Tuple       							=				randn_tensor(sample.shape  ,     layout=sample.layout  ,     generator=snake_case__				).to(sample.device				)

              # compute step size from the model_output, the noise, and the snr
              lowercase		:List[Any]       							=				torch.norm(model_output.reshape(model_output.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:List[str]       							=				torch.norm(noise.reshape(noise.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:int       							=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
              lowercase		:Optional[Any]       							=				step_size * torch.ones(sample.shape[0]				).to(sample.device				)
              # self.repeat_scalar(step_size, sample.shape[0])

              # compute corrected sample: model_output term and noise term
              lowercase		:Union[str, Any]       							=				step_size.flatten()
              while len(step_size.shape				) < len(sample.shape				):
                     lowercase		:Union[str, Any]       							=				step_size.unsqueeze(-1				)
              lowercase		:Union[str, Any]       							=				sample + step_size * model_output
              lowercase		:Any       							=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				timesteps.to(original_samples.device				)
              lowercase		:str       							=				self.discrete_sigmas.to(original_samples.device				)[timesteps]
              lowercase		:Optional[int]       							=				(
                  noise * sigmas[:, None, None, None]
                  if noise is not None
                  else torch.randn_like(snake_case__				) * sigmas[:, None, None, None]
              )
              lowercase		:str       							=				noise + original_samples
              return noisy_samples







       def __len__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return self.config.num_train_timesteps

