






import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
		from cookiecutter.main import cookiecutter

		__magic_name__:					List[Any]							=     True
except ImportError:
		__magic_name__:					Union[str, Any]							=     False

__magic_name__:					Any							=     logging.get_logger(__name__)  # pylint: disable=invalid-name

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						return AddNewModelCommand(args.testing,   args.testing_file,   path=args.path			)





class 	snake_case__ (							_lowerCAmelCase				):
	@staticmethod
	def       __magic_name__    (    lowerCAmelCase__			)							->				Optional[int]:
							__magic_name__      :       List[str]       						= parser.add_parser("""add-new-model"""			)
							add_new_model_parser.add_argument("""--testing"""      ,       action="""store_true"""      ,       help="""If in testing mode."""			)
							add_new_model_parser.add_argument("""--testing_file"""      ,       type=lowerCAmelCase__      ,       help="""Configuration file on which to run."""			)
							add_new_model_parser.add_argument(
							    """--path"""      ,       type=lowerCAmelCase__      ,       help="""Path to cookiecutter. Should only be used for testing purposes."""			)
							add_new_model_parser.set_defaults(func=lowerCAmelCase__			)
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__=None      ,       *lowerCAmelCase__			)							->				Dict:
							__magic_name__      :       str       						= testing
							__magic_name__      :       Any       						= testing_file
							__magic_name__      :       Optional[Any]       						= path







	def       __magic_name__    (    self			)							->				List[Any]:
							warnings.warn(
							    """The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. """
							    """It is not actively maintained anymore, so might give a result that won't pass all tests and quality """
							    """checks, you should use `transformers-cli add-new-model-like` instead."""			)
							if not _has_cookiecutter:
													raise ImportError(
													    """Model creation dependencies are required to use the `add_new_model` command. Install them by running """
													    """the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"""			)
							# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
							__magic_name__      :       List[str]       						= [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
							if len(lowerCAmelCase__			) > 0:
													raise ValueError(
													    """Several directories starting with `cookiecutter-template-` in current working directory. """
													    """Please clean your directory by removing all folders starting with `cookiecutter-template-` or """
													    """change your working directory."""			)

							__magic_name__      :       Optional[Any]       						= (
							    Path(lowerCAmelCase__			).parent.parent.parent.parent if self._path is None else Path(self._path			).parent.parent
							)
							__magic_name__      :       Optional[Any]       						= path_to_transformer_root / """templates""" / """adding_a_new_model"""

							# Execute cookiecutter
							if not self._testing:
													cookiecutter(str(lowerCAmelCase__			)			)
							else:
													with open(self._testing_file      ,       """r"""			) as configuration_file:
																			__magic_name__      :       Any       						= json.load(lowerCAmelCase__			)

													cookiecutter(
													    str(path_to_cookiecutter if self._path is None else self._path			)      ,       no_input=lowerCAmelCase__      ,       extra_context=lowerCAmelCase__      ,       )

							__magic_name__      :       List[str]       						= [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]

							# Retrieve configuration
							with open(directory + """/configuration.json"""      ,       """r"""			) as configuration_file:
													__magic_name__      :       int       						= json.load(lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= configuration["""lowercase_modelname"""]
							__magic_name__      :       List[Any]       						= configuration["""generate_tensorflow_pytorch_and_flax"""]
							os.remove(F'{directory}/configuration.json'			)

							__magic_name__      :       Dict       						= """PyTorch""" in generate_tensorflow_pytorch_and_flax
							__magic_name__      :       Optional[int]       						= """TensorFlow""" in generate_tensorflow_pytorch_and_flax
							__magic_name__      :       Dict       						= """Flax""" in generate_tensorflow_pytorch_and_flax

							__magic_name__      :       Any       						= F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
							os.makedirs(lowerCAmelCase__      ,       exist_ok=lowerCAmelCase__			)
							os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'      ,       exist_ok=lowerCAmelCase__			)

							# Tests require submodules as they have parent imports
							with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'      ,       """w"""			):
													pass

							shutil.move(
							    F'{directory}/__init__.py'      ,       F'{model_dir}/__init__.py'      ,       )
							shutil.move(
							    F'{directory}/configuration_{lowercase_model_name}.py'      ,       F'{model_dir}/configuration_{lowercase_model_name}.py'      ,       )

							def remove_copy_lines(lowerCAmelCase__			):
													with open(lowerCAmelCase__      ,       """r"""			) as f:
																			__magic_name__      :       List[Any]       						= f.readlines()
													with open(lowerCAmelCase__      ,       """w"""			) as f:
																			for line in lines:
																									if "# Copied from transformers." not in line:
																															f.write(lowerCAmelCase__			)

							if output_pytorch:
													if not self._testing:
																			remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'			)

													shutil.move(
													    F'{directory}/modeling_{lowercase_model_name}.py'      ,       F'{model_dir}/modeling_{lowercase_model_name}.py'      ,       )

													shutil.move(
													    F'{directory}/test_modeling_{lowercase_model_name}.py'      ,       F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'      ,       )
							else:
													os.remove(F'{directory}/modeling_{lowercase_model_name}.py'			)
													os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'			)

							if output_tensorflow:
													if not self._testing:
																			remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'			)

													shutil.move(
													    F'{directory}/modeling_tf_{lowercase_model_name}.py'      ,       F'{model_dir}/modeling_tf_{lowercase_model_name}.py'      ,       )

													shutil.move(
													    F'{directory}/test_modeling_tf_{lowercase_model_name}.py'      ,       F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'      ,       )
							else:
													os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'			)
													os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'			)

							if output_flax:
													if not self._testing:
																			remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'			)

													shutil.move(
													    F'{directory}/modeling_flax_{lowercase_model_name}.py'      ,       F'{model_dir}/modeling_flax_{lowercase_model_name}.py'      ,       )

													shutil.move(
													    F'{directory}/test_modeling_flax_{lowercase_model_name}.py'      ,       F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'      ,       )
							else:
													os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'			)
													os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'			)

							shutil.move(
							    F'{directory}/{lowercase_model_name}.md'      ,       F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'      ,       )

							shutil.move(
							    F'{directory}/tokenization_{lowercase_model_name}.py'      ,       F'{model_dir}/tokenization_{lowercase_model_name}.py'      ,       )

							shutil.move(
							    F'{directory}/tokenization_fast_{lowercase_model_name}.py'      ,       F'{model_dir}/tokenization_{lowercase_model_name}_fast.py'      ,       )

							from os import fdopen, remove
							from shutil import copymode, move
							from tempfile import mkstemp

							def replace(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			):
													# Create temp file
													__magic_name__						,__magic_name__      :       Union[str, Any]       						= mkstemp()
													__magic_name__      :       Optional[int]       						= False
													with fdopen(lowerCAmelCase__      ,       """w"""			) as new_file:
																			with open(lowerCAmelCase__			) as old_file:
																									for line in old_file:
																															new_file.write(lowerCAmelCase__			)
																															if line_to_copy_below in line:
																																					__magic_name__      :       Dict       						= True
																																					for line_to_copy in lines_to_copy:
																																											new_file.write(lowerCAmelCase__			)

													if not line_found:
																			raise ValueError(F'Line {line_to_copy_below} was not found in file.'			)

													# Copy the file permissions from the old file to the new file
													copymode(lowerCAmelCase__      ,       lowerCAmelCase__			)
													# Remove original file
													remove(lowerCAmelCase__			)
													# Move new file
													move(lowerCAmelCase__      ,       lowerCAmelCase__			)

							def skip_units(lowerCAmelCase__			):
													return (
													    ("generating PyTorch" in line and not output_pytorch)
													    or ("generating TensorFlow" in line and not output_tensorflow)
													    or ("generating Flax" in line and not output_flax)
													)

							def replace_in_files(lowerCAmelCase__			):
													with open(lowerCAmelCase__			) as datafile:
																			__magic_name__      :       Dict       						= []
																			__magic_name__      :       Optional[int]       						= False
																			__magic_name__      :       Union[str, Any]       						= False
																			for line in datafile:
																									if "# To replace in: " in line and "##" not in line:
																															__magic_name__      :       List[str]       						= line.split("""\""""			)[1]
																															__magic_name__      :       Optional[Any]       						= skip_units(lowerCAmelCase__			)
																									elif "# Below: " in line and "##" not in line:
																															__magic_name__      :       int       						= line.split("""\""""			)[1]
																															__magic_name__      :       List[str]       						= skip_units(lowerCAmelCase__			)
																									elif "# End." in line and "##" not in line:
																															if not skip_file and not skip_snippet:
																																					replace(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

																															__magic_name__      :       str       						= []
																									elif "# Replace with" in line and "##" not in line:
																															__magic_name__      :       Any       						= []
																									elif "##" not in line:
																															lines_to_copy.append(lowerCAmelCase__			)

													remove(lowerCAmelCase__			)

							replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'			)
							os.rmdir(lowerCAmelCase__			)








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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					torch.FloatTensor
	lowercase__						:					Optional[torch.FloatTensor]											=   None



def    UpperCamelCase						(	_A,   _A=0.999,   _A="cosine",   ):






						"""simple docstring"""







						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						__magic_name__      :       Optional[Any]       						= []
						for i in range(_A			):
												__magic_name__      :       Dict       						= i / num_diffusion_timesteps
												__magic_name__      :       Any       						= (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_A			) / alpha_bar_fn(_A			),   _A			)			)
						return torch.tensor(_A,   dtype=torch.floataa			)





class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase				):
	@register_to_config
	def __init__(    self      ,       lowerCAmelCase__ = 10_00      ,       lowerCAmelCase__ = "fixed_small_log"      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = 1.0      ,       lowerCAmelCase__ = "epsilon"      ,       lowerCAmelCase__ = "squaredcos_cap_v2"      ,       )							->				Union[str, Any]:
							if beta_schedule != "squaredcos_cap_v2":
													raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""			)

							__magic_name__      :       Tuple       						= betas_for_alpha_bar(lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= 1.0 - self.betas
							__magic_name__      :       str       						= torch.cumprod(self.alphas      ,       dim=0			)
							__magic_name__      :       Any       						= torch.tensor(1.0			)

							# standard deviation of the initial noise distribution
							__magic_name__      :       Tuple       						= 1.0

							# setable values
							__magic_name__      :       List[Any]       						= None
							__magic_name__      :       int       						= torch.from_numpy(np.arange(0      ,       lowerCAmelCase__			)[::-1].copy()			)

							__magic_name__      :       List[Any]       						= variance_type
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				torch.FloatTensor:
							return sample
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				str:
							__magic_name__      :       List[Any]       						= num_inference_steps
							__magic_name__      :       Union[str, Any]       						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							__magic_name__      :       List[Any]       						= (np.arange(0      ,       lowerCAmelCase__			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
							__magic_name__      :       Dict       						= torch.from_numpy(lowerCAmelCase__			).to(lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None			)							->				Tuple:
							if prev_timestep is None:
													__magic_name__      :       int       						= t - 1

							__magic_name__      :       Optional[Any]       						= self.alphas_cumprod[t]
							__magic_name__      :       Any       						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							__magic_name__      :       Tuple       						= 1 - alpha_prod_t
							__magic_name__      :       int       						= 1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
													__magic_name__      :       List[str]       						= self.betas[t]
							else:
													__magic_name__      :       List[Any]       						= 1 - alpha_prod_t / alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							__magic_name__      :       Dict       						= beta_prod_t_prev / beta_prod_t * beta

							if variance_type is None:
													__magic_name__      :       str       						= self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small_log":
													__magic_name__      :       str       						= torch.log(torch.clamp(lowerCAmelCase__      ,       min=1e-2_0			)			)
													__magic_name__      :       Optional[Any]       						= torch.exp(0.5 * variance			)
							elif variance_type == "learned_range":
													# NOTE difference with DDPM scheduler
													__magic_name__      :       List[str]       						= variance.log()
													__magic_name__      :       Optional[int]       						= beta.log()

													__magic_name__      :       Any       						= (predicted_variance + 1) / 2
													__magic_name__      :       Any       						= frac * max_log + (1 - frac) * min_log

							return variance
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__ = True      ,       )							->				Union[UnCLIPSchedulerOutput, Tuple]:
							__magic_name__      :       List[Any]       						= timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
													__magic_name__						,__magic_name__      :       List[Any]       						= torch.split(lowerCAmelCase__      ,       sample.shape[1]      ,       dim=1			)
							else:
													__magic_name__      :       List[str]       						= None

							# 1. compute alphas, betas
							if prev_timestep is None:
													__magic_name__      :       Union[str, Any]       						= t - 1

							__magic_name__      :       List[str]       						= self.alphas_cumprod[t]
							__magic_name__      :       Dict       						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							__magic_name__      :       Any       						= 1 - alpha_prod_t
							__magic_name__      :       Dict       						= 1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
													__magic_name__      :       Union[str, Any]       						= self.betas[t]
													__magic_name__      :       int       						= self.alphas[t]
							else:
													__magic_name__      :       Tuple       						= 1 - alpha_prod_t / alpha_prod_t_prev
													__magic_name__      :       Tuple       						= 1 - beta

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
													__magic_name__      :       Optional[int]       						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
													__magic_name__      :       Tuple       						= model_output
							else:
													raise ValueError(
													    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
													    """ for the UnCLIPScheduler."""			)

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
													__magic_name__      :       Tuple       						= torch.clamp(
													    lowerCAmelCase__      ,       -self.config.clip_sample_range      ,       self.config.clip_sample_range			)

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							__magic_name__      :       List[Any]       						= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
							__magic_name__      :       Dict       						= alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							__magic_name__      :       str       						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							__magic_name__      :       Tuple       						= 0
							if t > 0:
													__magic_name__      :       Any       						= randn_tensor(
													    model_output.shape      ,       dtype=model_output.dtype      ,       generator=lowerCAmelCase__      ,       device=model_output.device			)

													__magic_name__      :       Tuple       						= self._get_variance(
													    lowerCAmelCase__      ,       predicted_variance=lowerCAmelCase__      ,       prev_timestep=lowerCAmelCase__      ,       )

													if self.variance_type == "fixed_small_log":
																			__magic_name__      :       Tuple       						= variance
													elif self.variance_type == "learned_range":
																			__magic_name__      :       int       						= (0.5 * variance).exp()
													else:
																			raise ValueError(
																			    F'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
																			    """ for the UnCLIPScheduler."""			)

													__magic_name__      :       Tuple       						= variance * variance_noise

							__magic_name__      :       List[str]       						= pred_prev_sample + variance

							if not return_dict:
													return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase__      ,       pred_original_sample=lowerCAmelCase__			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       )							->				torch.FloatTensor:
							# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
							__magic_name__      :       List[str]       						= self.alphas_cumprod.to(device=original_samples.device      ,       dtype=original_samples.dtype			)
							__magic_name__      :       Any       						= timesteps.to(original_samples.device			)

							__magic_name__      :       int       						= alphas_cumprod[timesteps] ** 0.5
							__magic_name__      :       Union[str, Any]       						= sqrt_alpha_prod.flatten()
							while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
													__magic_name__      :       int       						= sqrt_alpha_prod.unsqueeze(-1			)

							__magic_name__      :       Any       						= (1 - alphas_cumprod[timesteps]) ** 0.5
							__magic_name__      :       str       						= sqrt_one_minus_alpha_prod.flatten()
							while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
													__magic_name__      :       Any       						= sqrt_one_minus_alpha_prod.unsqueeze(-1			)

							__magic_name__      :       Any       						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples
