




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

			UpperCAmelCase__				     = True
except ImportError:
			UpperCAmelCase__				     = False

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







def 		_A(       UpperCamelCase__							:			Namespace				)    -> Any:
							'''simple docstring'''


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







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						@staticmethod
						def     UpperCAmelCase_      ( lowerCamelCase__ :     ArgumentParser				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    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 :     Union[str, Any]				,							lowerCamelCase__ :     bool				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     int=None				,							*lowerCamelCase__ :     Optional[Any]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    testing
													__lowercase      =    testing_file
													__lowercase      =    path






						def     UpperCAmelCase_      ( self :     str				)    ->						Any:
													"""simple docstring"""


													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
													__lowercase      =    [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.'''				)

													__lowercase      =    (
													    Path(lowerCamelCase__				).parent.parent.parent.parent if self._path is None else Path(self._path				).parent.parent
													)
													__lowercase      =    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:
																											__lowercase      =    json.load(lowerCamelCase__				)

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

													__lowercase      =    [directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]

													# Retrieve configuration
													with open(directory + '''/configuration.json'''				,							'''r'''				) as configuration_file:
																				__lowercase      =    json.load(lowerCamelCase__				)

													__lowercase      =    configuration['''lowercase_modelname''']
													__lowercase      =    configuration['''generate_tensorflow_pytorch_and_flax''']
													os.remove(f'{directory}/configuration.json'				)

													__lowercase      =    '''PyTorch''' in generate_tensorflow_pytorch_and_flax
													__lowercase      =    '''TensorFlow''' in generate_tensorflow_pytorch_and_flax
													__lowercase      =    '''Flax''' in generate_tensorflow_pytorch_and_flax

													__lowercase      =    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__ :     str				):
																				with open(lowerCamelCase__				,							'''r'''				) as f:
																											__lowercase      =    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__ :     str				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     List[str]				):
																				# Create temp file
																				__lowercase     ,     __lowercase      =    mkstemp()
																				__lowercase      =    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:
																																																__lowercase      =    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__ :     Tuple				):
																				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__ :     List[Any]				):
																				with open(lowerCamelCase__				) as datafile:
																											__lowercase      =    []
																											__lowercase      =    False
																											__lowercase      =    False
																											for line in datafile:
																																		if "# To replace in: " in line and "##" not in line:
																																									__lowercase      =    line.split('''"'''				)[1]
																																									__lowercase      =    skip_units(lowerCamelCase__				)
																																		elif "# Below: " in line and "##" not in line:
																																									__lowercase      =    line.split('''"'''				)[1]
																																									__lowercase      =    skip_units(lowerCamelCase__				)
																																		elif "# End." in line and "##" not in line:
																																									if not skip_file and not skip_snippet:
																																																replace(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

																																									__lowercase      =    []
																																		elif "# Replace with" in line and "##" not in line:
																																									__lowercase      =    []
																																		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__				)












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       UNetaDModel
						UpperCamelCase_	:       KarrasVeScheduler






						def __init__( self :     Tuple				,							lowerCamelCase__ :     UNetaDModel				,							lowerCamelCase__ :     KarrasVeScheduler				)    ->						Optional[Any]:
													"""simple docstring"""


													super().__init__()
													self.register_modules(unet=lowerCamelCase__				,							scheduler=lowerCamelCase__				)






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							**lowerCamelCase__ :     int				,							)    ->						Union[Tuple, ImagePipelineOutput]:
													"""simple docstring"""


													__lowercase      =    self.unet.config.sample_size
													__lowercase      =    (batch_size, 3, img_size, img_size)

													__lowercase      =    self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													__lowercase      =    randn_tensor(lowerCamelCase__				,							generator=lowerCamelCase__				,							device=self.device				) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(lowerCamelCase__				)

													for t in self.progress_bar(self.scheduler.timesteps				):
																				# here sigma_t == t_i from the paper
																				__lowercase      =    self.scheduler.schedule[t]
																				__lowercase      =    self.scheduler.schedule[t - 1] if t > 0 else 0

																				# 1. Select temporarily increased noise level sigma_hat
																				# 2. Add new noise to move from sample_i to sample_hat
																				__lowercase     ,     __lowercase      =    self.scheduler.add_noise_to_input(lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				)

																				# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																				# The model inputs and output are adjusted by following eq. (213) in [1].
																				__lowercase      =    (sigma_hat / 2) * model((sample_hat + 1) / 2				,							sigma_hat / 2				).sample

																				# 4. Evaluate dx/dt at sigma_hat
																				# 5. Take Euler step from sigma to sigma_prev
																				__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

																				if sigma_prev != 0:
																											# 6. Apply 2nd order correction
																											# The model inputs and output are adjusted by following eq. (213) in [1].
																											__lowercase      =    (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,							sigma_prev / 2				).sample
																											__lowercase      =    self.scheduler.step_correct(
																											    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							step_output.prev_sample				,							step_output['''derivative''']				,							)
																				__lowercase      =    step_output.prev_sample

													__lowercase      =    (sample / 2 + 0.5).clamp(0				,							1				)
													__lowercase      =    sample.cpu().permute(0				,							2				,							3				,							1				).numpy()
													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)






