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

_UpperCAmelCase					: List[str]      =       logging.get_logger(__name__)  # pylint: disable=invalid-name


def       A       (					lowercase )   ->  Dict:
		'''simple docstring'''

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


class 				lowercase					(		__UpperCAmelCase   ):



		@staticmethod
		def __UpperCamelCase ( A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     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=A_				,      help='Configuration file on which to run.'       )
				add_new_model_parser.add_argument(
				    '--path'				,      type=A_				,      help='Path to cookiecutter. Should only be used for testing purposes.'       )
				add_new_model_parser.set_defaults(func=A_       )



		def __init__( self				,      A_				,      A_				,      A_=None				,      *A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     testing
				UpperCamelCase		=     testing_file
				UpperCamelCase		=     path



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""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
				UpperCamelCase		=     [directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
				if len(A_       ) > 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.'       )

				UpperCamelCase		=     (
				    Path(A_       ).parent.parent.parent.parent if self._path is None else Path(self._path       ).parent.parent
				)
				UpperCamelCase		=     path_to_transformer_root / 'templates' / 'adding_a_new_model'

				# Execute cookiecutter
				if not self._testing:
						cookiecutter(str(A_       )       )
				else:
						with open(self._testing_file				,      'r'       ) as configuration_file:
								UpperCamelCase		=     json.load(A_       )

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

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

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

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

				UpperCamelCase		=     'PyTorch' in generate_tensorflow_pytorch_and_flax
				UpperCamelCase		=     'TensorFlow' in generate_tensorflow_pytorch_and_flax
				UpperCamelCase		=     'Flax' in generate_tensorflow_pytorch_and_flax

				UpperCamelCase		=     F'''{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'''
				os.makedirs(A_				,      exist_ok=A_       )
				os.makedirs(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}'''				,      exist_ok=A_       )

				# 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(A_       ):
						with open(A_				,      'r'       ) as f:
								UpperCamelCase		=     f.readlines()
						with open(A_				,      'w'       ) as f:
								for line in lines:
										if "# Copied from transformers." not in line:
												f.write(A_       )

				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(A_				,      A_				,      A_       ):
						# Create temp file
						UpperCamelCase		=     mkstemp()
						UpperCamelCase		=     False
						with fdopen(A_				,      'w'       ) as new_file:
								with open(A_       ) as old_file:
										for line in old_file:
												new_file.write(A_       )
												if line_to_copy_below in line:
														UpperCamelCase		=     True
														for line_to_copy in lines_to_copy:
																new_file.write(A_       )

						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(A_				,      A_       )
						# Remove original file
						remove(A_       )
						# Move new file
						move(A_				,      A_       )

				def skip_units(A_       ):
						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(A_       ):
						with open(A_       ) as datafile:
								UpperCamelCase		=     []
								UpperCamelCase		=     False
								UpperCamelCase		=     False
								for line in datafile:
										if "# To replace in: " in line and "##" not in line:
												UpperCamelCase		=     line.split('"'       )[1]
												UpperCamelCase		=     skip_units(A_       )
										elif "# Below: " in line and "##" not in line:
												UpperCamelCase		=     line.split('"'       )[1]
												UpperCamelCase		=     skip_units(A_       )
										elif "# End." in line and "##" not in line:
												if not skip_file and not skip_snippet:
														replace(A_				,      A_				,      A_       )

												UpperCamelCase		=     []
										elif "# Replace with" in line and "##" not in line:
												UpperCamelCase		=     []
										elif "##" not in line:
												lines_to_copy.append(A_       )

						remove(A_       )

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







from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase)			->						Optional[Any]:







          '''simple docstring'''

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

   @torch.no_grad()
   def __call__(					self					,  lowercase = 1					,  lowercase = 100					,  lowercase = None					,  lowercase = None					,  lowercase = True					,  )			->						Union[AudioPipelineOutput, Tuple]:







          '''simple docstring'''

          if audio_length_in_s is None:
                 a__						:	Optional[int] =				self.unet.config.sample_size / self.unet.config.sample_rate

          a__						:	int =				audio_length_in_s * self.unet.config.sample_rate

          a__						:	Union[str, Any] =				2 ** len(self.unet.up_blocks)
          if sample_size < 3 * down_scale_factor:
                 raise ValueError(
                     F'{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'
                     F' {3 * down_scale_factor / self.unet.config.sample_rate}.')

          a__						:	str =				int(lowercase)
          if sample_size % down_scale_factor != 0:
                 a__						:	List[str] =				(
                     (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                 ) * down_scale_factor
                 logger.info(
                     F'{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'
                     F' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'
                     ' process.')
          a__						:	List[Any] =				int(lowercase)

          a__						:	int =				next(iter(self.unet.parameters())).dtype
          a__						:	Tuple =				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(lowercase					,  lowercase) and len(lowercase) != batch_size:
                 raise ValueError(
                     F'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
                     F' size of {batch_size}. Make sure the batch size matches the length of the generators.')

          a__						:	Optional[Any] =				randn_tensor(lowercase					,  generator=lowercase					,  device=self.device					,  dtype=lowercase)

          # set step values
          self.scheduler.set_timesteps(lowercase					,  device=audio.device)
          a__						:	Union[str, Any] =				self.scheduler.timesteps.to(lowercase)

          for t in self.progress_bar(self.scheduler.timesteps):
                 # 1. predict noise model_output
                 a__						:	Dict =				self.unet(lowercase					,  lowercase).sample

                 # 2. compute previous image: x_t -> t_t-1
                 a__						:	Any =				self.scheduler.step(lowercase					,  lowercase					,  lowercase).prev_sample

          a__						:	str =				audio.clamp(-1					,  1).float().cpu().numpy()

          a__						:	List[Any] =				audio[:, :, :original_sample_size]

          if not return_dict:
                 return (audio,)

          return AudioPipelineOutput(audios=lowercase)


