"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			str							=		DanceDiffusionPipeline
     _UpperCamelCase							:			int							=		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
     _UpperCamelCase							:			Union[str, Any]							=		PipelineTesterMixin.required_optional_params - {
         "callback",
         "latents",
         "callback_steps",
         "output_type",
         "num_images_per_prompt",
     }
     _UpperCamelCase							:			List[Any]							=		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
     _UpperCamelCase							:			Optional[Any]							=		False
     _UpperCamelCase							:			Optional[int]							=		False






     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       Any  			=     UNetaDModel(
           block_out_channels=(32, 32, 64)				,     extra_in_channels=16				,     sample_size=512				,     sample_rate=16000				,     in_channels=2				,     out_channels=2				,     flip_sin_to_cos=a__				,     use_timestep_embedding=a__				,     time_embedding_type="""fourier"""				,     mid_block_type="""UNetMidBlock1D"""				,     down_block_types=("""DownBlock1DNoSkip""", """DownBlock1D""", """AttnDownBlock1D""")				,     up_block_types=("""AttnUpBlock1D""", """UpBlock1D""", """UpBlock1DNoSkip""")				,     )
       _lowerCAmelCase						:       List[str]  			=     IPNDMScheduler()

       _lowerCAmelCase						:       Dict  			=     {
           """unet""": unet,
           """scheduler""": scheduler,
       }
       return components






     def     __A  (		self				,     a__				,     a__=0  ):
       if str(a__  ).startswith("""mps"""  ):
         _lowerCAmelCase						:       List[str]  			=     torch.manual_seed(a__  )
       else:
         _lowerCAmelCase						:       Dict  			=     torch.Generator(device=a__  ).manual_seed(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     {
           """batch_size""": 1,
           """generator""": generator,
           """num_inference_steps""": 4,
       }
       return inputs






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _lowerCAmelCase						:       Dict  			=     self.get_dummy_components()
       _lowerCAmelCase						:       Union[str, Any]  			=     DanceDiffusionPipeline(**a__  )
       _lowerCAmelCase						:       Tuple  			=     pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     self.get_dummy_inputs(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     pipe(**a__  )
       _lowerCAmelCase						:       int  			=     output.audios

       _lowerCAmelCase						:       List[Any]  			=     audio[0, -3:, -3:]

       assert audio.shape == (1, 2, components["unet"].sample_size)
       _lowerCAmelCase						:       Dict  			=     np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]  )
       assert np.abs(audio_slice.flatten() - expected_slice  ).max() < 1e-2






     @skip_mps
     def     __A  (		self  ):
       return super().test_save_load_local()






     @skip_mps
     def     __A  (		self  ):
       return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3  )






     @skip_mps
     def     __A  (		self  ):
       return super().test_save_load_optional_components()






     @skip_mps
     def     __A  (		self  ):
       return super().test_attention_slicing_forward_pass()






     def     __A  (		self  ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







@slow
@require_torch_gpu
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     torch_device

       _lowerCAmelCase						:       List[str]  			=     DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""  )
       _lowerCAmelCase						:       List[Any]  			=     pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Union[str, Any]  			=     pipe(generator=a__				,     num_inference_steps=100				,     audio_length_in_s=4.0_9_6  )
       _lowerCAmelCase						:       Dict  			=     output.audios

       _lowerCAmelCase						:       Union[str, Any]  			=     audio[0, -3:, -3:]

       assert audio.shape == (1, 2, pipe.unet.sample_size)
       _lowerCAmelCase						:       int  			=     np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]  )

       assert np.abs(audio_slice.flatten() - expected_slice  ).max() < 1e-2






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     torch_device

       _lowerCAmelCase						:       Tuple  			=     DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""				,     torch_dtype=torch.floataa  )
       _lowerCAmelCase						:       str  			=     pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Dict  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Optional[int]  			=     pipe(generator=a__				,     num_inference_steps=100				,     audio_length_in_s=4.0_9_6  )
       _lowerCAmelCase						:       Any  			=     output.audios

       _lowerCAmelCase						:       Tuple  			=     audio[0, -3:, -3:]

       assert audio.shape == (1, 2, pipe.unet.sample_size)
       _lowerCAmelCase						:       Tuple  			=     np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]  )

       assert np.abs(audio_slice.flatten() - expected_slice  ).max() < 1e-2






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

      lowercase__       =True
except ImportError:
      lowercase__       =False

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




def    __UpperCamelCase     (				lowerCAmelCase__							:				Namespace  ):
 return AddNewModelCommand(args.testing  ,   args.testing_file  ,   path=args.path  )



class 		UpperCamelCase__  (       __lowercase    ):
   @staticmethod
   def       lowerCAmelCase			(snake_case_ :					ArgumentParser   ):
    __a :  List[Any]						=  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=snake_case_					,				help='''Configuration file on which to run.'''   )
    add_new_model_parser.add_argument(
        '''--path'''					,				type=snake_case_					,				help='''Path to cookiecutter. Should only be used for testing purposes.'''   )
    add_new_model_parser.set_defaults(func=snake_case_   )
   def __init__(self :					Dict					,				snake_case_ :					bool					,				snake_case_ :					str					,				snake_case_ :					Dict=None					,				*snake_case_ :					Optional[Any]   ):
    __a :  Union[str, Any]						=  testing
    __a :  List[Any]						=  testing_file
    __a :  Any						=  path







   def       lowerCAmelCase			(self :					int   ):
    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
    __a :  Union[str, Any]						=  [directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:2_2]]
    if len(snake_case_   ) > 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.'''   )

    __a :  Union[str, Any]						=  (
        Path(snake_case_   ).parent.parent.parent.parent if self._path is None else Path(self._path   ).parent.parent
    )
    __a :  Union[str, Any]						=  path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

    # Execute cookiecutter
    if not self._testing:
     cookiecutter(str(snake_case_   )   )
    else:
     with open(self._testing_file					,				'''r'''   ) as configuration_file:
      __a :  List[Any]						=  json.load(snake_case_   )

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

    __a :  List[str]						=  [directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:2_2]][0]

    # Retrieve configuration
    with open(directory + '''/configuration.json'''					,				'''r'''   ) as configuration_file:
     __a :  Optional[Any]						=  json.load(snake_case_   )

    __a :  str						=  configuration['''lowercase_modelname''']
    __a :  int						=  configuration['''generate_tensorflow_pytorch_and_flax''']
    os.remove(f"{directory}/configuration.json"   )

    __a :  Any						=  '''PyTorch''' in generate_tensorflow_pytorch_and_flax
    __a :  Dict						=  '''TensorFlow''' in generate_tensorflow_pytorch_and_flax
    __a :  Optional[int]						=  '''Flax''' in generate_tensorflow_pytorch_and_flax

    __a :  Dict						=  f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
    os.makedirs(snake_case_					,				exist_ok=snake_case_   )
    os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}"					,				exist_ok=snake_case_   )

    # 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(snake_case_ :					Union[str, Any]   ):
     with open(snake_case_					,				'''r'''   ) as f:
      __a :  Union[str, Any]						=  f.readlines()
     with open(snake_case_					,				'''w'''   ) as f:
      for line in lines:
       if "# Copied from transformers." not in line:
        f.write(snake_case_   )

    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(snake_case_ :					str					,				snake_case_ :					str					,				snake_case_ :					List[str]   ):
     # Create temp file
     __a				,       __a :  Tuple						=  mkstemp()
     __a :  Optional[Any]						=  False
     with fdopen(snake_case_					,				'''w'''   ) as new_file:
      with open(snake_case_   ) as old_file:
       for line in old_file:
        new_file.write(snake_case_   )
        if line_to_copy_below in line:
         __a :  Tuple						=  True
         for line_to_copy in lines_to_copy:
          new_file.write(snake_case_   )

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

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

        __a :  str						=  []
       elif "# Replace with" in line and "##" not in line:
        __a :  Optional[int]						=  []
       elif "##" not in line:
        lines_to_copy.append(snake_case_   )

     remove(snake_case_   )

    replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py"   )
    os.rmdir(snake_case_   )
