






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__snake_case      :      List[Any]             =       False
__snake_case      :      int             =       True
__snake_case      :      Dict             =       False


if __name__ == "__main__":
					__snake_case      :      Any             =       argparse.ArgumentParser()

					parser.add_argument(
					    """--repo_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the architecture.""",
					)

					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

					__snake_case      :      str             =       parser.parse_args()

					__snake_case      :      Union[str, Any]             =       {
					    """image_size""": """sample_size""",
					    """num_res_blocks""": """layers_per_block""",
					    """block_channels""": """block_out_channels""",
					    """down_blocks""": """down_block_types""",
					    """up_blocks""": """up_block_types""",
					    """downscale_freq_shift""": """freq_shift""",
					    """resnet_num_groups""": """norm_num_groups""",
					    """resnet_act_fn""": """act_fn""",
					    """resnet_eps""": """norm_eps""",
					    """num_head_channels""": """attention_head_dim""",
					}

					__snake_case      :      Optional[Any]             =       {
					    """time_steps""": """time_proj""",
					    """mid""": """mid_block""",
					    """downsample_blocks""": """down_blocks""",
					    """upsample_blocks""": """up_blocks""",
					}

					__snake_case      :      List[str]             =       """""" if has_file(args.repo_path, """config.json""") else """unet"""

					with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
										__snake_case      :      Any             =       reader.read()
										__snake_case      :      Optional[Any]             =       json.loads(text)

					if do_only_config:
										for key in config_parameters_to_change.keys():
															config.pop(key, None)

					if has_file(args.repo_path, """config.json"""):
										__snake_case      :      Optional[int]             =       UNetaDModel(**config)
					else:
										__snake_case      :      Union[str, Any]             =       UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
										__snake_case      :      Tuple             =       class_name(**config)

					if do_only_config:
										model.save_config(os.path.join(args.repo_path, subfolder))

					__snake_case      :      Optional[Any]             =       dict(model.config)

					if do_only_renaming:
										for key, value in config_parameters_to_change.items():
															if key in config:
																				__snake_case      :      str             =       config[key]
																				del config[key]

										__snake_case      :      Dict             =       [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
										__snake_case      :      Any             =       [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

					if do_only_weights:
										__snake_case      :      int             =       torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

										__snake_case      :      int             =       {}
										for param_key, param_value in state_dict.items():
															if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
																				continue
															__snake_case      :      Any             =       False
															for key, new_key in key_parameters_to_change.items():
																				if not has_changed and param_key.split(""".""")[0] == key:
																									__snake_case      :      Optional[Any]             =       param_value
																									__snake_case      :      Any             =       True
															if not has_changed:
																				__snake_case      :      List[str]             =       param_value

										model.load_state_dict(new_state_dict)
										model.save_pretrained(os.path.join(args.repo_path, subfolder))













# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   '''microsoft/speecht5_tts'''
						_SCREAMING_SNAKE_CASE		:    Any						=   (
						    '''This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '''
						    '''text to read (in English) and returns a waveform object containing the sound.'''
						)
						_SCREAMING_SNAKE_CASE		:    int						=   '''text_reader'''
						_SCREAMING_SNAKE_CASE		:    List[str]						=   SpeechTaProcessor
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   SpeechTaForTextToSpeech
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   SpeechTaHifiGan

						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   ['''text''']
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''audio''']






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if self.post_processor is None:
																		lowerCAmelCase__        =  'microsoft/speecht5_hifigan'
												super().setup()






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=None				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.pre_processor(text=_UpperCamelCase       ,  return_tensors='pt'       ,  truncation=_UpperCamelCase				)

												if speaker_embeddings is None:
																		if not is_datasets_available():
																								raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'				)

																		lowerCAmelCase__        =  load_dataset('Matthijs/cmu-arctic-xvectors'       ,  split='validation'				)
																		lowerCAmelCase__        =  torch.tensor(embeddings_dataset[73_05]['xvector']				).unsqueeze(0				)

												return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.model.generate_speech(**_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.post_processor(_UpperCamelCase				).cpu().detach()





