






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__snake_case      :      Tuple             =       [
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

__snake_case      :      List[str]             =       [
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

__snake_case      :      int             =       []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
					# loop over downblocks/upblocks

					for j in range(2):
										# loop over resnets/attentions for downblocks
										__snake_case      :      Optional[int]             =       f'down_blocks.{i}.resnets.{j}.'
										__snake_case      :      Union[str, Any]             =       f'input_blocks.{3*i + j + 1}.0.'
										unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

										if i < 3:
															# no attention layers in down_blocks.3
															__snake_case      :      List[str]             =       f'down_blocks.{i}.attentions.{j}.'
															__snake_case      :      Dict             =       f'input_blocks.{3*i + j + 1}.1.'
															unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

					for j in range(3):
										# loop over resnets/attentions for upblocks
										__snake_case      :      Dict             =       f'up_blocks.{i}.resnets.{j}.'
										__snake_case      :      Union[str, Any]             =       f'output_blocks.{3*i + j}.0.'
										unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

										if i > 0:
															# no attention layers in up_blocks.0
															__snake_case      :      str             =       f'up_blocks.{i}.attentions.{j}.'
															__snake_case      :      str             =       f'output_blocks.{3*i + j}.1.'
															unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

					if i < 3:
										# no downsample in down_blocks.3
										__snake_case      :      str             =       f'down_blocks.{i}.downsamplers.0.conv.'
										__snake_case      :      str             =       f'input_blocks.{3*(i+1)}.0.op.'
										unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

										# no upsample in up_blocks.3
										__snake_case      :      int             =       f'up_blocks.{i}.upsamplers.0.'
										__snake_case      :      Optional[Any]             =       f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
										unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__snake_case      :      List[str]             =       """mid_block.attentions.0."""
__snake_case      :      Union[str, Any]             =       """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
					__snake_case      :      List[Any]             =       f'mid_block.resnets.{j}.'
					__snake_case      :      Optional[Any]             =       f'middle_block.{2*j}.'
					unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def 		_UpperCamelCase				(      lowerCAmelCase    :   Dict						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  {k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												lowerCAmelCase__        =  sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								lowerCAmelCase__        =  v.replace(lowerCAmelCase      ,    lowerCAmelCase						)
																		lowerCAmelCase__        =  v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		lowerCAmelCase__        =  v.replace(lowerCAmelCase      ,    lowerCAmelCase						)
												lowerCAmelCase__        =  v
						lowerCAmelCase__        =  {v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


# ================#
# VAE Conversion #
# ================#

__snake_case      :      Union[str, Any]             =       [
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
					# down_blocks have two resnets
					for j in range(2):
										__snake_case      :      Any             =       f'encoder.down_blocks.{i}.resnets.{j}.'
										__snake_case      :      Union[str, Any]             =       f'encoder.down.{i}.block.{j}.'
										vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

					if i < 3:
										__snake_case      :      Optional[int]             =       f'down_blocks.{i}.downsamplers.0.'
										__snake_case      :      Optional[int]             =       f'down.{i}.downsample.'
										vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

										__snake_case      :      str             =       f'up_blocks.{i}.upsamplers.0.'
										__snake_case      :      Any             =       f'up.{3-i}.upsample.'
										vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

					# up_blocks have three resnets
					# also, up blocks in hf are numbered in reverse from sd
					for j in range(3):
										__snake_case      :      int             =       f'decoder.up_blocks.{i}.resnets.{j}.'
										__snake_case      :      List[Any]             =       f'decoder.up.{3-i}.block.{j}.'
										vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
					__snake_case      :      List[str]             =       f'mid_block.resnets.{i}.'
					__snake_case      :      Optional[Any]             =       f'mid.block_{i+1}.'
					vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__snake_case      :      Tuple             =       [
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]







def 		_UpperCamelCase				(      lowerCAmelCase    :   int						)					->				Union[str, Any]:

						"""simple docstring"""



						return w.reshape(*w.shape      ,    1      ,    1						)







def 		_UpperCamelCase				(      lowerCAmelCase    :   Optional[Any]						)					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  {k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		lowerCAmelCase__        =  v.replace(lowerCAmelCase      ,    lowerCAmelCase						)
												lowerCAmelCase__        =  v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								lowerCAmelCase__        =  v.replace(lowerCAmelCase      ,    lowerCAmelCase						)
																		lowerCAmelCase__        =  v
						lowerCAmelCase__        =  {v: vae_state_dict[k] for k, v in mapping.items()}
						lowerCAmelCase__        =  ['q', 'k', 'v', 'proj_out']
						for k, v in new_state_dict.items():
												for weight_name in weights_to_convert:
																		if F"mid.attn_1.{weight_name}.weight" in k:
																								print(F"Reshaping {k} for SD format"						)
																								lowerCAmelCase__        =  reshape_weight_for_sd(lowerCAmelCase						)
						return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__snake_case      :      Optional[int]             =       [
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
__snake_case      :      str             =       {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__snake_case      :      int             =       re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__snake_case      :      Optional[int]             =       {"""q""": 0, """k""": 1, """v""": 2}







def 		_UpperCamelCase				(      lowerCAmelCase    :   Optional[Any]						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  {}
						lowerCAmelCase__        =  {}
						lowerCAmelCase__        =  {}
						for k, v in text_enc_dict.items():
												if (
												    k.endswith('.self_attn.q_proj.weight'						)
												    or k.endswith('.self_attn.k_proj.weight'						)
												    or k.endswith('.self_attn.v_proj.weight'						)
												):
																		lowerCAmelCase__        =  k[: -len('.q_proj.weight'						)]
																		lowerCAmelCase__        =  k[-len('q_proj.weight'						)]
																		if k_pre not in capture_qkv_weight:
																								lowerCAmelCase__        =  [None, None, None]
																		lowerCAmelCase__        =  v
																		continue

												if (
												    k.endswith('.self_attn.q_proj.bias'						)
												    or k.endswith('.self_attn.k_proj.bias'						)
												    or k.endswith('.self_attn.v_proj.bias'						)
												):
																		lowerCAmelCase__        =  k[: -len('.q_proj.bias'						)]
																		lowerCAmelCase__        =  k[-len('q_proj.bias'						)]
																		if k_pre not in capture_qkv_bias:
																								lowerCAmelCase__        =  [None, None, None]
																		lowerCAmelCase__        =  v
																		continue

												lowerCAmelCase__        =  textenc_pattern.sub(lambda lowerCAmelCase						: protected[re.escape(m.group(0						)						)]      ,    lowerCAmelCase						)
												lowerCAmelCase__        =  v

						for k_pre, tensors in capture_qkv_weight.items():
												if None in tensors:
																		raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'						)
												lowerCAmelCase__        =  textenc_pattern.sub(lambda lowerCAmelCase						: protected[re.escape(m.group(0						)						)]      ,    lowerCAmelCase						)
												lowerCAmelCase__        =  torch.cat(lowerCAmelCase						)

						for k_pre, tensors in capture_qkv_bias.items():
												if None in tensors:
																		raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'						)
												lowerCAmelCase__        =  textenc_pattern.sub(lambda lowerCAmelCase						: protected[re.escape(m.group(0						)						)]      ,    lowerCAmelCase						)
												lowerCAmelCase__        =  torch.cat(lowerCAmelCase						)

						return new_state_dict







def 		_UpperCamelCase				(      lowerCAmelCase    :   Optional[int]						)					->				str:

						"""simple docstring"""



						return text_enc_dict


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

					parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
					parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
					parser.add_argument(
					    """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
					)

					__snake_case      :      List[str]             =       parser.parse_args()

					assert args.model_path is not None, "Must provide a model path!"

					assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

					# Path for safetensors
					__snake_case      :      int             =       osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
					__snake_case      :      Union[str, Any]             =       osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
					__snake_case      :      Union[str, Any]             =       osp.join(args.model_path, """text_encoder""", """model.safetensors""")

					# Load models from safetensors if it exists, if it doesn't pytorch
					if osp.exists(unet_path):
										__snake_case      :      Dict             =       load_file(unet_path, device="""cpu""")
					else:
										__snake_case      :      Dict             =       osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
										__snake_case      :      int             =       torch.load(unet_path, map_location="""cpu""")

					if osp.exists(vae_path):
										__snake_case      :      Dict             =       load_file(vae_path, device="""cpu""")
					else:
										__snake_case      :      int             =       osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
										__snake_case      :      Tuple             =       torch.load(vae_path, map_location="""cpu""")

					if osp.exists(text_enc_path):
										__snake_case      :      Optional[int]             =       load_file(text_enc_path, device="""cpu""")
					else:
										__snake_case      :      int             =       osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
										__snake_case      :      Union[str, Any]             =       torch.load(text_enc_path, map_location="""cpu""")

					# Convert the UNet model
					__snake_case      :      List[Any]             =       convert_unet_state_dict(unet_state_dict)
					__snake_case      :      int             =       {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

					# Convert the VAE model
					__snake_case      :      List[Any]             =       convert_vae_state_dict(vae_state_dict)
					__snake_case      :      int             =       {"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

					# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
					__snake_case      :      List[Any]             =       """text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

					if is_vaa_model:
										# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
										__snake_case      :      Tuple             =       {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
										__snake_case      :      Optional[int]             =       convert_text_enc_state_dict_vaa(text_enc_dict)
										__snake_case      :      Dict             =       {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
					else:
										__snake_case      :      List[str]             =       convert_text_enc_state_dict(text_enc_dict)
										__snake_case      :      Any             =       {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

					# Put together new checkpoint
					__snake_case      :      Optional[Any]             =       {**unet_state_dict, **vae_state_dict, **text_enc_dict}
					if args.half:
										__snake_case      :      Optional[int]             =       {k: v.half() for k, v in state_dict.items()}

					if args.use_safetensors:
										save_file(state_dict, args.checkpoint_path)
					else:
										__snake_case      :      Optional[int]             =       {"""state_dict""": state_dict}
										torch.save(state_dict, args.checkpoint_path)













# 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()





