






import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowercase	:				Optional[int]      	=     logging.get_logger('''transformers.models.speecht5''')

lowercase	:				Optional[Any]      	=     {
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
lowercase	:				List[Any]      	=     {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
lowercase	:				List[str]      	=     {
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
lowercase	:				str      	=     {
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
lowercase	:				str      	=     {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
lowercase	:				List[Any]      	=     {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
lowercase	:				Optional[Any]      	=     {
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
lowercase	:				int      	=     {
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
lowercase	:				Optional[Any]      	=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowercase	:				Tuple      	=     {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase	:				str      	=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase	:				List[Any]      	=     []
lowercase	:				Union[str, Any]      	=     [
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
lowercase	:				List[str]      	=     IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
lowercase	:				Tuple      	=     IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
lowercase	:				Any      	=     IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]

def   lowerCAmelCase__						(     _a							:      Optional[int]	,   _a							:      Union[str, Any]	,   _a							:      Tuple	,   _a							:      Dict	,   _a							:      Tuple     ):




		for attribute in key.split("."     ):
				snake_case_					:				Optional[int]    			=							getattr(_UpperCamelCase	,   _UpperCamelCase     )

		if weight_type is not None:
				snake_case_					:				int    			=							getattr(_UpperCamelCase	,   _UpperCamelCase     ).shape
		else:
				snake_case_					:				List[Any]    			=							hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
				    F''' {value.shape} for {full_name}'''     )

		if weight_type == "weight":
				snake_case_					:				Optional[int]    			=							value
		elif weight_type == "weight_g":
				snake_case_					:				str    			=							value
		elif weight_type == "weight_v":
				snake_case_					:				str    			=							value
		elif weight_type == "bias":
				snake_case_					:				List[str]    			=							value
		elif weight_type == "running_mean":
				snake_case_					:				Optional[int]    			=							value
		elif weight_type == "running_var":
				snake_case_					:				List[str]    			=							value
		elif weight_type == "num_batches_tracked":
				snake_case_					:				str    			=							value
		else:
				snake_case_					:				List[Any]    			=							value

		logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''     )

def   lowerCAmelCase__						(     _a							:      Optional[Any]	,   _a							:      str     ):




		for key in ignore_keys:
				if key.endswith(".*"     ):
						if name.startswith(key[:-1]     ):
								return True
				elif ".*." in key:
						snake_case_			,							snake_case_					:				str    			=							key.split(".*."     )
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False

def   lowerCAmelCase__						(     _a							:      int	,   _a							:      Optional[Any]	,   _a							:      str     ):
		snake_case_					:				Dict    			=							[]

		if task == "s2t":
				snake_case_					:				str    			=							hf_model.speechta.encoder.prenet.feature_encoder
				snake_case_					:				str    			=							MAPPING_S2T
				snake_case_					:				Union[str, Any]    			=							IGNORE_KEYS_S2T
		elif task == "t2s":
				snake_case_					:				List[str]    			=							None
				snake_case_					:				int    			=							MAPPING_T2S
				snake_case_					:				List[str]    			=							IGNORE_KEYS_T2S
		elif task == "s2s":
				snake_case_					:				List[str]    			=							hf_model.speechta.encoder.prenet.feature_encoder
				snake_case_					:				Union[str, Any]    			=							MAPPING_S2S
				snake_case_					:				Any    			=							IGNORE_KEYS_S2S
		else:
				raise ValueError(F'''Unsupported task: {task}'''     )

		for name, value in fairseq_dict.items():
				if should_ignore(_UpperCamelCase	,   _UpperCamelCase     ):
						logger.info(F'''{name} was ignored'''     )
						continue

				snake_case_					:				List[str]    			=							False
				if "conv_layers" in name:
						load_conv_layer(
						    _UpperCamelCase	,   _UpperCamelCase	,   _UpperCamelCase	,   _UpperCamelCase	,   hf_model.config.feat_extract_norm == "group"	,   )
						snake_case_					:				Union[str, Any]    			=							True
				else:
						for key, mapped_key in MAPPING.items():
								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

								if "*" in key:
										snake_case_			,							snake_case_					:				str    			=							key.split(".*."     )
										if prefix in name and suffix in name:
												snake_case_					:				int    			=							suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
								if key in name:
										snake_case_					:				Tuple    			=							True
										if "*" in mapped_key:
												snake_case_					:				Optional[int]    			=							name.split(_UpperCamelCase     )[0].split("."     )[-2]
												snake_case_					:				List[Any]    			=							mapped_key.replace("*"	,   _UpperCamelCase     )
										if "weight_g" in name:
												snake_case_					:				Optional[int]    			=							"weight_g"
										elif "weight_v" in name:
												snake_case_					:				Any    			=							"weight_v"
										elif "bias" in name:
												snake_case_					:				Optional[int]    			=							"bias"
										elif "weight" in name:
												snake_case_					:				List[str]    			=							"weight"
										elif "running_mean" in name:
												snake_case_					:				Union[str, Any]    			=							"running_mean"
										elif "running_var" in name:
												snake_case_					:				List[str]    			=							"running_var"
										elif "num_batches_tracked" in name:
												snake_case_					:				Optional[Any]    			=							"num_batches_tracked"
										else:
												snake_case_					:				Optional[Any]    			=							None
										set_recursively(_UpperCamelCase	,   _UpperCamelCase	,   _UpperCamelCase	,   _UpperCamelCase	,   _UpperCamelCase     )
								continue
				if not is_used:
						unused_weights.append(_UpperCamelCase     )

		logger.warning(F'''Unused weights: {unused_weights}'''     )

def   lowerCAmelCase__						(     _a							:      List[str]	,   _a							:      int	,   _a							:      Union[str, Any]	,   _a							:      int	,   _a							:      str     ):
		snake_case_					:				List[str]    			=							full_name.split("conv_layers."     )[-1]
		snake_case_					:				int    			=							name.split("."     )
		snake_case_					:				Any    			=							int(items[0]     )
		snake_case_					:				Dict    			=							int(items[1]     )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''     )
						snake_case_					:				Tuple    			=							value
						logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''     )
						snake_case_					:				Union[str, Any]    			=							value
						logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''     )
						snake_case_					:				Tuple    			=							value
						logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''     )
						snake_case_					:				str    			=							value
						logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		else:
				unused_weights.append(_UpperCamelCase     )

@torch.no_grad()
def   lowerCAmelCase__						(     _a							:      Optional[int]	,   _a							:      int	,   _a							:      Optional[int]	,   _a							:      Optional[Any]=None	,   _a							:      List[str]=None	,   _a							:      int=None	,   ):




		if config_path is not None:
				snake_case_					:				List[str]    			=							SpeechTaConfig.from_pretrained(_UpperCamelCase     )
		else:
				snake_case_					:				List[Any]    			=							SpeechTaConfig()

		if task == "s2t":
				snake_case_					:				Any    			=							config.max_text_positions
				snake_case_					:				List[str]    			=							SpeechTaForSpeechToText(_UpperCamelCase     )
		elif task == "t2s":
				snake_case_					:				Optional[Any]    			=							18_76
				snake_case_					:				Optional[int]    			=							6_00
				snake_case_					:				List[Any]    			=							config.max_speech_positions
				snake_case_					:				Union[str, Any]    			=							SpeechTaForTextToSpeech(_UpperCamelCase     )
		elif task == "s2s":
				snake_case_					:				Optional[int]    			=							18_76
				snake_case_					:				Dict    			=							config.max_speech_positions
				snake_case_					:				Tuple    			=							SpeechTaForSpeechToSpeech(_UpperCamelCase     )
		else:
				raise ValueError(F'''Unknown task name: {task}'''     )

		if vocab_path:
				snake_case_					:				List[Any]    			=							SpeechTaTokenizer(_UpperCamelCase	,   model_max_length=config.max_text_positions     )

				# Mask token behaves like a normal word, i.e. include the space before it
				snake_case_					:				List[Any]    			=							AddedToken("<mask>"	,   lstrip=_UpperCamelCase	,   rstrip=_UpperCamelCase     )
				snake_case_					:				Any    			=							mask_token
				tokenizer.add_special_tokens({"mask_token": mask_token}     )
				tokenizer.add_tokens(["<ctc_blank>"]     )

		snake_case_					:				int    			=							SpeechTaFeatureExtractor()
		snake_case_					:				Union[str, Any]    			=							SpeechTaProcessor(tokenizer=_UpperCamelCase	,   feature_extractor=_UpperCamelCase     )
		processor.save_pretrained(_UpperCamelCase     )

		snake_case_					:				Dict    			=							torch.load(_UpperCamelCase     )
		recursively_load_weights(fairseq_checkpoint["model"]	,   _UpperCamelCase	,   _UpperCamelCase     )

		model.save_pretrained(_UpperCamelCase     )

		if repo_id:
				print("Pushing to the hub..."     )
				processor.push_to_hub(_UpperCamelCase     )
				model.push_to_hub(_UpperCamelCase     )


if __name__ == "__main__":
							lowercase	:				Any      	=     argparse.ArgumentParser()
							parser.add_argument(
							    '''--task''',
							    default='''s2t''',
							    type=str,
							    help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
							)
							parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
							)

							lowercase	:				Optional[int]      	=     parser.parse_args()
							convert_speechta_checkpoint(
							    args.task,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.vocab_path,
							    args.push_to_hub,
							)




"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
@slow
@require_torch_gpu
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]      )

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-1



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_dpmpp_2m"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe(
										    [prompt]       ,  generator=__a       ,  guidance_scale=7.5       ,  num_inference_steps=15       ,  output_type="np"       ,  use_karras_sigmas=__a       ,  )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array(
										    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]      )

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




