

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()
lowerCAmelCase_       	=	logging.get_logger('''transformers.models.speecht5''')

lowerCAmelCase_       	=	{
    '''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''',
}
lowerCAmelCase_       	=	{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
lowerCAmelCase_       	=	{
    '''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''',
}
lowerCAmelCase_       	=	{
    '''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''',
}
lowerCAmelCase_       	=	{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
lowerCAmelCase_       	=	{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
lowerCAmelCase_       	=	{
    '''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''',
}
lowerCAmelCase_       	=	{
    '''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''',
}
lowerCAmelCase_       	=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase_       	=	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase_       	=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase_       	=	[]
lowerCAmelCase_       	=	[
    '''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''',
]
lowerCAmelCase_       	=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
lowerCAmelCase_       	=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
lowerCAmelCase_       	=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						for attribute in key.split('''.'''       ):
												snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

						if weight_type is not None:
												snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ).shape
						else:
												snake_case_   =	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_   =	value
						elif weight_type == "weight_g":
												snake_case_   =	value
						elif weight_type == "weight_v":
												snake_case_   =	value
						elif weight_type == "bias":
												snake_case_   =	value
						elif weight_type == "running_mean":
												snake_case_   =	value
						elif weight_type == "running_var":
												snake_case_   =	value
						elif weight_type == "num_batches_tracked":
												snake_case_   =	value
						else:
												snake_case_   =	value

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


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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[]

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

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

												snake_case_   =	False
												if "conv_layers" in name:
																		load_conv_layer(
																		    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		hf_model.config.feat_extract_norm == '''group'''  ,		)
																		snake_case_   =	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_   =	key.split('''.*.'''       )
																														if prefix in name and suffix in name:
																																				snake_case_   =	suffix

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

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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	full_name.split('''conv_layers.'''       )[-1]
						snake_case_   =	name.split('''.'''       )
						snake_case_   =	int(items[0]       )
						snake_case_   =	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_   =	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_   =	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_   =	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_   =	value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
						else:
												unused_weights.append(SCREAMING_SNAKE_CASE__       )


@torch.no_grad()
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=None  ,		):
						if config_path is not None:
												snake_case_   =	SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
						else:
												snake_case_   =	SpeechTaConfig()

						if task == "s2t":
												snake_case_   =	config.max_text_positions
												snake_case_   =	SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE__       )
						elif task == "t2s":
												snake_case_   =	1876
												snake_case_   =	600
												snake_case_   =	config.max_speech_positions
												snake_case_   =	SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE__       )
						elif task == "s2s":
												snake_case_   =	1876
												snake_case_   =	config.max_speech_positions
												snake_case_   =	SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE__       )
						else:
												raise ValueError(F'''Unknown task name: {task}'''       )

						if vocab_path:
												snake_case_   =	SpeechTaTokenizer(SCREAMING_SNAKE_CASE__  ,		model_max_length=config.max_text_positions       )

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

						snake_case_   =	SpeechTaFeatureExtractor()
						snake_case_   =	SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__  ,		feature_extractor=SCREAMING_SNAKE_CASE__       )
						processor.save_pretrained(SCREAMING_SNAKE_CASE__       )

						snake_case_   =	torch.load(SCREAMING_SNAKE_CASE__       )
						recursively_load_weights(fairseq_checkpoint['''model''']  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

						model.save_pretrained(SCREAMING_SNAKE_CASE__       )

						if repo_id:
												print('''Pushing to the hub...'''       )
												processor.push_to_hub(SCREAMING_SNAKE_CASE__       )
												model.push_to_hub(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
					lowerCAmelCase_       	=	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.'''
					)

					lowerCAmelCase_       	=	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,
					)


import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







@slow
@require_flax
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def     a							(					self       ):
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'       )
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

											snake_case_							=    'xvjiarui/stable-diffusion-2-inpainting'
											snake_case_				,    snake_case_							=    FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case      ,   safety_checker=snake_case       )

											snake_case_							=    'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_							=    jax.random.PRNGKey(0       )
											snake_case_							=    50

											snake_case_							=    jax.device_count()
											snake_case_							=    num_samples * [prompt]
											snake_case_							=    num_samples * [init_image]
											snake_case_							=    num_samples * [mask_image]
											snake_case_				,    snake_case_				,    snake_case_							=    pipeline.prepare_inputs(snake_case      ,   snake_case      ,   snake_case       )

											# shard inputs and rng
											snake_case_							=    replicate(snake_case       )
											snake_case_							=    jax.random.split(snake_case      ,   jax.device_count()       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )

											snake_case_							=    pipeline(
											    snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case       )

											snake_case_							=    output.images.reshape(snake_case      ,   512      ,   512      ,   3       )

											snake_case_							=    images[0, 253:256, 253:256, -1]

											snake_case_							=    jnp.asarray(jax.device_get(image_slice.flatten()       )       )
											snake_case_							=    jnp.array(
											    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]       )
											print(F'''output_slice: {output_slice}'''       )
											assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2




