




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase__							:	Optional[int]											=		logging.get_logger(__name__)

lowercase__							:	Dict											=		{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





	for attribute in key.split("."				):
		snake_case_						       =	getattr(_A	,      _A				)

	if weight_type is not None:
		snake_case_						       =	getattr(_A	,      _A				).shape
	else:
		snake_case_						       =	hf_pointer.shape

	assert hf_shape == value.shape, (
	    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
	else:
		snake_case_						       =	value

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


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	[]
	snake_case_						       =	fairseq_model.state_dict()

	snake_case_						       =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case_						       =	False
		if "conv_layers" in name:
			load_conv_layer(
			    _A	,      _A	,      _A	,      _A	,      hf_model.config.feat_extract_norm == "group"	,      )
			snake_case_						       =	True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case_						       =	"sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

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

	logger.warning(f"Unused weights: {unused_weights}"				)


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





	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:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    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:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    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:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    f"{full_name} has size {value.shape}, but {feature_extractor[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:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    f"{full_name} has size {value.shape}, but"
			    f" {feature_extractor[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(_A				)


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	SEWConfig()
	if is_finetuned:
		snake_case_						       =	model.wav_encoder.wav_model.cfg
	else:
		snake_case_						       =	model.cfg

	snake_case_						       =	fs_config.conv_bias
	snake_case_						       =	eval(fs_config.conv_feature_layers				)
	snake_case_						       =	[x[0] for x in conv_layers]
	snake_case_						       =	[x[1] for x in conv_layers]
	snake_case_						       =	[x[2] for x in conv_layers]
	snake_case_						       =	"gelu"
	snake_case_						       =	"layer" if fs_config.extractor_mode == "layer_norm" else "group"
	snake_case_						       =	0.0
	snake_case_						       =	fs_config.activation_fn.name
	snake_case_						       =	fs_config.encoder_embed_dim
	snake_case_						       =	0.02
	snake_case_						       =	fs_config.encoder_ffn_embed_dim
	snake_case_						       =	1E-5
	snake_case_						       =	fs_config.encoder_layerdrop
	snake_case_						       =	fs_config.encoder_attention_heads
	snake_case_						       =	fs_config.conv_pos_groups
	snake_case_						       =	fs_config.conv_pos
	snake_case_						       =	len(_A				)
	snake_case_						       =	fs_config.encoder_layers
	snake_case_						       =	fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		snake_case_						       =	model.cfg
		snake_case_						       =	fs_config.final_dropout
		snake_case_						       =	fs_config.layerdrop
	snake_case_						       =	fs_config.activation_dropout
	snake_case_						       =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	snake_case_						       =	fs_config.attention_dropout
	snake_case_						       =	fs_config.dropout_input
	snake_case_						       =	fs_config.dropout
	snake_case_						       =	fs_config.mask_channel_length
	snake_case_						       =	fs_config.mask_channel_prob
	snake_case_						       =	fs_config.mask_length
	snake_case_						       =	fs_config.mask_prob

	snake_case_						       =	"Wav2Vec2FeatureExtractor"
	snake_case_						       =	"Wav2Vec2CTCTokenizer"

	return config


@torch.no_grad()
def 		lowerCamelCase__       (						_A	,      _A	,      _A=None	,      _A=None	,      _A=True				):







	'''simple docstring'''





	if is_finetuned:
		snake_case_       , snake_case_       , snake_case_						       =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]	,      arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
	else:
		snake_case_       , snake_case_       , snake_case_						       =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

	if config_path is not None:
		snake_case_						       =	SEWConfig.from_pretrained(_A				)
	else:
		snake_case_						       =	convert_config(model[0]	,      _A				)
	snake_case_						       =	model[0].eval()

	snake_case_						       =	True if config.feat_extract_norm == "layer" else False
	snake_case_						       =	WavaVecaFeatureExtractor(
	    feature_size=1	,      sampling_rate=16000	,      padding_value=0	,      do_normalize=_A	,      return_attention_mask=_A	,      )

	if is_finetuned:
		if dict_path:
			snake_case_						       =	Dictionary.load(_A				)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			snake_case_						       =	target_dict.pad_index
			snake_case_						       =	target_dict.bos_index
			snake_case_						       =	target_dict.pad_index
			snake_case_						       =	target_dict.bos_index
			snake_case_						       =	target_dict.eos_index
			snake_case_						       =	len(target_dict.symbols				)
			snake_case_						       =	os.path.join(_A	,      "vocab.json"				)
			if not os.path.isdir(_A				):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_A				)				)
				return
			os.makedirs(_A	,      exist_ok=_A				)
			with open(_A	,      "w"	,      encoding="utf-8"				) as vocab_handle:
				json.dump(target_dict.indices	,      _A				)
			snake_case_						       =	WavaVecaCTCTokenizer(
			    _A	,      unk_token=target_dict.unk_word	,      pad_token=target_dict.pad_word	,      bos_token=target_dict.bos_word	,      eos_token=target_dict.eos_word	,      word_delimiter_token="|"	,      do_lower_case=_A	,      )
			snake_case_						       =	WavaVecaProcessor(feature_extractor=_A	,      tokenizer=_A				)
			processor.save_pretrained(_A				)

		snake_case_						       =	SEWForCTC(_A				)
	else:
		snake_case_						       =	SEWModel(_A				)
		feature_extractor.save_pretrained(_A				)

	recursively_load_weights(_A	,      _A	,      _A				)

	hf_model.save_pretrained(_A				)


if __name__ == "__main__":
		lowercase__							:	Optional[Any]											=		argparse.ArgumentParser()
		parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
		parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
		parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
		parser.add_argument(
		    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
		)
		lowercase__							:	Union[str, Any]											=		parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class 						UpperCAmelCase  :







						'''simple docstring'''





						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	UNetaDConditionModel(
							    sample_size=32			,      layers_per_block=1			,      block_out_channels=[32, 64]			,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]			,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,      in_channels=3			,      out_channels=6			,      cross_attention_dim=32			,      encoder_hid_dim=32			,      attention_head_dim=8			,      addition_embed_type="text"			,      addition_embed_type_num_heads=2			,      cross_attention_norm="group_norm"			,      resnet_time_scale_shift="scale_shift"			,      act_fn="gelu"			,      )
							unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      dynamic_thresholding_ratio=0.95			,      sample_max_value=1.0			,      prediction_type="epsilon"			,      variance_type="learned_range"			,      )

							torch.manual_seed(0		)
							snake_case_						       =	IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	UNetaDConditionModel(
							    sample_size=32			,      layers_per_block=[1, 2]			,      block_out_channels=[32, 64]			,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]			,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,      in_channels=6			,      out_channels=6			,      cross_attention_dim=32			,      encoder_hid_dim=32			,      attention_head_dim=8			,      addition_embed_type="text"			,      addition_embed_type_num_heads=2			,      cross_attention_norm="group_norm"			,      resnet_time_scale_shift="scale_shift"			,      act_fn="gelu"			,      class_embed_type="timestep"			,      mid_block_scale_factor=1.414			,      time_embedding_act_fn="gelu"			,      time_embedding_dim=32			,      )
							unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      dynamic_thresholding_ratio=0.95			,      sample_max_value=1.0			,      prediction_type="epsilon"			,      variance_type="learned_range"			,      )

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      )

							torch.manual_seed(0		)
							snake_case_						       =	IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							snake_case_						       =	inputs["prompt"]
							snake_case_						       =	inputs["generator"]
							snake_case_						       =	inputs["num_inference_steps"]
							snake_case_						       =	inputs["output_type"]

							if "image" in inputs:
								snake_case_						       =	inputs["image"]
							else:
								snake_case_						       =	None

							if "mask_image" in inputs:
								snake_case_						       =	inputs["mask_image"]
							else:
								snake_case_						       =	None

							if "original_image" in inputs:
								snake_case_						       =	inputs["original_image"]
							else:
								snake_case_						       =	None

							snake_case_       , snake_case_						       =	pipe.encode_prompt(__lowercase		)

							# inputs with prompt converted to embeddings
							snake_case_						       =	{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								snake_case_						       =	image

							if mask_image is not None:
								snake_case_						       =	mask_image

							if original_image is not None:
								snake_case_						       =	original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
								setattr(__lowercase			,      __lowercase			,      __lowercase		)

							snake_case_						       =	pipe(**__lowercase		)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(__lowercase		)
								snake_case_						       =	self.pipeline_class.from_pretrained(__lowercase		)
								pipe_loaded.to(__lowercase		)
								pipe_loaded.set_progress_bar_config(disable=__lowercase		)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							for optional_component in pipe._optional_components:
								self.assertTrue(
								    getattr(__lowercase			,      __lowercase		) is None			,      f"`{optional_component}` did not stay set to None after loading."			,      )

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							snake_case_						       =	inputs["generator"]
							snake_case_						       =	inputs["num_inference_steps"]
							snake_case_						       =	inputs["output_type"]

							# inputs with prompt converted to embeddings
							snake_case_						       =	{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								snake_case_						       =	image

							if mask_image is not None:
								snake_case_						       =	mask_image

							if original_image is not None:
								snake_case_						       =	original_image

							snake_case_						       =	pipe_loaded(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe(**__lowercase		)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(__lowercase		)
								snake_case_						       =	self.pipeline_class.from_pretrained(__lowercase		)
								pipe_loaded.to(__lowercase		)
								pipe_loaded.set_progress_bar_config(disable=__lowercase		)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe_loaded(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)
