"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_lowercase			:			Any										=						logging.get_logger(__name__)

_lowercase			:			Optional[Any]										=						{
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_lowercase			:			Union[str, Any]										=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   str							,		__lowerCamelCase    :   List[Any]     ):
			"""simple docstring"""
			for attribute in key.split('''.'''     ):
						lowerCamelCase__		:		int		     =getattr(__lowerCamelCase							,		__lowerCamelCase     )

			if weight_type is not None:
						lowerCamelCase__		:		Union[str, Any]		     =getattr(__lowerCamelCase							,		__lowerCamelCase     ).shape
			else:
						lowerCamelCase__		:		str		     =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":
						lowerCamelCase__		:		Optional[int]		     =value
			elif weight_type == "weight_g":
						lowerCamelCase__		:		Optional[int]		     =value
			elif weight_type == "weight_v":
						lowerCamelCase__		:		Optional[Any]		     =value
			elif weight_type == "bias":
						lowerCamelCase__		:		Dict		     =value
			else:
						lowerCamelCase__		:		Optional[int]		     =value

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






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Any     ):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =[]
			lowerCamelCase__		:		List[str]		     =fairseq_model.state_dict()

			lowerCamelCase__		:		Optional[int]		     =hf_model.feature_extractor

			# if encoder has different dim to decoder -> use proj_weight
			lowerCamelCase__		:		Dict		     =None

			for name, value in fairseq_dict.items():
						lowerCamelCase__		:		List[str]		     =False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		hf_model.config.feat_extract_norm == '''group'''							,		)
									lowerCamelCase__		:		List[str]		     =True
						elif name.split('''.'''     )[0] == "proj":
									lowerCamelCase__		:		List[Any]		     =fairseq_model.proj
									lowerCamelCase__		:		Any		     =True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0]:
															lowerCamelCase__		:		Optional[int]		     =True
															if "*" in mapped_key:
																		lowerCamelCase__		:		Any		     =name.split(__lowerCamelCase     )[0].split('''.'''     )[-2]
																		lowerCamelCase__		:		Any		     =mapped_key.replace('''*'''							,		__lowerCamelCase     )
															if "weight_g" in name:
																		lowerCamelCase__		:		Dict		     ='''weight_g'''
															elif "weight_v" in name:
																		lowerCamelCase__		:		Union[str, Any]		     ='''weight_v'''
															elif "bias" in name:
																		lowerCamelCase__		:		List[str]		     ='''bias'''
															elif "weight" in name:
																		lowerCamelCase__		:		Any		     ='''weight'''
															else:
																		lowerCamelCase__		:		Dict		     =None
															set_recursively(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase     )
												continue
						if not is_used:
									unused_weights.append(__lowerCamelCase     )

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

			return proj_weight






def 					snake_case__       (		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =full_name.split('''conv_layers.'''     )[-1]
			lowerCamelCase__		:		Union[str, Any]		     =name.split('''.'''     )
			lowerCamelCase__		:		Tuple		     =int(items[0]     )
			lowerCamelCase__		:		int		     =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.'''
									)
									lowerCamelCase__		:		Optional[int]		     =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.'''
									)
									lowerCamelCase__		:		Tuple		     =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."
									)
									lowerCamelCase__		:		List[Any]		     =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.'''
									)
									lowerCamelCase__		:		Tuple		     =value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
			else:
						unused_weights.append(__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		str		     =emb.weight.shape
			lowerCamelCase__		:		str		     =nn.Linear(__lowerCamelCase							,		__lowerCamelCase							,		bias=__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =emb.weight.data
			return lin_layer






def 					snake_case__       (		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''r'''							,		encoding='''utf-8'''     ) as f:
						lowerCamelCase__		:		Optional[int]		     =f.readlines()
						lowerCamelCase__		:		Optional[Any]		     =[line.split(''' '''     )[0] for line in lines]

			lowerCamelCase__		:		Optional[Any]		     =len(__lowerCamelCase     )

			lowerCamelCase__		:		Tuple		     ={
			    '''<s>''': 0,
			    '''<pad>''': 1,
			    '''</s>''': 2,
			    '''<unk>''': 3,
			}

			vocab_dict.update(dict(zip(__lowerCamelCase							,		range(4							,		num_words + 4     )     )     )     )
			return vocab_dict






@torch.no_grad()
def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   int							,		):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =WavaVecaConfig.from_pretrained(__lowerCamelCase     )
			lowerCamelCase__		:		List[Any]		     =SpeechaTextaConfig.from_pretrained(
			    __lowerCamelCase							,		vocab_size=__lowerCamelCase							,		decoder_layers=__lowerCamelCase							,		do_stable_layer_norm=__lowerCamelCase     )

			lowerCamelCase__		:		Any		     =WavaVecaFeatureExtractor(
			    feature_size=1							,		sampling_rate=16000							,		padding_value=0							,		do_normalize=__lowerCamelCase							,		return_attention_mask=__lowerCamelCase							,		)

			lowerCamelCase__		:		List[str]		     =fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]							,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
			lowerCamelCase__		:		Any		     =model[0].eval()

			# set weights for wav2vec2 encoder
			lowerCamelCase__		:		Tuple		     =WavaVecaModel(__lowerCamelCase     )
			lowerCamelCase__		:		Optional[Any]		     =recursively_load_weights_wavaveca(model.encoder							,		__lowerCamelCase     )

			lowerCamelCase__		:		Dict		     =SpeechaTextaForCausalLM(__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,		strict=__lowerCamelCase     )

			# set output linear layer
			unexpected_keys.remove('''embed_out'''     )
			lowerCamelCase__		:		Optional[int]		     =nn.Parameter(model.decoder.embed_out.detach()     )

			# layer norm is init to identity matrix so leaving it is fine
			logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''     )
			logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''     )

			lowerCamelCase__		:		Optional[Any]		     =SpeechEncoderDecoderModel(encoder=__lowerCamelCase							,		decoder=__lowerCamelCase     )
			lowerCamelCase__		:		List[Any]		     =False

			# add projection layer
			lowerCamelCase__		:		Optional[Any]		     =nn.Parameter(projection_layer.weight     )
			lowerCamelCase__		:		List[Any]		     =nn.Parameter(projection_layer.bias     )

			lowerCamelCase__		:		Union[str, Any]		     =create_vocab_dict(__lowerCamelCase     )

			with open(os.path.join(__lowerCamelCase							,		'''vocab.json'''     )							,		'''w'''     ) as fp:
						json.dump(__lowerCamelCase							,		__lowerCamelCase     )

			lowerCamelCase__		:		Optional[int]		     =SpeechaTextaTokenizer(os.path.join(__lowerCamelCase							,		'''vocab.json'''     )     )
			tokenizer.save_pretrained(__lowerCamelCase     )

			lowerCamelCase__		:		Union[str, Any]		     =hf_wavavec.config.to_dict()
			lowerCamelCase__		:		int		     =tokenizer.pad_token_id
			lowerCamelCase__		:		Union[str, Any]		     =tokenizer.bos_token_id
			lowerCamelCase__		:		Union[str, Any]		     =tokenizer.eos_token_id
			lowerCamelCase__		:		Optional[Any]		     ='''speech_to_text_2'''
			lowerCamelCase__		:		Optional[int]		     ='''wav2vec2'''

			lowerCamelCase__		:		Any		     =SpeechEncoderDecoderConfig.from_dict(__lowerCamelCase     )

			hf_wavavec.save_pretrained(__lowerCamelCase     )
			feature_extractor.save_pretrained(__lowerCamelCase     )


if __name__ == "__main__":
		_lowercase			:			Dict										=						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(
		    "--encoder_config_path",
		    default="facebook/wav2vec2-large-lv60",
		    type=str,
		    help="Path to hf encoder wav2vec2 checkpoint config",
		)
		parser.add_argument(
		    "--decoder_config_path",
		    default="facebook/s2t-small-mustc-en-fr-st",
		    type=str,
		    help="Path to hf decoder s2t checkpoint config",
		)
		parser.add_argument("--vocab_size", default=1_0_2_2_4, type=int, help="Vocab size of decoder")
		parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

		_lowercase			:			List[str]										=						parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.dict_path,
		    encoder_config_path=args.encoder_config_path,
		    decoder_config_path=args.decoder_config_path,
		    vocab_size=args.vocab_size,
		    num_decoder_layers=args.num_decoder_layers,
		)









"""simple docstring"""






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[int]		     =controlnet_params

								lowerCamelCase__		:		Dict		     ='''bird'''
								lowerCamelCase__		:		List[str]		     =jax.device_count()
								lowerCamelCase__		:		Optional[Any]		     =pipe.prepare_text_inputs([prompts] * num_samples					)

								lowerCamelCase__		:		Dict		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''					)
								lowerCamelCase__		:		List[Any]		     =pipe.prepare_image_inputs([canny_image] * num_samples					)

								lowerCamelCase__		:		Optional[int]		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Dict		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		Tuple		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		Any		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		Optional[Any]		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Dict		     =jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[Any]		     =controlnet_params

								lowerCamelCase__		:		int		     ='''Chef in the kitchen'''
								lowerCamelCase__		:		Optional[Any]		     =jax.device_count()
								lowerCamelCase__		:		Any		     =pipe.prepare_text_inputs([prompts] * num_samples					)

								lowerCamelCase__		:		Any		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''					)
								lowerCamelCase__		:		List[Any]		     =pipe.prepare_image_inputs([pose_image] * num_samples					)

								lowerCamelCase__		:		Tuple		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Optional[Any]		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		int		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		int		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		List[str]		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		int		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Any		     =jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




