






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()
__a      :       str				  =   logging.get_logger(__name__)

__a      :       Tuple				  =   {
    "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",
}
__a      :       Union[str, Any]				  =   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[str]			,				__lowercase   :			Union[str, Any]			,				__lowercase   :			List[str]			,				__lowercase   :			Optional[int]    )						->      int:





				"""simple docstring"""

				for attribute in key.split("""."""    ):
								__A         =	getattr(__lowercase			,				__lowercase    )

				if weight_type is not None:
								__A         =	getattr(__lowercase			,				__lowercase    ).shape
				else:
								__A         =	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":
								__A         =	value
				elif weight_type == "weight_g":
								__A         =	value
				elif weight_type == "weight_v":
								__A         =	value
				elif weight_type == "bias":
								__A         =	value
				else:
								__A         =	value

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			Union[str, Any]    )						->      Any:





				"""simple docstring"""

				__A         =	[]
				__A         =	fairseq_model.state_dict()

				__A         =	hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				__A         =	None

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

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

				return proj_weight



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			Optional[Any]			,				__lowercase   :			Optional[Any]			,				__lowercase   :			Dict			,				__lowercase   :			Tuple    )						->      Any:





				"""simple docstring"""

				__A         =	full_name.split("""conv_layers."""    )[-1]
				__A         =	name.split("""."""    )
				__A         =	int(items[0]    )
				__A         =	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."
												)
												__A         =	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."
												)
												__A         =	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."
												)
												__A         =	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."
												)
												__A         =	value
												logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
				else:
								unused_weights.append(__lowercase    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      str:





				"""simple docstring"""

				__A    ,	__A         =	emb.weight.shape
				__A         =	nn.Linear(__lowercase			,				__lowercase			,				bias=__lowercase    )
				__A         =	emb.weight.data
				return lin_layer



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]    )						->      List[Any]:





				"""simple docstring"""

				with open(__lowercase			,				"""r"""			,				encoding="""utf-8"""    ) as f:
								__A         =	f.readlines()
								__A         =	[line.split(""" """    )[0] for line in lines]

				__A         =	len(__lowercase    )

				__A         =	{
				    """<s>""": 0,
				    """<pad>""": 1,
				    """</s>""": 2,
				    """<unk>""": 3,
				}

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



@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			Optional[int]			,				__lowercase   :			Union[str, Any]			,				__lowercase   :			Dict			,				__lowercase   :			Any			,				__lowercase   :			int			,				__lowercase   :			Optional[Any]			,				)						->      Tuple:





				"""simple docstring"""

				__A         =	WavaVecaConfig.from_pretrained(__lowercase    )
				__A         =	SpeechaTextaConfig.from_pretrained(
				    __lowercase			,				vocab_size=__lowercase			,				decoder_layers=__lowercase			,				do_stable_layer_norm=__lowercase    )

				__A         =	WavaVecaFeatureExtractor(
				    feature_size=1			,				sampling_rate=1_6_0_0_0			,				padding_value=0			,				do_normalize=__lowercase			,				return_attention_mask=__lowercase			,				)

				__A    ,	__A    ,	__A         =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]			,				arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    )}    )
				__A         =	model[0].eval()

				# set weights for wav2vec2 encoder
				__A         =	WavaVecaModel(__lowercase    )
				__A         =	recursively_load_weights_wavaveca(model.encoder			,				__lowercase    )

				__A         =	SpeechaTextaForCausalLM(__lowercase    )
				__A    ,	__A         =	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,				strict=__lowercase    )

				# set output linear layer
				unexpected_keys.remove("""embed_out"""    )
				__A         =	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}"    )

				__A         =	SpeechEncoderDecoderModel(encoder=__lowercase			,				decoder=__lowercase    )
				__A         =	False

				# add projection layer
				__A         =	nn.Parameter(projection_layer.weight    )
				__A         =	nn.Parameter(projection_layer.bias    )

				__A         =	create_vocab_dict(__lowercase    )

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

				__A         =	SpeechaTextaTokenizer(os.path.join(__lowercase			,				"""vocab.json"""    )    )
				tokenizer.save_pretrained(__lowercase    )

				__A         =	hf_wavavec.config.to_dict()
				__A         =	tokenizer.pad_token_id
				__A         =	tokenizer.bos_token_id
				__A         =	tokenizer.eos_token_id
				__A         =	"""speech_to_text_2"""
				__A         =	"""wav2vec2"""

				__A         =	SpeechEncoderDecoderConfig.from_dict(__lowercase    )

				hf_wavavec.save_pretrained(__lowercase    )
				feature_extractor.save_pretrained(__lowercase    )


if __name__ == "__main__":
		__a      :       Tuple				  =   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=10224, type=int, help="Vocab size of decoder")
		parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

		__a      :       Dict				  =   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,
		)












import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	checkpoints.load_tax_checkpoint(__lowercase    )
    __A         =	flatten_dict(__lowercase    )
    return flax_params



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      List[str]:





    """simple docstring"""

    __A         =	{}

    __A         =	{
        """token_embedder""": """embeddings""",
        """encoder_norm""": """layernorm""",
        """kernel""": """weight""",
        """.out""": """.output""",
        """scale""": """weight""",
        """embedders_0.pos_embedding""": """row_embedder.weight""",
        """embedders_1.pos_embedding""": """column_embedder.weight""",
    }

    __A         =	{
        """query""": """attention.query""",
        """key""": """attention.key""",
        """value""": """attention.value""",
        """output.dense""": """output""",
        """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
        """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
        """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
        """mlp.""": """mlp.DenseReluDense.""",
        """pre_mlp_layer_norm""": """mlp.layer_norm""",
        """self_attention.o""": """self_attention.attention.o""",
        """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
        """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
        """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
        """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
    }

    for key in flax_dict.keys():
        if "target" in key:
            # remove the first prefix from the key
            __A         =	""".""".join(key[1:]    )

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )
                __A         =	new_key.replace("""encoder"""			,				"""encoder.encoder"""    )

            elif "layers" in new_key and "decoder" in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )

            __A         =	flax_dict[key]

    __A         =	{}
    # convert converted_dict into torch format
    for key in converted_dict.keys():
        if ("embed_tokens" not in key) and ("embedder" not in key):
            __A         =	torch.from_numpy(converted_dict[key].T    )
        else:
            __A         =	torch.from_numpy(converted_dict[key]    )

    return converted_torch_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[Any]			,				__lowercase   :			Dict=False			,				__lowercase   :			List[str]=False    )						->      Any:





    """simple docstring"""

    __A         =	get_flax_param(__lowercase    )

    if not use_large:
        __A         =	PixaStructVisionConfig()
        __A         =	PixaStructTextConfig()
    else:
        __A         =	PixaStructVisionConfig(
            hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_attention_heads=2_4			,				num_hidden_layers=1_8    )
        __A         =	PixaStructTextConfig(hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_heads=2_4			,				num_layers=1_8    )
    __A         =	PixaStructConfig(
        vision_config=encoder_config.to_dict()			,				text_config=decoder_config.to_dict()			,				is_vqa=__lowercase    )

    __A         =	PixaStructForConditionalGeneration(__lowercase    )

    __A         =	rename_and_convert_flax_params(__lowercase    )
    model.load_state_dict(__lowercase    )

    __A         =	AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""    )
    __A         =	PixaStructImageProcessor()
    __A         =	PixaStructProcessor(image_processor=__lowercase			,				tokenizer=__lowercase    )

    if use_large:
        __A         =	4_0_9_6

    __A         =	True

    # mkdir if needed
    os.makedirs(__lowercase			,				exist_ok=__lowercase    )

    model.save_pretrained(__lowercase    )
    processor.save_pretrained(__lowercase    )

    print("""Model saved in {}""".format(__lowercase    )    )


if __name__ == "__main__":
  __a      :       Union[str, Any]				  =   argparse.ArgumentParser()
  parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--use_large", action="store_true", help="Use large model.")
  parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
  __a      :       Dict				  =   parser.parse_args()

  convert_pixastruct_original_pytorch_checkpoint_to_hf(
      args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
  )




