

"""simple docstring"""


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    a__   (   __SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Any									=      checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	List[str]									=      flatten_dict(__SCREAMING_SNAKE_CASE			)
		return flax_params






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Tuple:
		__lowerCAmelCase:	Union[str, Any]									=      {}

		__lowerCAmelCase:	Optional[Any]									=      {
		    "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",
		}

		__lowerCAmelCase:	Optional[Any]									=      {
		    "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
						__lowerCAmelCase:	str									=      ".".join(key[1:]			)

						# rename the key
						for old, new in CONVERSION_MAPPING.items():
								__lowerCAmelCase:	int									=      new_key.replace(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										__lowerCAmelCase:	Optional[Any]									=      new_key.replace(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

						if "layers" in new_key and "decoder" not in new_key:
								# use regex to replace the layer number
								__lowerCAmelCase:	Union[str, Any]									=      re.sub(R"layers_(\d+)" ,			R"layer.\1" ,			__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase:	Tuple									=      new_key.replace("encoder" ,			"encoder.encoder"			)

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

						__lowerCAmelCase:	Union[str, Any]									=      flax_dict[key]

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

		return converted_torch_dict






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=False ,			__SCREAMING_SNAKE_CASE=False			)     ->     Dict:
		__lowerCAmelCase:	str									=      get_flax_param(__SCREAMING_SNAKE_CASE			)

		if not use_large:
				__lowerCAmelCase:	List[Any]									=      PixaStructVisionConfig()
				__lowerCAmelCase:	Union[str, Any]									=      PixaStructTextConfig()
		else:
				__lowerCAmelCase:	str									=      PixaStructVisionConfig(
				    hidden_size=1_5_3_6 ,			d_ff=3_9_6_8 ,			num_attention_heads=2_4 ,			num_hidden_layers=1_8			)
				__lowerCAmelCase:	Union[str, Any]									=      PixaStructTextConfig(hidden_size=1_5_3_6 ,			d_ff=3_9_6_8 ,			num_heads=2_4 ,			num_layers=1_8			)
		__lowerCAmelCase:	List[Any]									=      PixaStructConfig(
		    vision_config=encoder_config.to_dict() ,			text_config=decoder_config.to_dict() ,			is_vqa=__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	List[Any]									=      PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	List[Any]									=      rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE			)
		model.load_state_dict(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple									=      AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"			)
		__lowerCAmelCase:	Any									=      PixaStructImageProcessor()
		__lowerCAmelCase:	Union[str, Any]									=      PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE ,			tokenizer=__SCREAMING_SNAKE_CASE			)

		if use_large:
				__lowerCAmelCase:	Union[str, Any]									=      4_0_9_6

		__lowerCAmelCase:	Union[str, Any]									=      True

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

		model.save_pretrained(__SCREAMING_SNAKE_CASE			)
		processor.save_pretrained(__SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
			__A						       =      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						       =      parser.parse_args()

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





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__A						       =      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      ["SpeechT5Tokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SpeechT5ForSpeechToText",
			    "SpeechT5ForSpeechToSpeech",
			    "SpeechT5ForTextToSpeech",
			    "SpeechT5Model",
			    "SpeechT5PreTrainedModel",
			    "SpeechT5HifiGan",
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

			__A						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


