




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {
    "facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		'nllb-moe'
						UpperCamelCase_	:       Union[str, Any]       	=		['past_key_values']
						UpperCamelCase_	:       Optional[Any]       	=		{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






						def __init__( self :     List[str]				,							lowerCamelCase__ :     Tuple=128_112				,							lowerCamelCase__ :     Optional[int]=1_024				,							lowerCamelCase__ :     Any=12				,							lowerCamelCase__ :     List[str]=4_096				,							lowerCamelCase__ :     Optional[int]=16				,							lowerCamelCase__ :     Dict=12				,							lowerCamelCase__ :     Any=4_096				,							lowerCamelCase__ :     Optional[Any]=16				,							lowerCamelCase__ :     List[str]=0.0_5				,							lowerCamelCase__ :     Optional[int]=0.0_5				,							lowerCamelCase__ :     Any=True				,							lowerCamelCase__ :     int=True				,							lowerCamelCase__ :     int="relu"				,							lowerCamelCase__ :     Optional[Any]=1_024				,							lowerCamelCase__ :     Any=0.1				,							lowerCamelCase__ :     Union[str, Any]=0.1				,							lowerCamelCase__ :     int=0.0				,							lowerCamelCase__ :     Dict=0.0_2				,							lowerCamelCase__ :     Tuple=2				,							lowerCamelCase__ :     Optional[Any]=True				,							lowerCamelCase__ :     int=False				,							lowerCamelCase__ :     int="float32"				,							lowerCamelCase__ :     Dict=False				,							lowerCamelCase__ :     Any=128				,							lowerCamelCase__ :     str=64				,							lowerCamelCase__ :     Any=4				,							lowerCamelCase__ :     Any=4				,							lowerCamelCase__ :     Any=0.0_0_1				,							lowerCamelCase__ :     Union[str, Any]=0.0_0_1				,							lowerCamelCase__ :     int="all"				,							lowerCamelCase__ :     Any=False				,							lowerCamelCase__ :     Optional[Any]=False				,							lowerCamelCase__ :     Dict=1.0				,							lowerCamelCase__ :     Tuple=0.2				,							lowerCamelCase__ :     Optional[int]=1				,							lowerCamelCase__ :     Union[str, Any]=0				,							lowerCamelCase__ :     Optional[Any]=2				,							lowerCamelCase__ :     Dict=False				,							**lowerCamelCase__ :     Tuple				,							)    ->						int:
													"""simple docstring"""


													__lowercase      =    vocab_size
													__lowercase      =    max_position_embeddings
													__lowercase      =    d_model
													__lowercase      =    encoder_ffn_dim
													__lowercase      =    encoder_layers
													__lowercase      =    encoder_attention_heads
													__lowercase      =    decoder_ffn_dim
													__lowercase      =    decoder_layers
													__lowercase      =    decoder_attention_heads
													__lowercase      =    dropout
													__lowercase      =    attention_dropout
													__lowercase      =    activation_dropout
													__lowercase      =    activation_function
													__lowercase      =    init_std
													__lowercase      =    encoder_layerdrop
													__lowercase      =    decoder_layerdrop
													__lowercase      =    use_cache
													__lowercase      =    encoder_layers
													__lowercase      =    scale_embedding  # scale factor will be sqrt(d_model) if True
													__lowercase      =    router_z_loss_coef
													__lowercase      =    router_aux_loss_coef
													__lowercase      =    decoder_sparse_step
													__lowercase      =    encoder_sparse_step
													__lowercase      =    num_experts
													__lowercase      =    expert_capacity
													__lowercase      =    router_bias
													if router_dtype not in ["float32", "float16", "bfloat16"]:
																				raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'				)
													__lowercase      =    router_dtype

													__lowercase      =    router_ignore_padding_tokens
													__lowercase      =    batch_prioritized_routing
													__lowercase      =    second_expert_policy
													__lowercase      =    normalize_router_prob_before_dropping
													__lowercase      =    moe_eval_capacity_token_fraction
													__lowercase      =    moe_token_dropout
													__lowercase      =    output_router_logits
													super().__init__(
													    pad_token_id=lowerCamelCase__				,							bos_token_id=lowerCamelCase__				,							eos_token_id=lowerCamelCase__				,							is_encoder_decoder=lowerCamelCase__				,							decoder_start_token_id=lowerCamelCase__				,							**lowerCamelCase__				,							)












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(       UpperCamelCase__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''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''',
							}

							__lowercase      =    {
							    '''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
																					__lowercase      =    '''.'''.join(key[1:]				)

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

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

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

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

																					__lowercase      =    flax_dict[key]

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

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

							if not use_large:
														__lowercase      =    PixaStructVisionConfig()
														__lowercase      =    PixaStructTextConfig()
							else:
														__lowercase      =    PixaStructVisionConfig(
														    hidden_size=1536				,       d_ff=3968				,       num_attention_heads=24				,       num_hidden_layers=18				)
														__lowercase      =    PixaStructTextConfig(hidden_size=1536				,       d_ff=3968				,       num_heads=24				,       num_layers=18				)
							__lowercase      =    PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=UpperCamelCase__				)

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

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

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

							if use_large:
														__lowercase      =    4096

							__lowercase      =    True

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

							model.save_pretrained(UpperCamelCase__				)
							processor.save_pretrained(UpperCamelCase__				)

							print('''Model saved in {}'''.format(UpperCamelCase__				)				)


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

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






