

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    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       {}

							snake_case_     							=       {
							    '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',
							}

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

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

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

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

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

																					snake_case_     							=       flax_dict[key]

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

							return converted_torch_dict





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=False  ,				UpperCamelCase__=False ):







							'''simple docstring'''






							snake_case_     							=       get_flax_param(UpperCamelCase__ )

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

							snake_case_     							=       PixaStructForConditionalGeneration(UpperCamelCase__ )

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

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

							if use_large:
														snake_case_     							=       4096

							snake_case_     							=       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						:  Dict									=			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						:  List[str]									=			parser.parse_args()

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase						:  Tuple									=			{
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  Tuple									=			[
					    """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """M2M100ForConditionalGeneration""",
					    """M2M100Model""",
					    """M2M100PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
					from .tokenization_mam_aaa import MaMaaaTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mam_aaa import (
										    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaMaaaForConditionalGeneration,
										    MaMaaaModel,
										    MaMaaaPreTrainedModel,
										)


else:
					import sys

					_UpperCAmelCase						:  Any									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




