


"""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 	lowerCamelCase			(	_UpperCamelCase						: List[str]				)						->			Dict:

							'''simple docstring'''



							__UpperCAmelCase  :		Dict 				=   checkpoints.load_tax_checkpoint(UpperCAmelCase_				)
							__UpperCAmelCase  :		int 				=   flatten_dict(UpperCAmelCase_				)
							return flax_params
def 	lowerCamelCase			(	_UpperCamelCase						: Dict				)						->			Optional[int]:

							'''simple docstring'''



							__UpperCAmelCase  :		Optional[int] 				=   {}

							__UpperCAmelCase  :		Tuple 				=   {
							    """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""",
							}

							__UpperCAmelCase  :		Tuple 				=   {
							    """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
																					__UpperCAmelCase  :		List[Any] 				=   """.""".join(key[1:]				)

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												__UpperCAmelCase  :		Optional[Any] 				=   new_key.replace(UpperCAmelCase_			,     UpperCAmelCase_				)

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			__UpperCAmelCase  :		List[Any] 				=   new_key.replace(UpperCAmelCase_			,     UpperCAmelCase_				)

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												__UpperCAmelCase  :		Optional[int] 				=   re.sub(R"""layers_(\d+)"""			,     R"""layer.\1"""			,     UpperCAmelCase_				)
																												__UpperCAmelCase  :		str 				=   new_key.replace("""encoder"""			,     """encoder.encoder"""				)

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

																					__UpperCAmelCase  :		List[Any] 				=   flax_dict[key]

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

							return converted_torch_dict
def 	lowerCamelCase			(	_UpperCamelCase						: int			,     _UpperCamelCase						: List[str]			,     _UpperCamelCase						: List[str]=False			,     _UpperCamelCase						: str=False				)						->			str:

							'''simple docstring'''



							__UpperCAmelCase  :		List[Any] 				=   get_flax_param(UpperCAmelCase_				)

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

							__UpperCAmelCase  :		Dict 				=   PixaStructForConditionalGeneration(UpperCAmelCase_				)

							__UpperCAmelCase  :		List[Any] 				=   rename_and_convert_flax_params(UpperCAmelCase_				)
							model.load_state_dict(UpperCAmelCase_				)

							__UpperCAmelCase  :		Optional[int] 				=   AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""				)
							__UpperCAmelCase  :		List[str] 				=   PixaStructImageProcessor()
							__UpperCAmelCase  :		List[Any] 				=   PixaStructProcessor(image_processor=UpperCAmelCase_			,     tokenizer=UpperCAmelCase_				)

							if use_large:
														__UpperCAmelCase  :		Tuple 				=   4_0_9_6

							__UpperCAmelCase  :		Union[str, Any] 				=   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
							)







'''simple docstring'''




from typing import TYPE_CHECKING

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


a__					:			Dict          =     {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Union[str, Any]          =     [
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

			a__					:			List[Any]          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



