

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__       (  lowerCamelCase__			)  ->     List[str]:
	__lowerCamelCase				:				Union[str, Any]					       = checkpoints.load_tax_checkpoint(_A			)
	__lowerCamelCase				:				int					       = flatten_dict(_A			)
	return flax_params






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Dict:
	__lowerCamelCase				:				List[str]					       = {}

	__lowerCamelCase				:				Dict					       = {
	    '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				:				str					       = {
	    '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				:				List[Any]					       = '.'.join(key[1:]			)

			# rename the key
			for old, new in CONVERSION_MAPPING.items():
				__lowerCamelCase				:				str					       = new_key.replace(_A	, _A			)

			if "decoder" in new_key:
				for old, new in DECODER_CONVERSION_MAPPING.items():
					__lowerCamelCase				:				Tuple					       = new_key.replace(_A	, _A			)

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

			elif "layers" in new_key and "decoder" in new_key:
				# use regex to replace the layer number
				__lowerCamelCase				:				Union[str, Any]					       = re.sub(R'layers_(\d+)'	, R'layer.\1'	, _A			)

			__lowerCamelCase				:				List[Any]					       = flax_dict[key]

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

	return converted_torch_dict






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False	, lowerCamelCase__=False			)  ->     Union[str, Any]:
	__lowerCamelCase				:				List[Any]					       = get_flax_param(_A			)

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

	__lowerCamelCase				:				int					       = PixaStructForConditionalGeneration(_A			)

	__lowerCamelCase				:				Optional[Any]					       = rename_and_convert_flax_params(_A			)
	model.load_state_dict(_A			)

	__lowerCamelCase				:				Dict					       = AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'			)
	__lowerCamelCase				:				Optional[int]					       = PixaStructImageProcessor()
	__lowerCamelCase				:				int					       = PixaStructProcessor(image_processor=_A	, tokenizer=_A			)

	if use_large:
		__lowerCamelCase				:				Any					       = 4_0_9_6

	__lowerCamelCase				:				int					       = True

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

	model.save_pretrained(_A			)
	processor.save_pretrained(_A			)

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


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
					)










import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def __UpperCamelCase			(       _A ,  _A  ):
						assert isinstance(_A ,  _A  )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory''' ,  [False, True]  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A ,  keep_in_memory=_A  ).read()
						_check_json_dataset(_A ,  _A  )





@pytest.mark.parametrize(
    '''features''' ,  [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ] ,  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  features.copy() if features else default_expected_features
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  features=_A ,  cache_dir=_A  ).read()
						_check_json_dataset(_A ,  _A  )





@pytest.mark.parametrize(
    '''features''' ,  [
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ] ,  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
						lowerCAmelCase_      			=  features.copy() if features else default_expected_features
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  features=_A ,  cache_dir=_A  ).read()
						assert isinstance(_A ,  _A  )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_3", "col_1", "col_2"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





def __UpperCamelCase			(       _A ,  _A  ):
						# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
						lowerCAmelCase_      			=  {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
						lowerCAmelCase_      			=  features.copy()
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  features=_A ,  cache_dir=_A  ).read()
						assert isinstance(_A ,  _A  )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_2", "col_3", "col_1"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''split''' ,  [None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A ,  split=_A  ).read()
						_check_json_dataset(_A ,  _A  )
						assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type''' ,  [str, list]  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						if issubclass(_A ,  _A  ):
												lowerCAmelCase_      			=  jsonl_path
						elif issubclass(_A ,  _A  ):
												lowerCAmelCase_      			=  [jsonl_path]
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A  ).read()
						_check_json_dataset(_A ,  _A  )





def __UpperCamelCase			(       _A ,  _A ,  _A=("train",)  ):
						assert isinstance(_A ,  _A  )
						for split in splits:
												lowerCAmelCase_      			=  dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory''' ,  [False, True]  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowerCAmelCase_      			=  JsonDatasetReader({'''train''': jsonl_path} ,  cache_dir=_A ,  keep_in_memory=_A  ).read()
						_check_json_datasetdict(_A ,  _A  )





@pytest.mark.parametrize(
    '''features''' ,  [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ] ,  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  features.copy() if features else default_expected_features
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  JsonDatasetReader({'''train''': jsonl_path} ,  features=_A ,  cache_dir=_A  ).read()
						_check_json_datasetdict(_A ,  _A  )





@pytest.mark.parametrize('''split''' ,  [None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						if split:
												lowerCAmelCase_      			=  {split: jsonl_path}
						else:
												lowerCAmelCase_      			=  '''train'''
												lowerCAmelCase_      			=  {'''train''': jsonl_path, '''test''': jsonl_path}
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A  ).read()
						_check_json_datasetdict(_A ,  _A ,  splits=list(path.keys()  )  )
						assert all(dataset[split].split == split for split in path.keys()  )





def __UpperCamelCase			(       _A  ):
						return json.load(_A  )





def __UpperCamelCase			(       _A  ):
						return [json.loads(_A  ) for line in buffer]






class  A		:



		@pytest.mark.parametrize('''lines, load_json_function''',	[(True, load_json_lines), (False, load_json)]   )
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json_function(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								assert isinstance(exported_content[0],	UpperCamelCase__   )
								assert len(UpperCamelCase__   ) == 10



		@pytest.mark.parametrize(
		    '''orient, container, keys, len_at''',	[
		        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
		        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
		        ('''index''', dict, set('''0123456789'''   ), None),
		        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
		        ('''values''', list, None, None),
		        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
		    ],	)
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__,	orient=UpperCamelCase__   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(UpperCamelCase__,	'''keys'''   ) and not hasattr(exported_content[0],	'''keys'''   )
								if len_at:
														assert len(exported_content[len_at]   ) == 10
								else:
														assert len(UpperCamelCase__   ) == 10



		@pytest.mark.parametrize('''lines, load_json_function''',	[(True, load_json_lines), (False, load_json)]   )
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__,	num_proc=2   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json_function(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								assert isinstance(exported_content[0],	UpperCamelCase__   )
								assert len(UpperCamelCase__   ) == 10



		@pytest.mark.parametrize(
		    '''orient, container, keys, len_at''',	[
		        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
		        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
		        ('''index''', dict, set('''0123456789'''   ), None),
		        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
		        ('''values''', list, None, None),
		        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
		    ],	)
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__,	orient=UpperCamelCase__,	num_proc=2   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(UpperCamelCase__,	'''keys'''   ) and not hasattr(exported_content[0],	'''keys'''   )
								if len_at:
														assert len(exported_content[len_at]   ) == 10
								else:
														assert len(UpperCamelCase__   ) == 10



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								with pytest.raises(UpperCamelCase__   ):
														with io.BytesIO() as buffer:
																				JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	num_proc=0   )







		@pytest.mark.parametrize('''compression, extension''',	[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]   )
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  tmp_path_factory.mktemp('''data'''   ) / f"test.json.{extension}"
								lowerCAmelCase_      			=  str(shared_datadir / f"test_file.json.{extension}"   )
								JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	compression=UpperCamelCase__   ).write()

								with fsspec.open(UpperCamelCase__,	'''rb''',	compression='''infer'''   ) as f:
														lowerCAmelCase_      			=  f.read()
								with fsspec.open(UpperCamelCase__,	'''rb''',	compression='''infer'''   ) as f:
														lowerCAmelCase_      			=  f.read()
								assert exported_content == original_content




