


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
			from ...onnx.config import PatchingSpec
			from ...tokenization_utils_base import PreTrainedTokenizerBase


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      Dict  				=			'''longformer'''

							def __init__(							self:     str   ,						UpperCamelCase_:     Union[List[int], int] = 512   ,						UpperCamelCase_:     int = 2   ,						UpperCamelCase_:     int = 1   ,						UpperCamelCase_:     int = 0   ,						UpperCamelCase_:     int = 2   ,						UpperCamelCase_:     int = 30_522   ,						UpperCamelCase_:     int = 768   ,						UpperCamelCase_:     int = 12   ,						UpperCamelCase_:     int = 12   ,						UpperCamelCase_:     int = 3_072   ,						UpperCamelCase_:     str = "gelu"   ,						UpperCamelCase_:     float = 0.1   ,						UpperCamelCase_:     float = 0.1   ,						UpperCamelCase_:     int = 512   ,						UpperCamelCase_:     int = 2   ,						UpperCamelCase_:     float = 0.02   ,						UpperCamelCase_:     float = 1E-1_2   ,						UpperCamelCase_:     bool = False   ,						**UpperCamelCase_:     Tuple   ,						)      ->       List[str]:





									"""simple docstring"""







									super().__init__(pad_token_id=UpperCamelCase_   ,						**UpperCamelCase_							)

									lowercase__						=				attention_window
									lowercase__						=				sep_token_id
									lowercase__						=				bos_token_id
									lowercase__						=				eos_token_id
									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				hidden_act
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				type_vocab_size
									lowercase__						=				initializer_range
									lowercase__						=				layer_norm_eps
									lowercase__						=				onnx_export


class 	_a							(      UpperCamelCase__      ):

							def __init__(							self:     Dict   ,						UpperCamelCase_:     "PretrainedConfig"   ,						UpperCamelCase_:     str = "default"   ,						UpperCamelCase_:     "List[PatchingSpec]" = None							)      ->       List[Any]:





									"""simple docstring"""







									super().__init__(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)
									lowercase__						=				True

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Mapping[str, Mapping[int, str]]:





									"""simple docstring"""







									if self.task == "multiple-choice":
											lowercase__						=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowercase__						=				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									        ('''global_attention_mask''', dynamic_axis),
									    ]							)

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       Mapping[str, Mapping[int, str]]:





									"""simple docstring"""







									lowercase__						=				super().outputs
									if self.task == "default":
											lowercase__						=				{0: '''batch'''}
									return outputs

							@property
							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       float:





									"""simple docstring"""







									return 1E-4

							@property
							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







									return max(super().default_onnx_opset   ,						14							)


							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     "PreTrainedTokenizerBase"   ,						UpperCamelCase_:     int = -1   ,						UpperCamelCase_:     int = -1   ,						UpperCamelCase_:     bool = False   ,						UpperCamelCase_:     Optional[TensorType] = None   ,						)      ->       Mapping[str, Any]:





									"""simple docstring"""







									lowercase__						=				super().generate_dummy_inputs(
									    preprocessor=UpperCamelCase_   ,						batch_size=UpperCamelCase_   ,						seq_length=UpperCamelCase_   ,						is_pair=UpperCamelCase_   ,						framework=UpperCamelCase_							)
									import torch

									# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
									# makes the export fail randomly
									lowercase__						=				torch.zeros_like(inputs['''input_ids''']							)
									# make every second token global
									lowercase__						=				1

									return inputs






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					):


		"""simple docstring"""




		lowercase__						=				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE					)
		lowercase__						=				flatten_dict(SCREAMING_SNAKE_CASE					)
		return flax_params
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""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(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										lowercase__						=				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
								lowercase__						=				re.sub(R'''layers_(\d+)'''						,    R'''layer.\1'''						,    SCREAMING_SNAKE_CASE					)
								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'''						,    SCREAMING_SNAKE_CASE					)

						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			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		lowercase__						=				get_flax_param(SCREAMING_SNAKE_CASE					)

		if not use_large:
				lowercase__						=				PixaStructVisionConfig()
				lowercase__						=				PixaStructTextConfig()
		else:
				lowercase__						=				PixaStructVisionConfig(
				    hidden_size=15_36						,    d_ff=39_68						,    num_attention_heads=24						,    num_hidden_layers=18					)
				lowercase__						=				PixaStructTextConfig(hidden_size=15_36						,    d_ff=39_68						,    num_heads=24						,    num_layers=18					)
		lowercase__						=				PixaStructConfig(
		    vision_config=encoder_config.to_dict()						,    text_config=decoder_config.to_dict()						,    is_vqa=SCREAMING_SNAKE_CASE					)

		lowercase__						=				PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE					)

		lowercase__						=				rename_and_convert_flax_params(SCREAMING_SNAKE_CASE					)
		model.load_state_dict(SCREAMING_SNAKE_CASE					)

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

		if use_large:
				lowercase__						=				40_96

		lowercase__						=				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__":
			lowerCAmelCase	=							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.')
			lowerCAmelCase	=							parser.parse_args()

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


