


"""simple docstring"""




import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_rjieba
@require_tokenizers
class 	_a							(      UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      List[Any]  				=			RoFormerTokenizer
							_lowercase     :      Dict  				=			RoFormerTokenizerFast
							_lowercase     :      List[str]  				=			True
							_lowercase     :      Dict  				=			True

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Any:





									"""simple docstring"""







									super().setUp()

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						**UpperCamelCase_:     Optional[int]							)      ->       Tuple:





									"""simple docstring"""







									return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[Any]   ,						**UpperCamelCase_:     Dict							)      ->       Optional[int]:





									"""simple docstring"""







									return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				'''永和服装饰品有限公司,今天天气非常好'''
									lowercase__						=				'''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
									return input_text, output_text

							def 				lowerCamelCase_			(							self:     Dict							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				self.get_tokenizer()
									lowercase__					,		lowercase__						=				self.get_chinese_input_output_texts()
									lowercase__						=				tokenizer.tokenize(UpperCamelCase_							)

									self.assertListEqual(UpperCamelCase_   ,						output_text.split()							)

									lowercase__						=				tokens + [tokenizer.unk_token]
									lowercase__						=				[22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_							)   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				self.get_rust_tokenizer()
									lowercase__					,		lowercase__						=				self.get_chinese_input_output_texts()
									lowercase__						=				tokenizer.tokenize(UpperCamelCase_							)
									self.assertListEqual(UpperCamelCase_   ,						output_text.split()							)
									lowercase__						=				tokens + [tokenizer.unk_token]
									lowercase__						=				[22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_							)   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Any							)      ->       Optional[int]:





									"""simple docstring"""







									pass

							def 				lowerCamelCase_			(							self:     Any							)      ->       List[str]:





									"""simple docstring"""







									pass


							def 				lowerCamelCase_			(							self:     str							)      ->       int:





									"""simple docstring"""







									pass






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
			)


