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      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  checkpoints.load_tax_checkpoint(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  flatten_dict(snake_case__		)
						return flax_params

def 	__lowerCamelCase      (		snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  {}

						_SCREAMING_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""",
						}

						_SCREAMING_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
																		_SCREAMING_SNAKE_CASE     				=  """.""".join(key[1:]		)

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

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

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

																		_SCREAMING_SNAKE_CASE     				=  flax_dict[key]

						_SCREAMING_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):
																		_SCREAMING_SNAKE_CASE     				=  torch.from_numpy(converted_dict[key].T		)
												else:
																		_SCREAMING_SNAKE_CASE     				=  torch.from_numpy(converted_dict[key]		)

						return converted_torch_dict

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__=False	,snake_case__=False		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  get_flax_param(snake_case__		)

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

						_SCREAMING_SNAKE_CASE     				=  PixaStructForConditionalGeneration(snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  rename_and_convert_flax_params(snake_case__		)
						model.load_state_dict(snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""		)
						_SCREAMING_SNAKE_CASE     				=  PixaStructImageProcessor()
						_SCREAMING_SNAKE_CASE     				=  PixaStructProcessor(image_processor=snake_case__	,tokenizer=snake_case__		)

						if use_large:
												_SCREAMING_SNAKE_CASE     				=  40_96

						_SCREAMING_SNAKE_CASE     				=  True

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

						model.save_pretrained(snake_case__		)
						processor.save_pretrained(snake_case__		)

						print("""Model saved in {}""".format(snake_case__		)		)


if __name__ == "__main__":
		UpperCamelCase			    =     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			    =     parser.parse_args()

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









import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				int									=							TransfoXLTokenizer
			__snake_case    :				Tuple									=							False
			__snake_case    :				List[Any]									=							False







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									super().setUp()

									_SCREAMING_SNAKE_CASE     				=  [
									    """<unk>""",
									    """[CLS]""",
									    """[SEP]""",
									    """want""",
									    """unwanted""",
									    """wa""",
									    """un""",
									    """running""",
									    """,""",
									    """low""",
									    """l""",
									]
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									with open(self.vocab_file				,     """w"""				,     encoding="""utf-8"""					) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)







			def 							UpperCamelCase				(     self:  Any				,     **UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """<unk> UNwanted , running"""
									_SCREAMING_SNAKE_CASE     				=  """<unk> unwanted, running"""
									return input_text, output_text







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(vocab_file=self.vocab_file				,     lower_case=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize("""<unk> UNwanted , running"""					)
									self.assertListEqual(UpperCAmelCase_				,     ["""<unk>""", """unwanted""", """,""", """running"""]					)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     [0, 4, 8, 7]					)







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)				,     ["""hello""", """!""", """how""", """are""", """you""", """?"""]					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)				,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
									_SCREAMING_SNAKE_CASE     				=  [
									    """Hello""",
									    """(""",
									    """bracket""",
									    """)""",
									    """and""",
									    """side""",
									    """@-@""",
									    """scrolled""",
									    """[""",
									    """and""",
									    """]""",
									    """Henry""",
									    """'s""",
									    """$""",
									    """5""",
									    """@,@""",
									    """000""",
									    """with""",
									    """3""",
									    """@.@""",
									    """34""",
									    """m""",
									    """.""",
									    """What""",
									    """'s""",
									    """up""",
									    """!""",
									    """?""",
									]

									self.assertListEqual(tokenizer.tokenize(UpperCAmelCase_					)				,     UpperCAmelCase_					)

									self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_tokenizer()
									_SCREAMING_SNAKE_CASE     				=  len(UpperCAmelCase_					)

									tokenizer.add_tokens(["""new1""", """new2"""]					)
									tokenizer.move_added_token("""new1"""				,     1					)

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(UpperCAmelCase_					)				,     original_len + 2					)
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode("""new1"""					)				,     [1]					)
									self.assertEqual(tokenizer.decode([1]					)				,     """new1"""					)

