



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     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__					)	->     Tuple:



				"""simple docstring"""





				lowerCAmelCase__		  =  {}

				lowerCAmelCase__		  =  {
				    '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__		  =  {
				    '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__		  =  '.'.join(key[1:]					)

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

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

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

												lowerCAmelCase__		  =  flax_dict[key]

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

				return converted_torch_dict







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__=False    ,			snake_case__=False					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  get_flax_param(snake_case__					)

				if not use_large:
								lowerCAmelCase__		  =  PixaStructVisionConfig()
								lowerCAmelCase__		  =  PixaStructTextConfig()
				else:
								lowerCAmelCase__		  =  PixaStructVisionConfig(
								    hidden_size=1536    ,			d_ff=3968    ,			num_attention_heads=24    ,			num_hidden_layers=18					)
								lowerCAmelCase__		  =  PixaStructTextConfig(hidden_size=1536    ,			d_ff=3968    ,			num_heads=24    ,			num_layers=18					)
				lowerCAmelCase__		  =  PixaStructConfig(
				    vision_config=encoder_config.to_dict()    ,			text_config=decoder_config.to_dict()    ,			is_vqa=snake_case__					)

				lowerCAmelCase__		  =  PixaStructForConditionalGeneration(snake_case__					)

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

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

				if use_large:
								lowerCAmelCase__		  =  4096

				lowerCAmelCase__		  =  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__":
	_lowerCAmelCase	:   List[Any]    =   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	:   Dict    =   parser.parse_args()

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





import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  tempfile.mkdtemp()

						# fmt: off
						lowerCAmelCase__		  =  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
						# fmt: on
						lowerCAmelCase__		  =  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]						)						)

						lowerCAmelCase__		  =  {
						    'do_resize': True,
						    'size': {'height': 18, 'width': 18},
						    'do_normalize': True,
						    'image_mean': [0.5, 0.5, 0.5],
						    'image_std': [0.5, 0.5, 0.5],
						}
						lowerCAmelCase__		  =  os.path.join(self.tmpdirname    ,a_						)
						with open(self.image_processor_file    ,'w'    ,encoding='utf-8'						) as fp:
										json.dump(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return BertTokenizer.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return ViTImageProcessor.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						shutil.rmtree(self.tmpdirname						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [np.random.randint(255    ,size=(3, 30, 400)    ,dtype=np.uinta						)]

						lowerCAmelCase__		  =  [Image.fromarray(np.moveaxis(a_    ,0    ,-1						)						) for x in image_inputs]

						return image_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_tokenizer()
						lowerCAmelCase__		  =  self.get_image_processor()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						processor.save_pretrained(self.tmpdirname						)
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname						)

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()    ,image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

						lowerCAmelCase__		  =  self.get_tokenizer(bos_token='(BOS)'    ,eos_token='(EOS)'						)
						lowerCAmelCase__		  =  self.get_image_processor(do_normalize=a_    ,padding_value=1.0						)

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname    ,bos_token='(BOS)'    ,eos_token='(EOS)'    ,do_normalize=a_    ,padding_value=1.0						)

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer_add_kwargs.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor_add_kwargs.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  image_processor(a_    ,return_tensors='np'						)
						lowerCAmelCase__		  =  processor(images=a_    ,return_tensors='np'						)

						for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()    ,input_processor[key].sum()    ,delta=1e-2						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'

						lowerCAmelCase__		  =  processor(text=a_						)

						lowerCAmelCase__		  =  tokenizer(a_						)

						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]    ,encoded_processor[key]						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  processor(text=a_    ,images=a_						)

						self.assertListEqual(list(inputs.keys()						)    ,['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

						# test if it raises when no input is passed
						with self.assertRaises(a_						):
										processor()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase__		  =  processor.batch_decode(a_						)
						lowerCAmelCase__		  =  tokenizer.batch_decode(a_						)

						self.assertListEqual(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  processor(text=a_    ,images=a_						)

						self.assertListEqual(list(inputs.keys()						)    ,processor.model_input_names						)
