
'''simple docstring'''







# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowercase			:  int   							=	abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						config.addinivalue_line(
						    '''markers'''				,				'''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested'''							)
						config.addinivalue_line(
						    '''markers'''				,				'''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested'''							)
						config.addinivalue_line('''markers'''				,				'''is_pipeline_test: mark test to run only when pipelines are tested'''							)
						config.addinivalue_line('''markers'''				,				'''is_staging_test: mark test to run only in the staging environment'''							)
						config.addinivalue_line('''markers'''				,				'''accelerate_tests: mark test that require accelerate'''							)
						config.addinivalue_line('''markers'''				,				'''tool_tests: mark the tool tests that are run on their specific schedule'''							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]							):



						"""simple docstring"""





						from transformers.testing_utils import pytest_addoption_shared

						pytest_addoption_shared(__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]							):



						"""simple docstring"""





						from transformers.testing_utils import pytest_terminal_summary_main

						lowercase_     :					Optional[Any]													=	terminalreporter.config.getoption('''--make-reports'''							)
						if make_reports:
												pytest_terminal_summary_main(__SCREAMING_SNAKE_CASE				,				id=__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			Any							):



						"""simple docstring"""





						if exitstatus == 5:
												lowercase_     :					str													=	0


# Doctest custom flag to ignore output.
_lowercase			:  Optional[int]   							=	doctest.register_optionflag("IGNORE_RESULT")

_lowercase			:  Union[str, Any]   							=	doctest.OutputChecker






class 			lowerCAmelCase__       (  lowerCamelCase_					):






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if IGNORE_RESULT & optionflags:
																	return True
											return OutputChecker.check_output(self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)







_lowercase			:  Optional[Any]   							=	CustomOutputChecker
_lowercase			:  List[str]   							=	HfDoctestModule




_lowercase			:  Dict   							=	HfDocTestParser





'''simple docstring'''







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  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Dict													=	flatten_dict(__SCREAMING_SNAKE_CASE							)
						return flax_params
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					int													=	{}

						lowercase_     :					Any													=	{
						    '''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_     :					Tuple													=	{
						    '''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_     :					Tuple													=	'''.'''.join(key[1:]							)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase_     :					Optional[Any]													=	new_key.replace(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowercase_     :					Optional[Any]													=	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_     :					List[Any]													=	re.sub(R'''layers_(\d+)'''				,				R'''layer.\1'''				,				__SCREAMING_SNAKE_CASE							)
																								lowercase_     :					Union[str, Any]													=	new_key.replace('''encoder'''				,				'''encoder.encoder'''							)

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

																		lowercase_     :					Dict													=	flax_dict[key]

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

						return converted_torch_dict
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Tuple=False				,				__SCREAMING_SNAKE_CASE :			List[Any]=False							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	get_flax_param(__SCREAMING_SNAKE_CASE							)

						if not use_large:
												lowercase_     :					List[str]													=	PixaStructVisionConfig()
												lowercase_     :					Optional[Any]													=	PixaStructTextConfig()
						else:
												lowercase_     :					Optional[int]													=	PixaStructVisionConfig(
												    hidden_size=1536				,				d_ff=3968				,				num_attention_heads=24				,				num_hidden_layers=18							)
												lowercase_     :					Dict													=	PixaStructTextConfig(hidden_size=1536				,				d_ff=3968				,				num_heads=24				,				num_layers=18							)
						lowercase_     :					str													=	PixaStructConfig(
						    vision_config=encoder_config.to_dict()				,				text_config=decoder_config.to_dict()				,				is_vqa=__SCREAMING_SNAKE_CASE							)

						lowercase_     :					List[str]													=	PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					int													=	rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE							)
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					str													=	AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''							)
						lowercase_     :					List[Any]													=	PixaStructImageProcessor()
						lowercase_     :					int													=	PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE				,				tokenizer=__SCREAMING_SNAKE_CASE							)

						if use_large:
												lowercase_     :					Tuple													=	4096

						lowercase_     :					Optional[int]													=	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__":
							_lowercase			:  str   							=	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.")
							_lowercase			:  Tuple   							=	parser.parse_args()

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



