





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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      int					 =		checkpoints.load_tax_checkpoint(lowercase__ )
    lowerCamelCase__    :      Union[str, Any]					 =		flatten_dict(lowercase__ )
    return flax_params


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     int:
    lowerCamelCase__    :      Optional[Any]					 =		{}

    lowerCamelCase__    :      str					 =		{
        '''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__    :      Optional[Any]					 =		{
        '''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__    :      Optional[Any]					 =		'''.'''.join(key[1:] )

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                lowerCamelCase__    :      Optional[int]					 =		new_key.replace(lowercase__						,	lowercase__ )

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    lowerCamelCase__    :      List[str]					 =		new_key.replace(lowercase__						,	lowercase__ )

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                lowerCamelCase__    :      Union[str, Any]					 =		re.sub(r'layers_(\d+)'						,	r'layer.\1'						,	lowercase__ )
                lowerCamelCase__    :      Any					 =		new_key.replace('encoder'						,	'encoder.encoder' )

            elif "layers" in new_key and "decoder" in new_key:
                # use regex to replace the layer number
                lowerCamelCase__    :      List[Any]					 =		re.sub(r'layers_(\d+)'						,	r'layer.\1'						,	lowercase__ )

            lowerCamelCase__    :      Any					 =		flax_dict[key]

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

    return converted_torch_dict


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False						,	_UpperCAmelCase=False )			->     List[str]:
    lowerCamelCase__    :      int					 =		get_flax_param(lowercase__ )

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

    lowerCamelCase__    :      int					 =		PixaStructForConditionalGeneration(lowercase__ )

    lowerCamelCase__    :      Optional[Any]					 =		rename_and_convert_flax_params(lowercase__ )
    model.load_state_dict(lowercase__ )

    lowerCamelCase__    :      List[Any]					 =		AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer' )
    lowerCamelCase__    :      Tuple					 =		PixaStructImageProcessor()
    lowerCamelCase__    :      Dict					 =		PixaStructProcessor(image_processor=lowercase__						,	tokenizer=lowercase__ )

    if use_large:
        lowerCamelCase__    :      Optional[Any]					 =		4096

    lowerCamelCase__    :      Tuple					 =		True

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

    model.save_pretrained(lowercase__ )
    processor.save_pretrained(lowercase__ )

    print('Model saved in {}'.format(lowercase__ ) )


if __name__ == "__main__":
    _UpperCAmelCase				:       Dict	   =				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				:       List[Any]	   =				parser.parse_args()

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









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VivitImageProcessor
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Any			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Tuple=7			,			lowerCAmelCase__			:List[Any]=3			,			lowerCAmelCase__			:Any=10			,			lowerCAmelCase__			:Optional[int]=18			,			lowerCAmelCase__			:Dict=30			,			lowerCAmelCase__			:Tuple=400			,			lowerCAmelCase__			:List[Any]=True			,			lowerCAmelCase__			:Tuple=None			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:Optional[Any]=None			,			)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		size if size is not None else {'''shortest_edge''': 18}
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

										__SCREAMING_SNAKE_CASE						:     Tuple					=		parent
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		batch_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		num_channels
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		num_frames
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_size
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		min_resolution
										__SCREAMING_SNAKE_CASE						:     Any					=		max_resolution
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		do_resize
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		do_normalize
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		image_mean
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_std
										__SCREAMING_SNAKE_CASE						:     str					=		crop_size



				def 		__magic_name__(       self			:Tuple	)		->			Any:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		VivitImageProcessor if is_vision_available() else None


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		VivitImageProcessingTester(self	)


				@property
				def 		__magic_name__(       self			:int	)		->			Union[str, Any]:
										return self.image_processor_tester.prepare_image_processor_dict()


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_mean'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_std'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_normalize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_resize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_center_crop'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''size'''	)	)


				def 		__magic_name__(       self			:Optional[Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.image_processing_class.from_dict(self.image_processor_dict	)
										self.assertEqual(image_processor.size			,			{'''shortest_edge''': 18}	)
										self.assertEqual(image_processor.crop_size			,			{'''height''': 18, '''width''': 18}	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.image_processing_class.from_dict(self.image_processor_dict			,			size=42			,			crop_size=84	)
										self.assertEqual(image_processor.size			,			{'''shortest_edge''': 42}	)
										self.assertEqual(image_processor.crop_size			,			{'''height''': 84, '''width''': 84}	)


				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PIL videos
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			Image.Image	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)


				def 		__magic_name__(       self			:str	)		->			int:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Any					=		self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:     List[str]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			numpify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			np.ndarray	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Any					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)



				def 		__magic_name__(       self			:Any	)		->			List[str]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			torchify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			torch.Tensor	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)
