



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   (						_lowerCamelCase    ):
			A							:					List[str]              =						checkpoints.load_tax_checkpoint(_lowerCamelCase    )
			A							:					Dict              =						flatten_dict(_lowerCamelCase    )
			return flax_params




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Optional[Any]              =						{}

			A							:					Union[str, 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",
			}

			A							:					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
									A							:					Any              =						".".join(key[1:]    )

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												A							:					List[str]              =						new_key.replace(_lowerCamelCase ,    _lowerCamelCase    )

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															A							:					Dict              =						new_key.replace(_lowerCamelCase ,    _lowerCamelCase    )

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												A							:					int              =						re.sub(R"layers_(\d+)" ,    R"layer.\1" ,    _lowerCamelCase    )
												A							:					List[str]              =						new_key.replace("encoder" ,    "encoder.encoder"    )

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

									A							:					str              =						flax_dict[key]

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

			return converted_torch_dict




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase=False ,    _lowerCamelCase=False    ):
			A							:					Any              =						get_flax_param(_lowerCamelCase    )

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

			A							:					Dict              =						PixaStructForConditionalGeneration(_lowerCamelCase    )

			A							:					Tuple              =						rename_and_convert_flax_params(_lowerCamelCase    )
			model.load_state_dict(_lowerCamelCase    )

			A							:					Optional[int]              =						AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"    )
			A							:					List[str]              =						PixaStructImageProcessor()
			A							:					int              =						PixaStructProcessor(image_processor=_lowerCamelCase ,    tokenizer=_lowerCamelCase    )

			if use_large:
						A							:					Dict              =						4096

			A							:					List[Any]              =						True

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

			model.save_pretrained(_lowerCamelCase    )
			processor.save_pretrained(_lowerCamelCase    )

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


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

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




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class 	lowerCamelCase_    :






						'''simple docstring'''







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				str:
									return self.get_dummy_input()







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									if self.block_type == "down":
												return (4, 32, 16, 16)
									elif self.block_type == "mid":
												return (4, 32, 32, 32)
									elif self.block_type == "up":
												return (4, 32, 64, 64)

									raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Union[str, Any]=True	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			Optional[int]=False	,	)      ->				Dict:
									A							:					Optional[Any]              =						4
									A							:					List[str]              =						32
									A							:					Any              =						(32, 32)

									A							:					str              =						torch.manual_seed(0 )
									A							:					int              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						(batch_size, num_channels) + sizes
									A							:					Dict              =						randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )
									A							:					int              =						{"hidden_states": hidden_states}

									if include_temb:
												A							:					Any              =						1_28
												A							:					List[str]              =						randn_tensor((batch_size, temb_channels)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									if include_res_hidden_states_tuple:
												A							:					str              =						torch.manual_seed(1 )
												A							:					Tuple              =						(randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase ),)

									if include_encoder_hidden_states:
												A							:					Dict              =						floats_tensor((batch_size, 32, 32) ).to(__lowerCamelCase )

									if include_skip_sample:
												A							:					Optional[int]              =						randn_tensor(((batch_size, 3) + sizes)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									return dummy_input







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									A							:					Dict              =						{
									    "in_channels": 32,
									    "out_channels": 32,
									    "temb_channels": 1_28,
									}
									if self.block_type == "up":
												A							:					Dict              =						32

									if self.block_type == "mid":
												init_dict.pop("out_channels" )

									A							:					str              =						self.dummy_input
									return init_dict, inputs_dict







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Optional[int] )      ->				Union[str, Any]:
									A       , A							:					str              =						self.prepare_init_args_and_inputs_for_common()
									A							:					List[Any]              =						self.block_class(**__lowerCamelCase )
									unet_block.to(__lowerCamelCase )
									unet_block.eval()

									with torch.no_grad():
												A							:					int              =						unet_block(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Union[str, Any]              =						output[0]

									self.assertEqual(output.shape	,	self.output_shape )

									A							:					Any              =						output[0, -1, -3:, -3:]
									A							:					Union[str, Any]              =						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
									assert torch_all_close(output_slice.flatten()	,	__lowerCamelCase	,	atol=5e-3 )







						@unittest.skipIf(torch_device == "mps"	,	"Training is not supported in mps" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A       , A							:					Tuple              =						self.prepare_init_args_and_inputs_for_common()
									A							:					str              =						self.block_class(**__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.train()
									A							:					Optional[int]              =						model(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Optional[Any]              =						output[0]

									A							:					List[str]              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						randn_tensor(output.shape	,	device=__lowerCamelCase )
									A							:					Dict              =						torch.nn.functional.mse_loss(__lowerCamelCase	,	__lowerCamelCase )
									loss.backward()