


"""simple docstring"""




from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class 	_a							(      lowercase__      ):
							_lowercase     :      torch.FloatTensor


class 	_a							(      nn.Module      ):

							def __init__(							self:     Tuple   ,						UpperCamelCase_:     List[str]=3   ,						UpperCamelCase_:     Optional[int]=3   ,						UpperCamelCase_:     List[Any]=("DownEncoderBlock2D",)   ,						UpperCamelCase_:     Tuple=(64,)   ,						UpperCamelCase_:     Dict=2   ,						UpperCamelCase_:     Optional[int]=32   ,						UpperCamelCase_:     Tuple="silu"   ,						UpperCamelCase_:     Tuple=True   ,						)      ->       List[str]:





									"""simple docstring"""







									super().__init__()
									lowercase__						=				layers_per_block

									lowercase__						=				torch.nn.Convad(
									    _a   ,						block_out_channels[0]   ,						kernel_size=3   ,						stride=1   ,						padding=1   ,						)

									lowercase__						=				None
									lowercase__						=				nn.ModuleList([]							)

									# down
									lowercase__						=				block_out_channels[0]
									for i, down_block_type in enumerate(_a							):
											lowercase__						=				output_channel
											lowercase__						=				block_out_channels[i]
											lowercase__						=				i == len(_a							) - 1

											lowercase__						=				get_down_block(
											    _a   ,						num_layers=self.layers_per_block   ,						in_channels=_a   ,						out_channels=_a   ,						add_downsample=not is_final_block   ,						resnet_eps=1E-6   ,						downsample_padding=0   ,						resnet_act_fn=_a   ,						resnet_groups=_a   ,						attention_head_dim=_a   ,						temb_channels=_a   ,						)
											self.down_blocks.append(_a							)

									# mid
									lowercase__						=				UNetMidBlockaD(
									    in_channels=block_out_channels[-1]   ,						resnet_eps=1E-6   ,						resnet_act_fn=_a   ,						output_scale_factor=1   ,						resnet_time_scale_shift='''default'''   ,						attention_head_dim=block_out_channels[-1]   ,						resnet_groups=_a   ,						temb_channels=_a   ,						)

									# out
									lowercase__						=				nn.GroupNorm(num_channels=block_out_channels[-1]   ,						num_groups=_a   ,						eps=1E-6							)
									lowercase__						=				nn.SiLU()

									lowercase__						=				2 * out_channels if double_z else out_channels
									lowercase__						=				nn.Convad(block_out_channels[-1]   ,						_a   ,						3   ,						padding=1							)

									lowercase__						=				False


							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     Optional[Any]							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				x
									lowercase__						=				self.conv_in(_a							)

									if self.training and self.gradient_checkpointing:

											def create_custom_forward(UpperCamelCase_:     str							):
													def custom_forward(*UpperCamelCase_:     str							):
															return module(*_a							)

													return custom_forward

											# down
											if is_torch_version('''>='''   ,						'''1.11.0'''							):
													for down_block in self.down_blocks:
															lowercase__						=				torch.utils.checkpoint.checkpoint(
															    create_custom_forward(_a							)   ,						_a   ,						use_reentrant=_a							)
													# middle
													lowercase__						=				torch.utils.checkpoint.checkpoint(
													    create_custom_forward(self.mid_block							)   ,						_a   ,						use_reentrant=_a							)
											else:
													for down_block in self.down_blocks:
															lowercase__						=				torch.utils.checkpoint.checkpoint(create_custom_forward(_a							)   ,						_a							)
													# middle
													lowercase__						=				torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block							)   ,						_a							)

									else:
											# down
											for down_block in self.down_blocks:
													lowercase__						=				down_block(_a							)

											# middle
											lowercase__						=				self.mid_block(_a							)

									# post-process
									lowercase__						=				self.conv_norm_out(_a							)
									lowercase__						=				self.conv_act(_a							)
									lowercase__						=				self.conv_out(_a							)

									return sample


class 	_a							(      nn.Module      ):

							def __init__(							self:     List[Any]   ,						UpperCamelCase_:     Dict=3   ,						UpperCamelCase_:     Optional[int]=3   ,						UpperCamelCase_:     List[Any]=("UpDecoderBlock2D",)   ,						UpperCamelCase_:     Union[str, Any]=(64,)   ,						UpperCamelCase_:     Union[str, Any]=2   ,						UpperCamelCase_:     List[str]=32   ,						UpperCamelCase_:     Optional[int]="silu"   ,						UpperCamelCase_:     Optional[Any]="group"   ,						)      ->       int:





									"""simple docstring"""







									super().__init__()
									lowercase__						=				layers_per_block

									lowercase__						=				nn.Convad(
									    _a   ,						block_out_channels[-1]   ,						kernel_size=3   ,						stride=1   ,						padding=1   ,						)

									lowercase__						=				None
									lowercase__						=				nn.ModuleList([]							)

									lowercase__						=				in_channels if norm_type == 'spatial' else None

									# mid
									lowercase__						=				UNetMidBlockaD(
									    in_channels=block_out_channels[-1]   ,						resnet_eps=1E-6   ,						resnet_act_fn=_a   ,						output_scale_factor=1   ,						resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type   ,						attention_head_dim=block_out_channels[-1]   ,						resnet_groups=_a   ,						temb_channels=_a   ,						)

									# up
									lowercase__						=				list(reversed(_a							)							)
									lowercase__						=				reversed_block_out_channels[0]
									for i, up_block_type in enumerate(_a							):
											lowercase__						=				output_channel
											lowercase__						=				reversed_block_out_channels[i]

											lowercase__						=				i == len(_a							) - 1

											lowercase__						=				get_up_block(
											    _a   ,						num_layers=self.layers_per_block + 1   ,						in_channels=_a   ,						out_channels=_a   ,						prev_output_channel=_a   ,						add_upsample=not is_final_block   ,						resnet_eps=1E-6   ,						resnet_act_fn=_a   ,						resnet_groups=_a   ,						attention_head_dim=_a   ,						temb_channels=_a   ,						resnet_time_scale_shift=_a   ,						)
											self.up_blocks.append(_a							)
											lowercase__						=				output_channel

									# out
									if norm_type == "spatial":
											lowercase__						=				SpatialNorm(block_out_channels[0]   ,						_a							)
									else:
											lowercase__						=				nn.GroupNorm(num_channels=block_out_channels[0]   ,						num_groups=_a   ,						eps=1E-6							)
									lowercase__						=				nn.SiLU()
									lowercase__						=				nn.Convad(block_out_channels[0]   ,						_a   ,						3   ,						padding=1							)

									lowercase__						=				False


							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     List[str]=None							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				z
									lowercase__						=				self.conv_in(_a							)

									lowercase__						=				next(iter(self.up_blocks.parameters()							)							).dtype
									if self.training and self.gradient_checkpointing:

											def create_custom_forward(UpperCamelCase_:     Optional[Any]							):
													def custom_forward(*UpperCamelCase_:     int							):
															return module(*_a							)

													return custom_forward

											if is_torch_version('''>='''   ,						'''1.11.0'''							):
													# middle
													lowercase__						=				torch.utils.checkpoint.checkpoint(
													    create_custom_forward(self.mid_block							)   ,						_a   ,						_a   ,						use_reentrant=_a							)
													lowercase__						=				sample.to(_a							)

													# up
													for up_block in self.up_blocks:
															lowercase__						=				torch.utils.checkpoint.checkpoint(
															    create_custom_forward(_a							)   ,						_a   ,						_a   ,						use_reentrant=_a							)
											else:
													# middle
													lowercase__						=				torch.utils.checkpoint.checkpoint(
													    create_custom_forward(self.mid_block							)   ,						_a   ,						_a							)
													lowercase__						=				sample.to(_a							)

													# up
													for up_block in self.up_blocks:
															lowercase__						=				torch.utils.checkpoint.checkpoint(create_custom_forward(_a							)   ,						_a   ,						_a							)
									else:
											# middle
											lowercase__						=				self.mid_block(_a   ,						_a							)
											lowercase__						=				sample.to(_a							)

											# up
											for up_block in self.up_blocks:
													lowercase__						=				up_block(_a   ,						_a							)

											# post-process
									if latent_embeds is None:
											lowercase__						=				self.conv_norm_out(_a							)
									else:
											lowercase__						=				self.conv_norm_out(_a   ,						_a							)
									lowercase__						=				self.conv_act(_a							)
									lowercase__						=				self.conv_out(_a							)

									return sample


class 	_a							(      nn.Module      ):

							def __init__(							self:     Dict   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     int=None   ,						UpperCamelCase_:     List[str]="random"   ,						UpperCamelCase_:     List[Any]=False   ,						UpperCamelCase_:     str=True							)      ->       Optional[int]:





									"""simple docstring"""







									super().__init__()
									lowercase__						=				n_e
									lowercase__						=				vq_embed_dim
									lowercase__						=				beta
									lowercase__						=				legacy

									lowercase__						=				nn.Embedding(self.n_e   ,						self.vq_embed_dim							)
									self.embedding.weight.data.uniform_(-1.0 / self.n_e   ,						1.0 / self.n_e							)

									lowercase__						=				remap
									if self.remap is not None:
											self.register_buffer('''used'''   ,						torch.tensor(np.load(self.remap							)							)							)
											lowercase__						=				self.used.shape[0]
											lowercase__						=				unknown_index  # "random" or "extra" or integer
											if self.unknown_index == "extra":
													lowercase__						=				self.re_embed
													lowercase__						=				self.re_embed + 1
											print(
											    f'Remapping {self.n_e} indices to {self.re_embed} indices. '
											    f'Using {self.unknown_index} for unknown indices.'							)
									else:
											lowercase__						=				n_e

									lowercase__						=				sane_index_shape

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     Dict							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				inds.shape
									assert len(_a							) > 1
									lowercase__						=				inds.reshape(ishape[0]   ,						-1							)
									lowercase__						=				self.used.to(_a							)
									lowercase__						=				(inds[:, :, None] == used[None, None, ...]).long()
									lowercase__						=				match.argmax(-1							)
									lowercase__						=				match.sum(2							) < 1
									if self.unknown_index == "random":
											lowercase__						=				torch.randint(0   ,						self.re_embed   ,						size=new[unknown].shape							).to(device=new.device							)
									else:
											lowercase__						=				self.unknown_index
									return new.reshape(_a							)

							def 				lowerCamelCase_			(							self:     List[Any]   ,						UpperCamelCase_:     Dict							)      ->       str:





									"""simple docstring"""







									lowercase__						=				inds.shape
									assert len(_a							) > 1
									lowercase__						=				inds.reshape(ishape[0]   ,						-1							)
									lowercase__						=				self.used.to(_a							)
									if self.re_embed > self.used.shape[0]:  # extra token
											lowercase__						=				0  # simply set to zero
									lowercase__						=				torch.gather(used[None, :][inds.shape[0] * [0], :]   ,						1   ,						_a							)
									return back.reshape(_a							)

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     str							)      ->       Optional[int]:





									"""simple docstring"""







									lowercase__						=				z.permute(0   ,						2   ,						3   ,						1							).contiguous()
									lowercase__						=				z.view(-1   ,						self.vq_embed_dim							)

									# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
									lowercase__						=				torch.argmin(torch.cdist(_a   ,						self.embedding.weight							)   ,						dim=1							)

									lowercase__						=				self.embedding(_a							).view(z.shape							)
									lowercase__						=				None
									lowercase__						=				None

									# compute loss for embedding
									if not self.legacy:
											lowercase__						=				self.beta * torch.mean((z_q.detach() - z) ** 2							) + torch.mean((z_q - z.detach()) ** 2							)
									else:
											lowercase__						=				torch.mean((z_q.detach() - z) ** 2							) + self.beta * torch.mean((z_q - z.detach()) ** 2							)

									# preserve gradients
									lowercase__						=				z + (z_q - z).detach()

									# reshape back to match original input shape
									lowercase__						=				z_q.permute(0   ,						3   ,						1   ,						2							).contiguous()

									if self.remap is not None:
											lowercase__						=				min_encoding_indices.reshape(z.shape[0]   ,						-1							)  # add batch axis
											lowercase__						=				self.remap_to_used(_a							)
											lowercase__						=				min_encoding_indices.reshape(-1   ,						1							)  # flatten

									if self.sane_index_shape:
											lowercase__						=				min_encoding_indices.reshape(z_q.shape[0]   ,						z_q.shape[2]   ,						z_q.shape[3]							)

									return z_q, loss, (perplexity, min_encodings, min_encoding_indices)


							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     Tuple							)      ->       int:





									"""simple docstring"""







									if self.remap is not None:
											lowercase__						=				indices.reshape(shape[0]   ,						-1							)  # add batch axis
											lowercase__						=				self.unmap_to_all(_a							)
											lowercase__						=				indices.reshape(-1							)  # flatten again

									# get quantized latent vectors
									lowercase__						=				self.embedding(_a							)

									if shape is not None:
											lowercase__						=				z_q.view(_a							)
											# reshape back to match original input shape
											lowercase__						=				z_q.permute(0   ,						3   ,						1   ,						2							).contiguous()

									return z_q


class 	_a							(      lowercase__      ):

							def __init__(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     List[str]=False							)      ->       int:





									"""simple docstring"""







									lowercase__						=				parameters
									lowercase__						=				torch.chunk(_a   ,						2   ,						dim=1							)
									lowercase__						=				torch.clamp(self.logvar   ,						-30.0   ,						20.0							)
									lowercase__						=				deterministic
									lowercase__						=				torch.exp(0.5 * self.logvar							)
									lowercase__						=				torch.exp(self.logvar							)
									if self.deterministic:
											lowercase__						=				torch.zeros_like(
											    self.mean   ,						device=self.parameters.device   ,						dtype=self.parameters.dtype							)

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[torch.Generator] = None							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				randn_tensor(
									    self.mean.shape   ,						generator=_a   ,						device=self.parameters.device   ,						dtype=self.parameters.dtype							)
									lowercase__						=				self.mean + self.std * sample
									return x

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     List[str]=None							)      ->       str:





									"""simple docstring"""







									if self.deterministic:
											return torch.Tensor([0.0]							)
									else:
											if other is None:
													return 0.5 * torch.sum(torch.pow(self.mean   ,						2							) + self.var - 1.0 - self.logvar   ,						dim=[1, 2, 3]							)
											else:
													return 0.5 * torch.sum(
													    torch.pow(self.mean - other.mean   ,						2							) / other.var
													    + self.var / other.var
													    - 1.0
													    - self.logvar
													    + other.logvar   ,						dim=[1, 2, 3]   ,						)

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Dict=[1, 2, 3]							)      ->       str:





									"""simple docstring"""







									if self.deterministic:
											return torch.Tensor([0.0]							)
									lowercase__						=				np.log(2.0 * np.pi							)
									return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean   ,						2							) / self.var   ,						dim=_a							)


							def 				lowerCamelCase_			(							self:     int							)      ->       Optional[Any]:





									"""simple docstring"""







									return self.mean






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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE					)
		lowercase__						=				flatten_dict(SCREAMING_SNAKE_CASE					)
		return flax_params
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				{}

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

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

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

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

						lowercase__						=				flax_dict[key]

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

		return converted_torch_dict
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		lowercase__						=				get_flax_param(SCREAMING_SNAKE_CASE					)

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

		lowercase__						=				PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE					)

		lowercase__						=				rename_and_convert_flax_params(SCREAMING_SNAKE_CASE					)
		model.load_state_dict(SCREAMING_SNAKE_CASE					)

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

		if use_large:
				lowercase__						=				40_96

		lowercase__						=				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__":
			lowerCAmelCase	=							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	=							parser.parse_args()

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


