





"""simple docstring"""





from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       torch.FloatTensor




class 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase ):

      '''simple docstring'''



      @register_to_config
      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      int = 32						,				__lowercase       :      int = 64						,				__lowercase       :      int = 20						,				__lowercase       :      int = 768						,				__lowercase       :      Optional[int]=77						,				__lowercase       :      Union[str, Any]=4						,				__lowercase       :      float = 0.0						,				__lowercase       :      str = "silu"						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[str] = "linear"						,				__lowercase       :      Optional[str] = "prd"						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				):


        '''simple docstring'''




        super().__init__()
        __UpperCAmelCase    :						int       =						num_attention_heads
        __UpperCAmelCase    :						List[Any]       =						attention_head_dim
        __UpperCAmelCase    :						int       =						num_attention_heads * attention_head_dim
        __UpperCAmelCase    :						List[str]       =						additional_embeddings

        __UpperCAmelCase    :						Optional[int]       =						time_embed_dim or inner_dim
        __UpperCAmelCase    :						Tuple       =						embedding_proj_dim or embedding_dim
        __UpperCAmelCase    :						Dict       =						clip_embed_dim or embedding_dim

        __UpperCAmelCase    :						Dict       =						Timesteps(__lowercase						,				__lowercase						,				0 )
        __UpperCAmelCase    :						List[str]       =						TimestepEmbedding(__lowercase						,				__lowercase						,				out_dim=__lowercase						,				act_fn=__lowercase )

        __UpperCAmelCase    :						Any       =						nn.Linear(__lowercase						,				__lowercase )

        if embedding_proj_norm_type is None:
          __UpperCAmelCase    :						Dict       =						None
        elif embedding_proj_norm_type == "layer":
          __UpperCAmelCase    :						Any       =						nn.LayerNorm(__lowercase )
        else:
          raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''' )

        __UpperCAmelCase    :						List[Any]       =						nn.Linear(__lowercase						,				__lowercase )

        if encoder_hid_proj_type is None:
          __UpperCAmelCase    :						int       =						None
        elif encoder_hid_proj_type == "linear":
          __UpperCAmelCase    :						int       =						nn.Linear(__lowercase						,				__lowercase )
        else:
          raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''' )

        __UpperCAmelCase    :						str       =						nn.Parameter(torch.zeros(1						,				num_embeddings + additional_embeddings						,				__lowercase ) )

        if added_emb_type == "prd":
          __UpperCAmelCase    :						Optional[int]       =						nn.Parameter(torch.zeros(1						,				1						,				__lowercase ) )
        elif added_emb_type is None:
          __UpperCAmelCase    :						str       =						None
        else:
          raise ValueError(
              f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''' )

        __UpperCAmelCase    :						Union[str, Any]       =						nn.ModuleList(
            [
                BasicTransformerBlock(
                    __lowercase						,				__lowercase						,				__lowercase						,				dropout=__lowercase						,				activation_fn='''gelu'''						,				attention_bias=__lowercase						,				)
                for d in range(__lowercase )
            ] )

        if norm_in_type == "layer":
          __UpperCAmelCase    :						Optional[Any]       =						nn.LayerNorm(__lowercase )
        elif norm_in_type is None:
          __UpperCAmelCase    :						List[Any]       =						None
        else:
          raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.''' )

        __UpperCAmelCase    :						str       =						nn.LayerNorm(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						nn.Linear(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						List[Any]       =						torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						,				-1_0_0_0_0.0 )
        causal_attention_mask.triu_(1 )
        __UpperCAmelCase    :						Optional[Any]       =						causal_attention_mask[None, ...]
        self.register_buffer('''causal_attention_mask'''						,				__lowercase						,				persistent=__lowercase )

        __UpperCAmelCase    :						Any       =						nn.Parameter(torch.zeros(1						,				__lowercase ) )
        __UpperCAmelCase    :						List[str]       =						nn.Parameter(torch.zeros(1						,				__lowercase ) )

      @property
      # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						{}

        def fn_recursive_add_processors(__lowercase       :      str						,				__lowercase       :      torch.nn.Module						,				__lowercase       :      Dict[str, AttentionProcessor] ):
          if hasattr(__lowercase						,				'''set_processor''' ):
            __UpperCAmelCase    :						Optional[Any]       =						module.processor

          for sub_name, child in module.named_children():
            fn_recursive_add_processors(f'''{name}.{sub_name}'''						,				__lowercase						,				__lowercase )

          return processors

        for name, module in self.named_children():
          fn_recursive_add_processors(__lowercase						,				__lowercase						,				__lowercase )

        return processors

      def       A_					(							self       :      Any						,				__lowercase       :      Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						len(self.attn_processors.keys() )

        if isinstance(__lowercase						,				__lowercase ) and len(__lowercase ) != count:
          raise ValueError(
              f'''A dict of processors was passed, but the number of processors {len(__lowercase )} does not match the'''
              f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )

        def fn_recursive_attn_processor(__lowercase       :      str						,				__lowercase       :      torch.nn.Module						,				__lowercase       :      int ):
          if hasattr(__lowercase						,				'''set_processor''' ):
            if not isinstance(__lowercase						,				__lowercase ):
              module.set_processor(__lowercase )
            else:
              module.set_processor(processor.pop(f'''{name}.processor''' ) )

          for sub_name, child in module.named_children():
            fn_recursive_attn_processor(f'''{name}.{sub_name}'''						,				__lowercase						,				__lowercase )

        for name, module in self.named_children():
          fn_recursive_attn_processor(__lowercase						,				__lowercase						,				__lowercase )

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        self.set_attn_processor(AttnProcessor() )

      def       A_					(							self       :      Optional[int]						,				__lowercase       :      Dict						,				__lowercase       :      Union[torch.Tensor, float, int]						,				__lowercase       :      torch.FloatTensor						,				__lowercase       :      Optional[torch.FloatTensor] = None						,				__lowercase       :      Optional[torch.BoolTensor] = None						,				__lowercase       :      bool = True						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						hidden_states.shape[0]

        __UpperCAmelCase    :						Any       =						timestep
        if not torch.is_tensor(__lowercase ):
          __UpperCAmelCase    :						Union[str, Any]       =						torch.tensor([timesteps]						,				dtype=torch.long						,				device=hidden_states.device )
        elif torch.is_tensor(__lowercase ) and len(timesteps.shape ) == 0:
          __UpperCAmelCase    :						Union[str, Any]       =						timesteps[None].to(hidden_states.device )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __UpperCAmelCase    :						List[str]       =						timesteps * torch.ones(__lowercase						,				dtype=timesteps.dtype						,				device=timesteps.device )

        __UpperCAmelCase    :						List[str]       =						self.time_proj(__lowercase )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        __UpperCAmelCase    :						Any       =						timesteps_projected.to(dtype=self.dtype )
        __UpperCAmelCase    :						List[str]       =						self.time_embedding(__lowercase )

        if self.embedding_proj_norm is not None:
          __UpperCAmelCase    :						Dict       =						self.embedding_proj_norm(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						self.embedding_proj(__lowercase )
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
          __UpperCAmelCase    :						Dict       =						self.encoder_hidden_states_proj(__lowercase )
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
          raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''' )

        __UpperCAmelCase    :						int       =						self.proj_in(__lowercase )

        __UpperCAmelCase    :						Tuple       =						self.positional_embedding.to(hidden_states.dtype )

        __UpperCAmelCase    :						Optional[Any]       =						[]
        __UpperCAmelCase    :						str       =						0

        if encoder_hidden_states is not None:
          additional_embeds.append(__lowercase )
          additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape ) == 2:
          __UpperCAmelCase    :						str       =						proj_embeddings[:, None, :]

        if len(hidden_states.shape ) == 2:
          __UpperCAmelCase    :						Optional[int]       =						hidden_states[:, None, :]

        __UpperCAmelCase    :						str       =						additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
          __UpperCAmelCase    :						Dict       =						self.prd_embedding.to(hidden_states.dtype ).expand(__lowercase						,				-1						,				-1 )
          additional_embeds.append(__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						torch.cat(
            __lowercase						,				dim=1						,				)

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        __UpperCAmelCase    :						Optional[int]       =						additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
          __UpperCAmelCase    :						List[str]       =						F.pad(
              __lowercase						,				(
                  0,
                  0,
                  additional_embeddings_len,
                  self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
              )						,				value=0.0						,				)

        __UpperCAmelCase    :						Dict       =						hidden_states + positional_embeddings

        if attention_mask is not None:
          __UpperCAmelCase    :						Optional[Any]       =						(1 - attention_mask.to(hidden_states.dtype )) * -1_0_0_0_0.0
          __UpperCAmelCase    :						Optional[Any]       =						F.pad(__lowercase						,				(0, self.additional_embeddings)						,				value=0.0 )
          __UpperCAmelCase    :						Dict       =						(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
          __UpperCAmelCase    :						Tuple       =						attention_mask.repeat_interleave(self.config.num_attention_heads						,				dim=0 )

        if self.norm_in is not None:
          __UpperCAmelCase    :						str       =						self.norm_in(__lowercase )

        for block in self.transformer_blocks:
          __UpperCAmelCase    :						Dict       =						block(__lowercase						,				attention_mask=__lowercase )

        __UpperCAmelCase    :						Any       =						self.norm_out(__lowercase )

        if self.prd_embedding is not None:
          __UpperCAmelCase    :						List[Any]       =						hidden_states[:, -1]
        else:
          __UpperCAmelCase    :						List[str]       =						hidden_states[:, additional_embeddings_len:]

        __UpperCAmelCase    :						Optional[int]       =						self.proj_to_clip_embeddings(__lowercase )

        if not return_dict:
          return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=__lowercase )







      def       A_					(							self       :      List[Any]						,				__lowercase       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						(prior_latents * self.clip_std) + self.clip_mean
        return prior_latents






"""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       lowerCamelCase_       (   UpperCAmelCase_  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						checkpoints.load_tax_checkpoint(UpperCAmelCase_  )
  __UpperCAmelCase    :						Any       =						flatten_dict(UpperCAmelCase_  )
  return flax_params


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						{}

  __UpperCAmelCase    :						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''',
  }

  __UpperCAmelCase    :						Optional[int]       =						{
      '''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
      __UpperCAmelCase    :						Optional[Any]       =						'''.'''.join(key[1:]  )

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

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

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        __UpperCAmelCase    :						Dict       =						re.sub(R'''layers_(\d+)'''  ,			R'''layer.\1'''  ,			UpperCAmelCase_  )
        __UpperCAmelCase    :						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
        __UpperCAmelCase    :						Any       =						re.sub(R'''layers_(\d+)'''  ,			R'''layer.\1'''  ,			UpperCAmelCase_  )

      __UpperCAmelCase    :						Dict       =						flax_dict[key]

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

  return converted_torch_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=False  ,			UpperCAmelCase_=False  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						get_flax_param(UpperCAmelCase_  )

  if not use_large:
    __UpperCAmelCase    :						List[str]       =						PixaStructVisionConfig()
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructTextConfig()
  else:
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructVisionConfig(
        hidden_size=15_36  ,			d_ff=39_68  ,			num_attention_heads=24  ,			num_hidden_layers=18  )
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructTextConfig(hidden_size=15_36  ,			d_ff=39_68  ,			num_heads=24  ,			num_layers=18  )
  __UpperCAmelCase    :						Dict       =						PixaStructConfig(
      vision_config=encoder_config.to_dict()  ,			text_config=decoder_config.to_dict()  ,			is_vqa=UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						PixaStructForConditionalGeneration(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						rename_and_convert_flax_params(UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

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

  if use_large:
    __UpperCAmelCase    :						str       =						40_96

  __UpperCAmelCase    :						str       =						True

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

  model.save_pretrained(UpperCAmelCase_  )
  processor.save_pretrained(UpperCAmelCase_  )

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


if __name__ == "__main__":
  lowercase__				:List[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__				:Optional[Any]				   =       parser.parse_args()

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