




'''simple docstring'''

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class 		SCREAMING_SNAKE_CASE__  :
			__SCREAMING_SNAKE_CASE    =			42  # [batch_size x 3]
			__SCREAMING_SNAKE_CASE    =			42  # [batch_size x 3]
			__SCREAMING_SNAKE_CASE    =			42  # [batch_size x 3]
			__SCREAMING_SNAKE_CASE    =			42  # [batch_size x 3]
			__SCREAMING_SNAKE_CASE    =			42
			__SCREAMING_SNAKE_CASE    =			42
			__SCREAMING_SNAKE_CASE    =			42
			__SCREAMING_SNAKE_CASE    =			42
			__SCREAMING_SNAKE_CASE    =			42





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
						assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
						assert len(self.x.shape							) == len(self.y.shape							) == len(self.z.shape							) == len(self.origin.shape							) == 2





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						return torch.from_numpy(np.array([self.width, self.height]      ,		dtype=np.floataa							)							)





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						return torch.from_numpy(np.array([self.x_fov, self.y_fov]      ,		dtype=np.floataa							)							)





			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		torch.arange(self.height * self.width							)
						__snake_case			=		torch.stack(
						    [
						        pixel_indices % self.width,
						        torch.div(a_      ,		self.width      ,		rounding_mode="trunc"							),
						    ]      ,		axis=1      ,		)
						return coords





			@property
			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						__snake_case		,     *__snake_case			=		self.shape
						__snake_case			=		int(np.prod(a_							)							)

						__snake_case			=		self.get_image_coords()
						__snake_case			=		torch.broadcast_to(coords.unsqueeze(0							)      ,		[batch_size * inner_batch_size, *coords.shape]							)
						__snake_case			=		self.get_camera_rays(a_							)

						__snake_case			=		rays.view(a_      ,		inner_batch_size * self.height * self.width      ,		2      ,		3							)

						return rays





			def 							A (      self						:    Optional[int]      ,		a_						:    torch.Tensor							):



						"""simple docstring"""




						__snake_case		,     *__snake_case		,     __snake_case			=		coords.shape
						assert n_coords == 2
						assert batch_size == self.origin.shape[0]

						__snake_case			=		coords.view(a_      ,		-1      ,		2							)

						__snake_case			=		self.resolution()
						__snake_case			=		self.fov()

						__snake_case			=		(flat.float() / (res - 1)) * 2 - 1
						__snake_case			=		fracs * torch.tan(fov / 2							)

						__snake_case			=		fracs.view(a_      ,		-1      ,		2							)
						__snake_case			=		(
						    self.z.view(a_      ,		1      ,		3							)
						    + self.x.view(a_      ,		1      ,		3							) * fracs[:, :, :1]
						    + self.y.view(a_      ,		1      ,		3							) * fracs[:, :, 1:]
						)
						__snake_case			=		directions / directions.norm(dim=-1      ,		keepdim=a_							)
						__snake_case			=		torch.stack(
						    [
						        torch.broadcast_to(self.origin.view(a_      ,		1      ,		3							)      ,		[batch_size, directions.shape[1], 3]							),
						        directions,
						    ]      ,		dim=2      ,		)
						return rays.view(a_      ,		*a_      ,		2      ,		3							)






			def 							A (      self						:    List[str]      ,		a_						:    int      ,		a_						:    int							):



						"""simple docstring"""




						assert width * self.height == height * self.width, "The aspect ratio should not change."
						return DifferentiableProjectiveCamera(
						    origin=self.origin      ,		x=self.x      ,		y=self.y      ,		z=self.z      ,		width=a_      ,		height=a_      ,		x_fov=self.x_fov      ,		y_fov=self.y_fov      ,		)







def __UpperCAmelCase	(   _UpperCAmelCase		:   int						)      ->      DifferentiableProjectiveCamera:
			__snake_case			=		[]
			__snake_case			=		[]
			__snake_case			=		[]
			__snake_case			=		[]
			for theta in np.linspace(0		,				2 * np.pi		,				num=20						):
						__snake_case			=		np.array([np.sin(_UpperCAmelCase						), np.cos(_UpperCAmelCase						), -0.5]						)
						z /= np.sqrt(np.sum(z**2						)						)
						__snake_case			=		-z * 4
						__snake_case			=		np.array([np.cos(_UpperCAmelCase						), -np.sin(_UpperCAmelCase						), 0.0]						)
						__snake_case			=		np.cross(_UpperCAmelCase		,				_UpperCAmelCase						)
						origins.append(_UpperCAmelCase						)
						xs.append(_UpperCAmelCase						)
						ys.append(_UpperCAmelCase						)
						zs.append(_UpperCAmelCase						)
			return DifferentiableProjectiveCamera(
			    origin=torch.from_numpy(np.stack(_UpperCAmelCase		,				axis=0						)						).float()		,				x=torch.from_numpy(np.stack(_UpperCAmelCase		,				axis=0						)						).float()		,				y=torch.from_numpy(np.stack(_UpperCAmelCase		,				axis=0						)						).float()		,				z=torch.from_numpy(np.stack(_UpperCAmelCase		,				axis=0						)						).float()		,				width=_UpperCAmelCase		,				height=_UpperCAmelCase		,				x_fov=0.7		,				y_fov=0.7		,				shape=(1, len(_UpperCAmelCase						))		,				)








import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def      A__					(			__A  :						List[str]     ,				__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						int="attention"			)					->str:
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v





def      A__					(			__A  :						Optional[int]     ,				__A  :						List[str]     ,				__A  :						Any     ,				__A  :						Tuple=False			)					->Any:
      if split_mlp_wi:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            __A    =(wi_a, wi_a)
      else:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      __A    =params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Any     ,				__A  :						Optional[Any]			)					->str:
      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def      A__					(			__A  :						dict     ,				*, __A  :						int     ,				__A  :						bool			)					->Optional[Any]:
      __A    =traverse_util.flatten_dict(variables['''target''']			)
      __A    ={'''/'''.join(__A			): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      __A    ='''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''     ,				__A			)

      __A    =collections.OrderedDict()

      # Shared embeddings.
      __A    =old['''token_embedder/embedding''']

      # Encoder.
      for i in range(__A			):
            # Block i, layer 0 (Self Attention).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_attention_layer_norm'''			)
            __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''attention'''			)
            __A    =layer_norm
            __A    =k.T
            __A    =o.T
            __A    =q.T
            __A    =v.T

            # Block i, layer 1 (MLP).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_mlp_layer_norm'''			)
            __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''encoder'''     ,				__A			)
            __A    =layer_norm
            if split_mlp_wi:
                  __A    =wi[0].T
                  __A    =wi[1].T
            else:
                  __A    =wi.T
            __A    =wo.T

      __A    =old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      __A    =old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(__A			):
                  # Block i, layer 0 (Self Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_self_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''self_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 1 (Cross Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_cross_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''encoder_decoder_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 2 (MLP).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_mlp_layer_norm'''			)
                  __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''decoder'''     ,				__A			)
                  __A    =layer_norm
                  if split_mlp_wi:
                        __A    =wi[0].T
                        __A    =wi[1].T
                  else:
                        __A    =wi.T
                  __A    =wo.T

            __A    =old['''decoder/decoder_norm/scale''']
            __A    =old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  __A    =old['''decoder/logits_dense/kernel'''].T

      return new





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						bool			)					->Any:
      __A    =collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            __A    =state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  __A    =state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.'''			)
                  __A    =state_dict['''shared.weight''']

      return state_dict





def      A__					(			__A  :						str     ,				__A  :						Optional[int]     ,				__A  :						int     ,				__A  :						Optional[Any]			)					->Tuple:
      __A    =checkpoints.load_tax_checkpoint(__A			)
      __A    =convert_tax_to_pytorch(__A     ,				num_layers=config.num_layers     ,				is_encoder_only=__A			)
      __A    =make_state_dict(__A     ,				__A			)
      model.load_state_dict(__A     ,				strict=__A			)





def      A__					(			__A  :						List[str]     ,				__A  :						str     ,				__A  :						str     ,				__A  :						bool = False			)					->List[str]:
      __A    =TaConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            __A    =TaEncoderModel(__A			)
      else:
            __A    =TaForConditionalGeneration(__A			)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(__A     ,				__A     ,				__A     ,				__A			)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
      model.save_pretrained(__A			)

      # Verify that we can load the checkpoint.
      model.from_pretrained(__A			)
      print('''Done'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
   # Required parameters
   parser.add_argument(
       '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
   )
   _lowerCamelCase      :					int					    =      parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )


