





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
 return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a="attention"   ):
 snake_case_		:			List[str]											=	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]   )
 snake_case_		:			List[Any]											=	k_tmp.reshape(k_tmp.shape[0]   , k_tmp.shape[1] * k_tmp.shape[2]   )
 snake_case_		:			Any											=	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]   )
 snake_case_		:			List[Any]											=	o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]   , o_tmp.shape[2]   )
 snake_case_		:			Union[str, Any]											=	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]   )
 snake_case_		:			List[str]											=	q_tmp.reshape(q_tmp.shape[0]   , q_tmp.shape[1] * q_tmp.shape[2]   )
 snake_case_		:			List[str]											=	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]   )
 snake_case_		:			str											=	v_tmp.reshape(v_tmp.shape[0]   , v_tmp.shape[1] * v_tmp.shape[2]   )
 return k, o, q, v


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a=False   ):
 if split_mlp_wi:
  snake_case_		:			List[Any]											=	params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
  snake_case_		:			Tuple											=	params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
  snake_case_		:			str											=	(wi_a, wi_a)
 else:
  snake_case_		:			str											=	params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

 snake_case_		:			List[str]											=	params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
 return wi, wo


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
 return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]


def 							SCREAMING_SNAKE_CASE__						(    __a   , *, __a   , __a   , __a = False   ):
 snake_case_		:			int											=	traverse_util.flatten_dict(variables['target']   )
 snake_case_		:			Dict											=	{"""/""".join(UpperCAmelCase__   ): v for k, v in old.items()}

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

 snake_case_		:			List[Any]											=	collections.OrderedDict()

 # Shared embeddings.
 snake_case_		:			Tuple											=	old["""token_embedder/embedding"""]

 # Encoder.
 for i in range(UpperCAmelCase__   ):
  # Block i, layer 0 (Self Attention).
  snake_case_		:			List[str]											=	tax_layer_norm_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'encoder'   , 'pre_attention_layer_norm'   )
  snake_case_		:			int											=	tax_attention_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'encoder'   , 'attention'   )
  snake_case_		:			Optional[Any]											=	layer_norm
  snake_case_		:			Any											=	k.T
  snake_case_		:			str											=	o.T
  snake_case_		:			Union[str, Any]											=	q.T
  snake_case_		:			Optional[Any]											=	v.T

  # Block i, layer 1 (MLP).
  snake_case_		:			Any											=	tax_layer_norm_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'encoder'   , 'pre_mlp_layer_norm'   )
  snake_case_		:			Dict											=	tax_mlp_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'encoder'   , UpperCAmelCase__   )
  snake_case_		:			Tuple											=	layer_norm
  if split_mlp_wi:
   snake_case_		:			Tuple											=	wi[0].T
   snake_case_		:			Optional[Any]											=	wi[1].T
  else:
   snake_case_		:			Optional[Any]											=	wi.T
  snake_case_		:			Any											=	wo.T
  if scalable_attention:
   # convert the rel_embedding of each layer
   snake_case_		:			Dict											=	tax_relpos_bias_lookup(
       UpperCAmelCase__   , UpperCAmelCase__   , 'encoder'   ).T

 snake_case_		:			Dict											=	old["""encoder/encoder_norm/scale"""]

 if not scalable_attention:
  snake_case_		:			Dict											=	tax_relpos_bias_lookup(
      UpperCAmelCase__   , 0   , 'encoder'   ).T
  snake_case_		:			List[Any]											=	tax_relpos_bias_lookup(
      UpperCAmelCase__   , 0   , 'decoder'   ).T

 if not is_encoder_only:
  # Decoder.
  for i in range(UpperCAmelCase__   ):
   # Block i, layer 0 (Self Attention).
   snake_case_		:			List[Any]											=	tax_layer_norm_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'decoder'   , 'pre_self_attention_layer_norm'   )
   snake_case_		:			str											=	tax_attention_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'decoder'   , 'self_attention'   )
   snake_case_		:			Tuple											=	layer_norm
   snake_case_		:			Optional[int]											=	k.T
   snake_case_		:			str											=	o.T
   snake_case_		:			List[Any]											=	q.T
   snake_case_		:			List[str]											=	v.T

   # Block i, layer 1 (Cross Attention).
   snake_case_		:			Tuple											=	tax_layer_norm_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'decoder'   , 'pre_cross_attention_layer_norm'   )
   snake_case_		:			Union[str, Any]											=	tax_attention_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'decoder'   , 'encoder_decoder_attention'   )
   snake_case_		:			Union[str, Any]											=	layer_norm
   snake_case_		:			Union[str, Any]											=	k.T
   snake_case_		:			str											=	o.T
   snake_case_		:			Dict											=	q.T
   snake_case_		:			Union[str, Any]											=	v.T

   # Block i, layer 2 (MLP).
   snake_case_		:			Any											=	tax_layer_norm_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'decoder'   , 'pre_mlp_layer_norm'   )
   snake_case_		:			Tuple											=	tax_mlp_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'decoder'   , UpperCAmelCase__   )
   snake_case_		:			Optional[int]											=	layer_norm
   if split_mlp_wi:
    snake_case_		:			Tuple											=	wi[0].T
    snake_case_		:			Optional[Any]											=	wi[1].T
   else:
    snake_case_		:			Any											=	wi.T
   snake_case_		:			str											=	wo.T

   if scalable_attention:
    # convert the rel_embedding of each layer
    snake_case_		:			Tuple											=	tax_relpos_bias_lookup(UpperCAmelCase__   , UpperCAmelCase__   , 'decoder'   ).T

  snake_case_		:			List[Any]											=	old["""decoder/decoder_norm/scale"""]

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   snake_case_		:			Tuple											=	old["""decoder/logits_dense/kernel"""].T

 return new


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
 snake_case_		:			Dict											=	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:
  snake_case_		:			Tuple											=	state_dict["""shared.weight"""]

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   snake_case_		:			Union[str, Any]											=	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.'   )
   snake_case_		:			Any											=	state_dict["""shared.weight"""]

 return state_dict


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   ):
 snake_case_		:			Union[str, Any]											=	checkpoints.load_tax_checkpoint(UpperCAmelCase__   )
 snake_case_		:			Tuple											=	convert_tax_to_pytorch(
     UpperCAmelCase__   , num_layers=config.num_layers   , is_encoder_only=UpperCAmelCase__   , scalable_attention=UpperCAmelCase__   )
 snake_case_		:			int											=	make_state_dict(UpperCAmelCase__   , UpperCAmelCase__   )
 model.load_state_dict(UpperCAmelCase__   , strict=UpperCAmelCase__   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a = False   , __a = False   , ):
 snake_case_		:			Any											=	MTaConfig.from_json_file(UpperCAmelCase__   )
 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:
  snake_case_		:			Any											=	UMTaEncoderModel(UpperCAmelCase__   )
 else:
  snake_case_		:			Any											=	UMTaForConditionalGeneration(UpperCAmelCase__   )

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

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
 model.save_pretrained(UpperCAmelCase__   )

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


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE      	=		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
      )
      parser.add_argument(
          """--scalable_attention""",
          action="""store_true""",
          help="""Whether the model uses scaled attention (umt5 model)""",
          default=False,
      )
      _SCREAMING_SNAKE_CASE      	=		parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path,
          args.config_file,
          args.pytorch_dump_path,
          args.is_encoder_only,
          args.scalable_attention,
      )








import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						@property
						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			List[Any]											=	UNetaDModel(
							    block_out_channels=(32, 64)		,   layers_per_block=2		,   sample_size=32		,   in_channels=3		,   out_channels=3		,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,   )
							return model






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.dummy_uncond_unet
							snake_case_		:			Optional[Any]											=	ScoreSdeVeScheduler()

							snake_case_		:			Tuple											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
							snake_case_		:			Tuple											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			int											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A		,   return_dict=_A	)[
							    0
							]

							snake_case_		:			List[str]											=	image[0, -3:, -3:, -1]
							snake_case_		:			Dict											=	image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:			List[Any]											=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Dict	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	'google/ncsnpp-church-256'
							snake_case_		:			List[Any]											=	UNetaDModel.from_pretrained(_A	)

							snake_case_		:			str											=	ScoreSdeVeScheduler.from_pretrained(_A	)

							snake_case_		:			Optional[Any]											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Any											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=10		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							snake_case_		:			Dict											=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

