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







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=False       )     ->Any:
  if split_mlp_wi:
    a__:					List[Any]      							=     params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
    a__:					Optional[Any]      							=     params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
    a__:					Union[str, Any]      							=     (wi_a, wi_a)
  else:
    a__:					Dict      							=     params[F'{prefix}/layers_{i}/mlp/wi/kernel']

  a__:					Optional[Any]      							=     params[F'{prefix}/layers_{i}/mlp/wo/kernel']
  return wi, wo







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[Any]:
  return params[F'{prefix}/layers_{i}/{layer_name}/scale']







def 						__a       (     _SCREAMING_SNAKE_CASE		,					*, _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  a__:					Any      							=     traverse_util.flatten_dict(variables['target']       )
  a__:					Optional[Any]      							=     {'/'.join(_SCREAMING_SNAKE_CASE       ): v for k, v in old.items()}

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

  a__:					int      							=     collections.OrderedDict()

  # Shared embeddings.
  a__:					Tuple      							=     old['token_embedder/embedding']

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

    # Block i, layer 1 (MLP).
    a__:					Union[str, Any]      							=     tax_layer_norm_lookup(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					'encoder'		,					'pre_mlp_layer_norm'       )
    a__							,   a__:					Tuple      							=     tax_mlp_lookup(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					'encoder'		,					_SCREAMING_SNAKE_CASE       )
    a__:					Optional[Any]      							=     layer_norm
    if split_mlp_wi:
      a__:					Optional[Any]      							=     wi[0].T
      a__:					Any      							=     wi[1].T
    else:
      a__:					Optional[Any]      							=     wi.T
    a__:					Union[str, Any]      							=     wo.T

  a__:					Tuple      							=     old[
      'encoder/relpos_bias/rel_embedding'
  ].T
  a__:					Tuple      							=     old['encoder/encoder_norm/scale']

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

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

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

    a__:					List[Any]      							=     old['decoder/decoder_norm/scale']
    a__:					Dict      							=     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__:					Optional[Any]      							=     old['decoder/logits_dense/kernel'].T

  return new







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[int]:
  a__:					Union[str, Any]      							=     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__:					Any      							=     state_dict['shared.weight']

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      a__:					int      							=     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__:					Tuple      							=     state_dict['shared.weight']

  return state_dict







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->int:
  a__:					Any      							=     checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE       )
  a__:					Union[str, Any]      							=     convert_tax_to_pytorch(_SCREAMING_SNAKE_CASE		,					num_layers=config.num_layers		,					is_encoder_only=_SCREAMING_SNAKE_CASE       )
  a__:					List[str]      							=     make_state_dict(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  model.load_state_dict(_SCREAMING_SNAKE_CASE		,					strict=_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE = False       )     ->List[str]:
  a__:					Union[str, Any]      							=     TaConfig.from_json_file(_SCREAMING_SNAKE_CASE       )
  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__:					List[Any]      							=     TaEncoderModel(_SCREAMING_SNAKE_CASE       )
  else:
    a__:					Any      							=     TaForConditionalGeneration(_SCREAMING_SNAKE_CASE       )

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

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

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


if __name__ == "__main__":
 lowercase__ 	=     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
 )
 lowercase__ 	=     parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
 )



"""simple docstring"""
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        __snake_case	(					unittest.TestCase    ):



      @property
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					str      							=     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



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					List[Any]      							=     VQModel(
            block_out_channels=[32, 64]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,      latent_channels=3   ,      )
        return model



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





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					Optional[int]      							=     CLIPTextConfig(
            bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=32   ,      intermediate_size=37   ,      layer_norm_eps=1e-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=10_00   ,      )
        return CLIPTextModel(lowercase)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.dummy_uncond_unet
        a__:					Optional[int]      							=     DDIMScheduler()
        a__:					Optional[int]      							=     self.dummy_vq_model

        a__:					Union[str, Any]      							=     LDMPipeline(unet=lowercase   ,      vqvae=lowercase   ,      scheduler=lowercase)
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					str      							=     torch.manual_seed(0)
        a__:					Dict      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy').images

        a__:					Union[str, Any]      							=     torch.manual_seed(0)
        a__:					int      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy'   ,      return_dict=lowercase)[0]

        a__:					Union[str, Any]      							=     image[0, -3:, -3:, -1]
        a__:					int      							=     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__:					int      							=     np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
        a__:					Optional[Any]      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance







@slow
@require_torch
class        __snake_case	(					unittest.TestCase    ):



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





        '''simple docstring'''





        a__:					Union[str, Any]      							=     LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256')
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					List[str]      							=     torch.manual_seed(0)
        a__:					Optional[int]      							=     ldm(generator=lowercase   ,      num_inference_steps=5   ,      output_type='numpy').images

        a__:					Optional[int]      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        a__:					int      							=     np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
        a__:					Any      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance


