
"""simple docstring"""




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        _lowerCamelCase ( __a,							__a,							__a    ):
  return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]



def        _lowerCamelCase ( __a,							__a,							__a,							__a="attention"    ):
  SCREAMING_SNAKE_CASE_						 = SCREAMING_SNAKE_CASE_						 = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]    )
  SCREAMING_SNAKE_CASE_						 = k_tmp.reshape(k_tmp.shape[0],							k_tmp.shape[1] * k_tmp.shape[2]    )
  SCREAMING_SNAKE_CASE_						 = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]    )
  SCREAMING_SNAKE_CASE_						 = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1],							o_tmp.shape[2]    )
  SCREAMING_SNAKE_CASE_						 = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]    )
  SCREAMING_SNAKE_CASE_						 = q_tmp.reshape(q_tmp.shape[0],							q_tmp.shape[1] * q_tmp.shape[2]    )
  SCREAMING_SNAKE_CASE_						 = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]    )
  SCREAMING_SNAKE_CASE_						 = v_tmp.reshape(v_tmp.shape[0],							v_tmp.shape[1] * v_tmp.shape[2]    )
  return k, o, q, v



def        _lowerCamelCase ( __a,							__a,							__a,							__a=False    ):






  if split_mlp_wi:
    SCREAMING_SNAKE_CASE_						 = params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
    SCREAMING_SNAKE_CASE_						 = params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
    SCREAMING_SNAKE_CASE_						 = (wi_a, wi_a)
  else:
    SCREAMING_SNAKE_CASE_						 = params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

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



def        _lowerCamelCase ( __a,							__a,							__a,							__a    ):
  return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def        _lowerCamelCase ( __a,							*, __a,							__a,							__a = False    ):
  SCREAMING_SNAKE_CASE_						 = traverse_util.flatten_dict(variables['''target''']    )
  SCREAMING_SNAKE_CASE_						 = {'''/'''.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
  SCREAMING_SNAKE_CASE_						 = '''encoder/encoder/mlp/wi_0/kernel''' in old
  print('''Split MLP:''',							a__    )

  SCREAMING_SNAKE_CASE_						 = collections.OrderedDict()

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

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

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

  SCREAMING_SNAKE_CASE_						 = old['''encoder/encoder_norm/scale''']

  if not scalable_attention:
    SCREAMING_SNAKE_CASE_						 = tax_relpos_bias_lookup(
        a__,							0,							'''encoder'''    ).T
    SCREAMING_SNAKE_CASE_						 = tax_relpos_bias_lookup(
        a__,							0,							'''decoder'''    ).T

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

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

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

      if scalable_attention:
        # convert the rel_embedding of each layer
        SCREAMING_SNAKE_CASE_						 = tax_relpos_bias_lookup(a__,							a__,							'''decoder'''    ).T

    SCREAMING_SNAKE_CASE_						 = 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:
      SCREAMING_SNAKE_CASE_						 = old['''decoder/logits_dense/kernel'''].T

  return new



def        _lowerCamelCase ( __a,							__a    ):
  SCREAMING_SNAKE_CASE_						 = 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:
    SCREAMING_SNAKE_CASE_						 = state_dict['''shared.weight''']

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

  return state_dict



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a    ):
  SCREAMING_SNAKE_CASE_						 = checkpoints.load_tax_checkpoint(a__    )
  SCREAMING_SNAKE_CASE_						 = convert_tax_to_pytorch(
      a__,							num_layers=config.num_layers,							is_encoder_only=a__,							scalable_attention=a__    )
  SCREAMING_SNAKE_CASE_						 = make_state_dict(a__,							a__    )
  model.load_state_dict(a__,							strict=a__    )



def        _lowerCamelCase ( __a,							__a,							__a,							__a = False,							__a = False,							):
  SCREAMING_SNAKE_CASE_						 = MTaConfig.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:
    SCREAMING_SNAKE_CASE_						 = UMTaEncoderModel(a__    )
  else:
    SCREAMING_SNAKE_CASE_						 = UMTaForConditionalGeneration(a__    )

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(a__,							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__							=				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,
      )
      lowerCAmelCase__							=				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,
      )

"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      (DDIMParallelScheduler,)
							UpperCAmelCase__						     =      (('''eta''', 0.0), ('''num_inference_steps''', 50))

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {
									    '''num_train_timesteps''': 10_00,
									    '''beta_start''': 0.00_01,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''clip_sample''': True,
									}

									config.update(**SCREAMING_SNAKE_CASE_    )
									return config

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter

									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

									return sample

							def _lowercase					(self    ):

									"""simple docstring"""







									for timesteps in [1_00, 5_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(steps_offset=1    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps       ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,       [0.0_02, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,       beta_end=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_    )
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=SCREAMING_SNAKE_CASE_       ,       prediction_type=SCREAMING_SNAKE_CASE_       ,       sample_max_value=SCREAMING_SNAKE_CASE_       ,       )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t in [1, 10, 49]:
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, num_inference_steps in zip([1, 10, 50]       ,       [10, 50, 5_00]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, eta in zip([1, 10, 49]       ,       [0.0, 0.5, 1.0]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       eta=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_20       ,       4_00    ) - 0.1_47_71    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_80       ,       9_60    ) - 0.3_24_60    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87       ,       4_86    ) - 0.0_09_79    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99       ,       9_98    ) - 0.02    )    ) < 1e-5

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter + 0.1
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter - 0.1

									SCREAMING_SNAKE_CASE_						 = samplea.shape[0]
									SCREAMING_SNAKE_CASE_						 = torch.stack([samplea, samplea, samplea]       ,       dim=0    )
									SCREAMING_SNAKE_CASE_						 = torch.arange(SCREAMING_SNAKE_CASE_    )[0:3, None].repeat(1       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = model(samples.flatten(0       ,       1    )       ,       timesteps.flatten(0       ,       1    )    )
									SCREAMING_SNAKE_CASE_						 = scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_       ,       timesteps.flatten(0       ,       1    )       ,       samples.flatten(0       ,       1    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
									assert abs(result_mean.item() - 0.49_82    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop()

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
									assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(prediction_type='''v_prediction'''    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 52.53_02    ) < 1e-2
									assert abs(result_mean.item() - 0.06_84    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
									assert abs(result_mean.item() - 0.19_51    ) < 1e-3







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
									assert abs(result_mean.item() - 0.19_41    ) < 1e-3