
"""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     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					List[str] ) ->      Optional[Any]:






    """simple docstring"""




    return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]




def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					str="attention" ) ->      str:






    """simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					str=False ) ->      int:






    """simple docstring"""




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

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




def     lowercase__						(					lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Any ) ->      int:






    """simple docstring"""




    return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]




def     lowercase__						(					lowerCAmelCase				:					dict ,				*, lowerCAmelCase				:					int ,				lowerCAmelCase				:					bool ,				lowerCAmelCase				:					bool = False ) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					traverse_util.flatten_dict(variables['target'] )
    UpperCAmelCase									=					{'/'.join(lowerCAmelCase ): v for k, v in old.items()}

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

    UpperCAmelCase									=					collections.OrderedDict()

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

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

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

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

    if not scalable_attention:
        UpperCAmelCase									=					tax_relpos_bias_lookup(
            lowerCAmelCase ,				0 ,				'encoder' ).T
        UpperCAmelCase									=					tax_relpos_bias_lookup(
            lowerCAmelCase ,				0 ,				'decoder' ).T

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

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

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

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

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

    return new




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					bool ) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase									=					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:
        UpperCAmelCase									=					state_dict['shared.weight']

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

    return state_dict




def     lowercase__						(					lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					int ) ->      Dict:






    """simple docstring"""




    UpperCAmelCase									=					checkpoints.load_tax_checkpoint(lowerCAmelCase )
    UpperCAmelCase									=					convert_tax_to_pytorch(
        lowerCAmelCase ,				num_layers=config.num_layers ,				is_encoder_only=lowerCAmelCase ,				scalable_attention=lowerCAmelCase )
    UpperCAmelCase									=					make_state_dict(lowerCAmelCase ,				lowerCAmelCase )
    model.load_state_dict(lowerCAmelCase ,				strict=lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					int ,				lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					bool = False ,				lowerCAmelCase				:					bool = False ,				) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					MTaConfig.from_json_file(lowerCAmelCase )
    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:
        UpperCAmelCase									=					UMTaEncoderModel(lowerCAmelCase )
    else:
        UpperCAmelCase									=					UMTaForConditionalGeneration(lowerCAmelCase )

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

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

    # Verify that we can load the checkpoint.
    model.from_pretrained(lowerCAmelCase )
    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,
   )





"""simple docstring"""

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							str							      =	(DDPMScheduler,)




      def     a_      (							self   ,       **lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					{
              'num_train_timesteps': 1_0_0_0,
              'beta_start': 0.0_0_0_1,
              'beta_end': 0.0_2,
              'beta_schedule': 'linear',
              'variance_type': 'fixed_small',
              'clip_sample': True,
          }

          config.update(**lowercase_							)
          return config




      def     a_      (							self							)						->					int:
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
              self.check_over_configs(beta_start=lowercase_   ,       beta_end=lowercase_							)




      def     a_      (							self							)						->					Dict:
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase_							)




      def     a_      (							self							)						->					Union[str, Any]:
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase_							)




      def     a_      (							self							)						->					Tuple:
          self.check_over_configs(thresholding=lowercase_							)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=lowercase_   ,       prediction_type=lowercase_   ,       sample_max_value=lowercase_   ,       )




      def     a_      (							self							)						->					Any:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase_							)




      def     a_      (							self							)						->					Union[str, Any]:
          for t in [0, 5_0_0, 9_9_9]:
              self.check_over_forward(time_step=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.0_2							)							) < 1E-5




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              UpperCAmelCase									=					pred_prev_sample

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config(prediction_type='v_prediction'							)
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              UpperCAmelCase									=					pred_prev_sample

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]

          scheduler.set_timesteps(timesteps=lowercase_							)

          UpperCAmelCase									=					scheduler.timesteps

          for i, timestep in enumerate(lowercase_							):
              if i == len(lowercase_							) - 1:
                  UpperCAmelCase									=					-1
              else:
                  UpperCAmelCase									=					timesteps[i + 1]

              UpperCAmelCase									=					scheduler.previous_timestep(lowercase_							)
              UpperCAmelCase									=					prev_t.item()

              self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 5_1, 0]

          with self.assertRaises(lowercase_   ,       msg='`custom_timesteps` must be in descending order.'							):
              scheduler.set_timesteps(timesteps=lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]
          UpperCAmelCase									=					len(lowercase_							)

          with self.assertRaises(lowercase_   ,       msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'							):
              scheduler.set_timesteps(num_inference_steps=lowercase_   ,       timesteps=lowercase_							)




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              lowercase_   ,       msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'   ,       ):
              scheduler.set_timesteps(timesteps=lowercase_							)



