import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Union[str, Any]						=(DEISMultistepScheduler,)
  SCREAMING_SNAKE_CASE_	:	List[str]						=(("num_inference_steps", 25),)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''solver_order''': 2,
       }

       config.update(**SCREAMING_SNAKE_CASE_	)
       return config






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_=0   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      dict(self.forward_default_kwargs	)
       UpperCamelCase__										=      kwargs.pop('''num_inference_steps'''   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.dummy_sample
       UpperCamelCase__										=      0.1 * sample
       UpperCamelCase__										=      [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__										=      self.get_scheduler_config(**SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)
            scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
            # copy over dummy past residuals
            UpperCamelCase__										=      dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_	)
                 new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
                 # copy over dummy past residuals
                 UpperCamelCase__										=      dummy_past_residuals[: new_scheduler.config.solver_order]

            UpperCamelCase__						,       UpperCamelCase__										=      sample, sample
            for t in range(SCREAMING_SNAKE_CASE_   ,				time_step + scheduler.config.solver_order + 1	):
                 UpperCamelCase__										=      scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	).prev_sample
                 UpperCamelCase__										=      new_scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	).prev_sample

                 assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       pass






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_=0   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      dict(self.forward_default_kwargs	)
       UpperCamelCase__										=      kwargs.pop('''num_inference_steps'''   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.dummy_sample
       UpperCamelCase__										=      0.1 * sample
       UpperCamelCase__										=      [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__										=      self.get_scheduler_config()
            UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)
            scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

            # copy over dummy past residuals (must be after setting timesteps)
            UpperCamelCase__										=      dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_	)
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

                 # copy over dummy past residual (must be after setting timesteps)
                 UpperCamelCase__										=      dummy_past_residuals[: new_scheduler.config.solver_order]

            UpperCamelCase__										=      scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	).prev_sample
            UpperCamelCase__										=      new_scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	).prev_sample

            assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       if scheduler is None:
            UpperCamelCase__										=      self.scheduler_classes[0]
            UpperCamelCase__										=      self.get_scheduler_config(**SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)

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

       UpperCamelCase__										=      10
       UpperCamelCase__										=      self.dummy_model()
       UpperCamelCase__										=      self.dummy_sample_deter
       scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

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

       return sample






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      dict(self.forward_default_kwargs	)

       UpperCamelCase__										=      kwargs.pop('''num_inference_steps'''   ,				SCREAMING_SNAKE_CASE_	)

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__										=      self.get_scheduler_config()
            UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      self.dummy_sample
            UpperCamelCase__										=      0.1 * sample

            if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE_   ,				'''set_timesteps'''	):
                 scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
            elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE_   ,				'''set_timesteps'''	):
                 UpperCamelCase__										=      num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            UpperCamelCase__										=      [residual + 0.2, residual + 0.15, residual + 0.10]
            UpperCamelCase__										=      dummy_past_residuals[: scheduler.config.solver_order]

            UpperCamelCase__										=      scheduler.timesteps[5]
            UpperCamelCase__										=      scheduler.timesteps[6]

            UpperCamelCase__										=      scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	).prev_sample
            UpperCamelCase__										=      scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	).prev_sample

            self.assertEqual(output_a.shape   ,				sample.shape	)
            self.assertEqual(output_a.shape   ,				output_a.shape	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      DEISMultistepScheduler(**self.get_scheduler_config()	)
       UpperCamelCase__										=      self.full_loop(scheduler=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_mean.item() - 0.23916	) < 1E-3

       UpperCamelCase__										=      DPMSolverSinglestepScheduler.from_config(scheduler.config	)
       UpperCamelCase__										=      DPMSolverMultistepScheduler.from_config(scheduler.config	)
       UpperCamelCase__										=      UniPCMultistepScheduler.from_config(scheduler.config	)
       UpperCamelCase__										=      DEISMultistepScheduler.from_config(scheduler.config	)

       UpperCamelCase__										=      self.full_loop(scheduler=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_mean.item() - 0.23916	) < 1E-3






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_	)
       for order in [1, 2, 3]:
            for solver_type in ["logrho"]:
                 for threshold in [0.5, 1.0, 2.0]:
                      for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               thresholding=SCREAMING_SNAKE_CASE_   ,				prediction_type=SCREAMING_SNAKE_CASE_   ,				sample_max_value=SCREAMING_SNAKE_CASE_   ,				algorithm_type='''deis'''   ,				solver_order=SCREAMING_SNAKE_CASE_   ,				solver_type=SCREAMING_SNAKE_CASE_   ,				)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





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






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





       '''simple docstring'''





       for algorithm_type in ["deis"]:
            for solver_type in ["logrho"]:
                 for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               solver_order=SCREAMING_SNAKE_CASE_   ,				solver_type=SCREAMING_SNAKE_CASE_   ,				prediction_type=SCREAMING_SNAKE_CASE_   ,				algorithm_type=SCREAMING_SNAKE_CASE_   ,				)
                           UpperCamelCase__										=      self.full_loop(
                               solver_order=SCREAMING_SNAKE_CASE_   ,				solver_type=SCREAMING_SNAKE_CASE_   ,				prediction_type=SCREAMING_SNAKE_CASE_   ,				algorithm_type=SCREAMING_SNAKE_CASE_   ,				)
                           assert not torch.isnan(SCREAMING_SNAKE_CASE_	).any(), "Samples have nan numbers"






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE_	)
       self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE_   ,				time_step=0	)






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.full_loop()
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_mean.item() - 0.23916	) < 1E-3






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.full_loop(prediction_type='''v_prediction'''	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_mean.item() - 0.091	) < 1E-3






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      self.scheduler_classes[0]
       UpperCamelCase__										=      self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE_   ,				dynamic_thresholding_ratio=0	)
       UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      10
       UpperCamelCase__										=      self.dummy_model()
       UpperCamelCase__										=      self.dummy_sample_deter.half()
       scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

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

       assert sample.dtype == torch.floataa




import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int







def  __UpperCamelCase    (						A ,							A							):
     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)
          UpperCamelCase__										=      torch.permute(A ,							(0, 2, 1)							)
     elif flax_key_tuple[-1] == "kernel" and ".".join(A							):
          # linear layer
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)
          UpperCamelCase__										=      flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)

     return flax_key_tuple, flax_tensor







def  __UpperCamelCase    (						A ,							A ,							A							):
     if "metadata" in layer:
          UpperCamelCase__										=      layer.split('''metadata'''							)
          UpperCamelCase__										=      ''''''.join(split_layer[0]							)[:-1]
          UpperCamelCase__										=      [tuple(('''metadata''' + split_layer[1]).split('''/'''							)							)]
     elif "kvstore" in layer:
          UpperCamelCase__										=      layer.split('''kvstore'''							)
          UpperCamelCase__										=      ''''''.join(split_layer[0]							)[:-1]
          UpperCamelCase__										=      [tuple(('''kvstore''' + split_layer[1]).split('''/'''							)							)]

     else:
          UpperCamelCase__										=      layer.split('''/'''							)
          UpperCamelCase__										=      '''/'''.join(split_layer[:-1]							)
          UpperCamelCase__										=      (split_layer[-1],)

     if "kvstore/path" in layer:
          UpperCamelCase__										=      f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
     elif "kvstore/driver" in layer:
          UpperCamelCase__										=      '''file'''
     else:
          UpperCamelCase__										=      checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      rename_keys(A							)
     UpperCamelCase__										=      {}
     for k, v in current_block.items():
          UpperCamelCase__										=      v
     UpperCamelCase__										=      new_current_block
     torch.save(A ,							A							)







def  __UpperCamelCase    (						A ,							A ,							A ,							A ,							A = WEIGHTS_NAME							):
     UpperCamelCase__										=      convert_file_size_to_int(A							)
     UpperCamelCase__										=      []
     UpperCamelCase__										=      {}
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0

     os.makedirs(A ,							exist_ok=A							)
     with gfile.GFile(switch_checkpoint_path + '''/checkpoint''' ,							'''rb'''							) as fp:
          UpperCamelCase__										=      serialization.msgpack_restore(fp.read()							)['''optimizer''']['''target''']
          UpperCamelCase__										=      flatten_dict(A ,							sep='''/'''							)

     UpperCamelCase__										=      {}
     for layer in checkpoint_info.keys():
          UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      get_key_and_tensorstore_dict(
              A ,							A ,							A							)
          if curr_real_layer_name in all_layers:
               UpperCamelCase__										=      content
          else:
               UpperCamelCase__										=      {split_layer[-1]: content}

     for key in all_layers.keys():
          # open tensorstore file
          UpperCamelCase__										=      ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
          UpperCamelCase__										=      torch.tensor(A							)
          UpperCamelCase__										=      raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

          # use the renaming pattern from the small conversion scripts
          UpperCamelCase__						,       UpperCamelCase__										=      rename_base_flax_keys(tuple(key.split('''/'''							)							) ,							A							)
          UpperCamelCase__										=      '''/'''.join(A							)

          # If this weight is going to tip up over the maximal size, we split.
          if current_block_size + weight_size > max_shard_size:
               UpperCamelCase__										=      os.path.join(
                   A ,							weights_name.replace('''.bin''' ,							f"-{len(A							)+1:05d}-of-???.bin"							)							)
               rename_and_save_block(A ,							A							)
               sharded_state_dicts.append(current_block.keys()							)
               del current_block
               UpperCamelCase__										=      {}
               UpperCamelCase__										=      0

          UpperCamelCase__										=      raw_weights.to(getattr(A ,							A							)							)
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     UpperCamelCase__										=      os.path.join(A ,							weights_name.replace('''.bin''' ,							f"-{len(A							)+1:05d}-of-???.bin"							)							)
     rename_and_save_block(A ,							A							)
     sharded_state_dicts.append(current_block.keys()							)

     # If we only have one shard, we return it
     if len(A							) == 1:
          return {weights_name: sharded_state_dicts[0]}, None

     # Otherwise, let's build the index
     UpperCamelCase__										=      {}
     UpperCamelCase__										=      {}
     for idx, shard in enumerate(A							):
          UpperCamelCase__										=      weights_name.replace(
              '''.bin''' ,							f"-{idx+1:05d}-of-{len(A							):05d}.bin"							)  # len(sharded_state_dicts):05d}
          UpperCamelCase__										=      os.path.join(A ,							weights_name.replace('''.bin''' ,							f"-{idx+1:05d}-of-???.bin"							)							)
          os.rename(A ,							os.path.join(A ,							A							)							)
          UpperCamelCase__										=      shard
          for key in shard:
               UpperCamelCase__										=      shard_file

    # Add the metadata
     UpperCamelCase__										=      {'''total_size''': total_size}
     UpperCamelCase__										=      {'''metadata''': metadata, '''weight_map''': weight_map}

     with open(os.path.join(A ,							A							) ,							'''w''' ,							encoding='''utf-8'''							) as f:
          UpperCamelCase__										=      json.dumps(A ,							indent=2 ,							sort_keys=A							) + '''\n'''
          f.write(A							)

     return metadata, index


if __name__ == "__main__":
     __magic_name__								=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--switch_t5x_checkpoint_path''',
         default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
         type=str,
         required=False,
         help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
     )
     parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
     parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
         type=str,
         required=False,
         help='''Path to the output pytorch model.''',
     )
     __magic_name__								=parser.parse_args()
     shard_on_the_fly(
         args.switch_tax_checkpoint_path,
         args.pytorch_dump_folder_path,
         args.max_shard_size,
         args.dtype,
     )







def  __UpperCamelCase    (						):
     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     UpperCamelCase__										=      SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''							)
     config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''							)
     UpperCamelCase__										=      SwitchTransformersForConditionalGeneration.from_pretrained(
         '''/home/arthur_huggingface_co/transformers/switch_converted''' ,							device_map='''auto'''							)

     UpperCamelCase__										=      TaTokenizer.from_pretrained('''t5-small'''							)
     UpperCamelCase__										=      '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

     UpperCamelCase__										=      tokenizer(A ,							return_tensors='''pt'''							).input_ids
     UpperCamelCase__										=      model.generate(A ,							decoder_start_token_id=0							)
     print(tokenizer.decode(out[0]							)							)



