



'''simple docstring'''





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 lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



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

     return flax_key_tuple, flax_tensor




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



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

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

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

     return curr_real_layer_name, split_layer, content




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    rename_keys(__A)
     _a           =    {}
     for k, v in current_block.items():
          _a           =    v
     _a           =    new_current_block
     torch.save(__A    ,      __A)




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A    ,      __A = WEIGHTS_NAME):



     """simple docstring"""



     _a           =    convert_file_size_to_int(__A)
     _a           =    []
     _a           =    {}
     _a           =    0
     _a           =    0

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

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

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

          # use the renaming pattern from the small conversion scripts
          _a    ,       _a           =    rename_base_flax_keys(tuple(key.split('''/'''))    ,      __A)
          _a           =    '''/'''.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:
               _a           =    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
               _a           =    {}
               _a           =    0

          _a           =    raw_weights.to(getattr(__A    ,      __A))
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     _a           =    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
     _a           =    {}
     _a           =    {}
     for idx, shard in enumerate(__A):
          _a           =    weights_name.replace(
              '''.bin'''    ,      F'''-{idx+1:05d}-of-{len(__A):05d}.bin''')  # len(sharded_state_dicts):05d}
          _a           =    os.path.join(__A    ,      weights_name.replace('''.bin'''    ,      F'''-{idx+1:05d}-of-???.bin'''))
          os.rename(__A    ,      os.path.join(__A    ,      __A))
          _a           =    shard
          for key in shard:
               _a           =    shard_file

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

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

     return metadata, index


if __name__ == "__main__":
  lowercase_									=    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.",
  )
  lowercase_									=    parser.parse_args()
  shard_on_the_fly(
      args.switch_tax_checkpoint_path,
      args.pytorch_dump_folder_path,
      args.max_shard_size,
      args.dtype,
  )




def lowerCAmelCase    ():



     """simple docstring"""



     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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








'''simple docstring'''





import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=True					,  A=True					,  A=False					,  A=True					,  A=99					,  A=32					,  A=5					,  A=4					,  A=37					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=512					,  A=16					,  A=2					,  A=0.02					,  A=3					,  A=4					,  A=None					,  )       ->	str:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    is_training
      _a           =    use_input_mask
      _a           =    use_token_type_ids
      _a           =    use_labels
      _a           =    vocab_size
      _a           =    hidden_size
      _a           =    num_hidden_layers
      _a           =    num_attention_heads
      _a           =    intermediate_size
      _a           =    hidden_act
      _a           =    hidden_dropout_prob
      _a           =    attention_probs_dropout_prob
      _a           =    max_position_embeddings
      _a           =    type_vocab_size
      _a           =    type_sequence_label_size
      _a           =    initializer_range
      _a           =    num_labels
      _a           =    num_choices
      _a           =    scope



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


      """simple docstring"""



      _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size			)

      _a           =    None
      if self.use_input_mask:
           _a           =    random_attention_mask([self.batch_size, self.seq_length]			)

      _a           =    None
      if self.use_token_type_ids:
           _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size			)

      _a           =    None
      _a           =    None
      _a           =    None
      if self.use_labels:
           _a           =    ids_tensor([self.batch_size]					,  self.type_sequence_label_size			)
           _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.num_labels			)
           _a           =    ids_tensor([self.batch_size]					,  self.num_choices			)

      _a           =    self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



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


      """simple docstring"""



      return OpenLlamaConfig(
          vocab_size=self.vocab_size					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  type_vocab_size=self.type_vocab_size					,  is_decoder=A					,  initializer_range=self.initializer_range					,  use_stable_embedding=A					,  )



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    OpenLlamaModel(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A			)
      _a           =    model(A			)
      self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Any:


      """simple docstring"""



      _a           =    True
      _a           =    OpenLlamaModel(A			)
      model.to(A			)
      model.eval()
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  )
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  )
      _a           =    model(A					,  attention_mask=A			)
      self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Tuple:


      """simple docstring"""



      _a           =    OpenLlamaForCausalLM(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size)			)



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Dict:


      """simple docstring"""



      _a           =    True
      _a           =    True
      _a           =    OpenLlamaForCausalLM(config=A			)
      model.to(A			)
      model.eval()

      # first forward pass
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  use_cache=A					,  )
      _a           =    outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _a           =    ids_tensor((self.batch_size, 3)					,  config.vocab_size			)
      _a           =    ids_tensor((self.batch_size, 3)					,  vocab_size=2			)

      # append to next input_ids and
      _a           =    torch.cat([input_ids, next_tokens]					,  dim=-1			)
      _a           =    torch.cat([input_mask, next_mask]					,  dim=-1			)

      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  output_hidden_states=A					,  )['''hidden_states'''][0]
      _a           =    model(
          A					,  attention_mask=A					,  encoder_hidden_states=A					,  encoder_attention_mask=A					,  past_key_values=A					,  output_hidden_states=A					,  )['''hidden_states'''][0]

      # select random slice
      _a           =    ids_tensor((1,)					,  output_from_past.shape[-1]			).item()
      _a           =    output_from_no_past[:, -3:, random_slice_idx].detach()
      _a           =    output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(A					,  A					,  atol=1E-3			)			)



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


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict






@require_torch
class __A     (    A  ,  A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			(
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 __lowerCamelCase       :			Any		=			(OpenLlamaForCausalLM,) if is_torch_available() else ()
 __lowerCamelCase       :			List[Any]		=			(
     {
         'feature-extraction': OpenLlamaModel,
         'text-classification': OpenLlamaForSequenceClassification,
         'text-generation': OpenLlamaForCausalLM,
         'zero-shot': OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			List[str]		=			False



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    OpenLlamaModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



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


      """simple docstring"""



      self.config_tester.run_common_tests()



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           _a           =    type
           self.model_tester.create_and_check_model(*A			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    3
      _a           =    input_dict['''input_ids''']
      _a           =    input_ids.ne(1			).to(A			)
      _a           =    ids_tensor([self.model_tester.batch_size]					,  self.model_tester.type_sequence_label_size			)
      _a           =    OpenLlamaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.assertEqual(result.logits.shape					,  (self.model_tester.batch_size, self.model_tester.num_labels)			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    3
      _a           =    '''single_label_classification'''
      _a           =    input_dict['''input_ids''']
      _a           =    input_ids.ne(1			).to(A			)
      _a           =    ids_tensor([self.model_tester.batch_size]					,  self.model_tester.type_sequence_label_size			)
      _a           =    OpenLlamaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.assertEqual(result.logits.shape					,  (self.model_tester.batch_size, self.model_tester.num_labels)			)



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


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    3
      _a           =    '''multi_label_classification'''
      _a           =    input_dict['''input_ids''']
      _a           =    input_ids.ne(1			).to(A			)
      _a           =    ids_tensor(
          [self.model_tester.batch_size, config.num_labels]					,  self.model_tester.type_sequence_label_size			).to(torch.float			)
      _a           =    OpenLlamaForSequenceClassification(A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  attention_mask=A					,  labels=A			)
      self.assertEqual(result.logits.shape					,  (self.model_tester.batch_size, self.model_tester.num_labels)			)



 @unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test'''			)
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      pass



 @parameterized.expand([('''linear''',), ('''dynamic''',)]			)
 def    a__    (self					,  A			)       ->	Optional[int]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()
      _a           =    ids_tensor([1, 10]					,  config.vocab_size			)
      _a           =    ids_tensor([1, int(config.max_position_embeddings * 1.5			)]					,  config.vocab_size			)

      set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
      _a           =    OpenLlamaModel(A			)
      original_model.to(A			)
      original_model.eval()
      _a           =    original_model(A			).last_hidden_state
      _a           =    original_model(A			).last_hidden_state

      set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
      _a           =    {'''type''': scaling_type, '''factor''': 10.0}
      _a           =    OpenLlamaModel(A			)
      scaled_model.to(A			)
      scaled_model.eval()
      _a           =    scaled_model(A			).last_hidden_state
      _a           =    scaled_model(A			).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
           self.assertTrue(torch.allclose(A					,  A					,  atol=1E-5			)			)
      else:
           self.assertFalse(torch.allclose(A					,  A					,  atol=1E-5			)			)

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(A					,  A					,  atol=1E-5			)			)



