




'''simple docstring'''


import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, 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 (
     XLMForMultipleChoice,
     XLMForQuestionAnswering,
     XLMForQuestionAnsweringSimple,
     XLMForSequenceClassification,
     XLMForTokenClassification,
     XLMModel,
     XLMWithLMHeadModel,
 )
 from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  str						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Dict=13						,						lowercase__   :  Optional[Any]=7						,						lowercase__   :  Dict=True						,						lowercase__   :  Optional[Any]=True						,						lowercase__   :  List[Any]=True						,						lowercase__   :  Optional[int]=True						,						lowercase__   :  Optional[int]=True						,						lowercase__   :  List[str]=False						,						lowercase__   :  Union[str, Any]=False						,						lowercase__   :  Any=False						,						lowercase__   :  List[str]=2						,						lowercase__   :  Dict=99						,						lowercase__   :  Tuple=0						,						lowercase__   :  List[Any]=32						,						lowercase__   :  Optional[Any]=5						,						lowercase__   :  Any=4						,						lowercase__   :  Union[str, Any]=0.1						,						lowercase__   :  Optional[Any]=0.1						,						lowercase__   :  List[str]=512						,						lowercase__   :  Tuple=2						,						lowercase__   :  Tuple=0.02						,						lowercase__   :  Tuple=2						,						lowercase__   :  Dict=4						,						lowercase__   :  int="last"						,						lowercase__   :  Tuple=True						,						lowercase__   :  Optional[int]=None						,						lowercase__   :  Union[str, Any]=0						,						):


           '''simple docstring'''

           a_     :							List[Any]     						=  parent
           a_     :							Union[str, Any]     						=  batch_size
           a_     :							List[str]     						=  seq_length
           a_     :							int     						=  is_training
           a_     :							Union[str, Any]     						=  use_input_lengths
           a_     :							List[str]     						=  use_token_type_ids
           a_     :							Union[str, Any]     						=  use_labels
           a_     :							Tuple     						=  gelu_activation
           a_     :							str     						=  sinusoidal_embeddings
           a_     :							str     						=  causal
           a_     :							Tuple     						=  asm
           a_     :							Dict     						=  n_langs
           a_     :							int     						=  vocab_size
           a_     :							Any     						=  n_special
           a_     :							List[Any]     						=  hidden_size
           a_     :							int     						=  num_hidden_layers
           a_     :							List[str]     						=  num_attention_heads
           a_     :							List[str]     						=  hidden_dropout_prob
           a_     :							Union[str, Any]     						=  attention_probs_dropout_prob
           a_     :							Optional[Any]     						=  max_position_embeddings
           a_     :							Tuple     						=  type_sequence_label_size
           a_     :							Optional[Any]     						=  initializer_range
           a_     :							List[Any]     						=  num_labels
           a_     :							List[str]     						=  num_choices
           a_     :							Tuple     						=  summary_type
           a_     :							Any     						=  use_proj
           a_     :							List[str]     						=  scope
           a_     :							Union[str, Any]     						=  bos_token_id






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size       )
           a_     :							Any     						=  random_attention_mask([self.batch_size, self.seq_length]       )

           a_     :							Tuple     						=  None
           if self.use_input_lengths:
                a_     :							int     						=  (
                    ids_tensor([self.batch_size]						,						vocab_size=2       ) + self.seq_length - 2
                )  # small variation of seq_length

           a_     :							Dict     						=  None
           if self.use_token_type_ids:
                a_     :							Optional[Any]     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.n_langs       )

           a_     :							List[Any]     						=  None
           a_     :							Any     						=  None
           a_     :							Optional[int]     						=  None
           if self.use_labels:
                a_     :							str     						=  ids_tensor([self.batch_size]						,						self.type_sequence_label_size       )
                a_     :							Optional[Any]     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels       )
                a_     :							int     						=  ids_tensor([self.batch_size]						,						2       ).float()
                a_     :							Optional[Any]     						=  ids_tensor([self.batch_size]						,						self.num_choices       )

           a_     :							int     						=  self.get_config()

           return (
               config,
               input_ids,
               token_type_ids,
               input_lengths,
               sequence_labels,
               token_labels,
               is_impossible_labels,
               choice_labels,
               input_mask,
           )






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           return XLMConfig(
               vocab_size=self.vocab_size						,						n_special=self.n_special						,						emb_dim=self.hidden_size						,						n_layers=self.num_hidden_layers						,						n_heads=self.num_attention_heads						,						dropout=self.hidden_dropout_prob						,						attention_dropout=self.attention_probs_dropout_prob						,						gelu_activation=self.gelu_activation						,						sinusoidal_embeddings=self.sinusoidal_embeddings						,						asm=self.asm						,						causal=self.causal						,						n_langs=self.n_langs						,						max_position_embeddings=self.max_position_embeddings						,						initializer_range=self.initializer_range						,						summary_type=self.summary_type						,						use_proj=self.use_proj						,						num_labels=self.num_labels						,						bos_token_id=self.bos_token_id						,						)






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  str						,						lowercase__   :  int						,						lowercase__   :  Optional[int]						,						lowercase__   :  Tuple						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Optional[int]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  List[str]						,						lowercase__   :  Any						,						):


           '''simple docstring'''

           a_     :							List[str]     						=  XLMModel(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							Any     						=  model(lowercase__						,						lengths=lowercase__						,						langs=lowercase__       )
           a_     :							int     						=  model(lowercase__						,						langs=lowercase__       )
           a_     :							Optional[Any]     						=  model(lowercase__       )
           self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)       )






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  List[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Tuple						,						lowercase__   :  int						,						lowercase__   :  Dict						,						lowercase__   :  int						,						):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  XLMWithLMHeadModel(lowercase__       )
           model.to(lowercase__       )
           model.eval()

           a_     :							Tuple     						=  model(lowercase__						,						token_type_ids=lowercase__						,						labels=lowercase__       )
           self.parent.assertEqual(result.loss.shape						,						()       )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.vocab_size)       )






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Any						,						lowercase__   :  List[Any]						,						lowercase__   :  List[Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Dict						,						lowercase__   :  Tuple						,						lowercase__   :  Any						,						lowercase__   :  Optional[int]						,						):


           '''simple docstring'''

           a_     :							Tuple     						=  XLMForQuestionAnsweringSimple(lowercase__       )
           model.to(lowercase__       )
           model.eval()

           a_     :							int     						=  model(lowercase__       )

           a_     :							Optional[int]     						=  model(lowercase__						,						start_positions=lowercase__						,						end_positions=lowercase__       )
           a_     :							str     						=  outputs
           self.parent.assertEqual(result.start_logits.shape						,						(self.batch_size, self.seq_length)       )
           self.parent.assertEqual(result.end_logits.shape						,						(self.batch_size, self.seq_length)       )






      def 							lowercase_						( self   :  Any						,						lowercase__   :  Tuple						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  int						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Tuple						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Union[str, Any]						,						):


           '''simple docstring'''

           a_     :							List[str]     						=  XLMForQuestionAnswering(lowercase__       )
           model.to(lowercase__       )
           model.eval()

           a_     :							Optional[Any]     						=  model(lowercase__       )

           a_     :							Optional[Any]     						=  model(
               lowercase__						,						start_positions=lowercase__						,						end_positions=lowercase__						,						cls_index=lowercase__						,						is_impossible=lowercase__						,						p_mask=lowercase__						,						)

           a_     :							Union[str, Any]     						=  model(
               lowercase__						,						start_positions=lowercase__						,						end_positions=lowercase__						,						cls_index=lowercase__						,						is_impossible=lowercase__						,						)

           ((a_)						,     )     :							Tuple     						=  result_with_labels.to_tuple()

           a_     :							Dict     						=  model(lowercase__						,						start_positions=lowercase__						,						end_positions=lowercase__       )

           ((a_)						,     )     :							Optional[Any]     						=  result_with_labels.to_tuple()

           self.parent.assertEqual(result_with_labels.loss.shape						,						()       )
           self.parent.assertEqual(result.start_top_log_probs.shape						,						(self.batch_size, model.config.start_n_top)       )
           self.parent.assertEqual(result.start_top_index.shape						,						(self.batch_size, model.config.start_n_top)       )
           self.parent.assertEqual(
               result.end_top_log_probs.shape						,						(self.batch_size, model.config.start_n_top * model.config.end_n_top)       )
           self.parent.assertEqual(
               result.end_top_index.shape						,						(self.batch_size, model.config.start_n_top * model.config.end_n_top)       )
           self.parent.assertEqual(result.cls_logits.shape						,						(self.batch_size,)       )






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  Optional[int]						,						lowercase__   :  int						,						lowercase__   :  Optional[Any]						,						lowercase__   :  int						,						lowercase__   :  List[str]						,						lowercase__   :  Optional[int]						,						lowercase__   :  Dict						,						lowercase__   :  Optional[Any]						,						lowercase__   :  List[str]						,						):


           '''simple docstring'''

           a_     :							int     						=  XLMForSequenceClassification(lowercase__       )
           model.to(lowercase__       )
           model.eval()

           a_     :							Optional[int]     						=  model(lowercase__       )
           a_     :							Dict     						=  model(lowercase__						,						labels=lowercase__       )
           self.parent.assertEqual(result.loss.shape						,						()       )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  str						,						lowercase__   :  List[Any]						,						lowercase__   :  Tuple						,						lowercase__   :  List[str]						,						lowercase__   :  Any						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Dict						,						lowercase__   :  Optional[int]						,						lowercase__   :  Any						,						):


           '''simple docstring'''

           a_     :							List[Any]     						=  self.num_labels
           a_     :							str     						=  XLMForTokenClassification(lowercase__       )
           model.to(lowercase__       )
           model.eval()

           a_     :							Dict     						=  model(lowercase__						,						attention_mask=lowercase__						,						labels=lowercase__       )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels)       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  str						,						lowercase__   :  int						,						lowercase__   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  Dict						,						lowercase__   :  Tuple						,						):


           '''simple docstring'''

           a_     :							Dict     						=  self.num_choices
           a_     :							Dict     						=  XLMForMultipleChoice(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							Tuple     						=  input_ids.unsqueeze(1       ).expand(-1						,						self.num_choices						,						-1       ).contiguous()
           a_     :							Dict     						=  token_type_ids.unsqueeze(1       ).expand(-1						,						self.num_choices						,						-1       ).contiguous()
           a_     :							Optional[Any]     						=  input_mask.unsqueeze(1       ).expand(-1						,						self.num_choices						,						-1       ).contiguous()
           a_     :							Any     						=  model(
               lowercase__						,						attention_mask=lowercase__						,						token_type_ids=lowercase__						,						labels=lowercase__						,						)
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_choices)       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.prepare_config_and_inputs()
           (
               (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     
           )     :							List[Any]     						=  config_and_inputs
           a_     :							Optional[Any]     						=  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
           return config, inputs_dict







@require_torch
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Union[str, Any]   							=					(
          (
              XLMModel,
              XLMWithLMHeadModel,
              XLMForQuestionAnswering,
              XLMForSequenceClassification,
              XLMForQuestionAnsweringSimple,
              XLMForTokenClassification,
              XLMForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      __magic_name__  :       Optional[int]   							=					(
          (XLMWithLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      __magic_name__  :       Any   							=					(
          {
              '''feature-extraction''': XLMModel,
              '''fill-mask''': XLMWithLMHeadModel,
              '''question-answering''': XLMForQuestionAnsweringSimple,
              '''text-classification''': XLMForSequenceClassification,
              '''text-generation''': XLMWithLMHeadModel,
              '''token-classification''': XLMForTokenClassification,
              '''zero-shot''': XLMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )






      def 							lowercase_						( self   :  int						,						lowercase__   :  Tuple						,						lowercase__   :  Dict						,						lowercase__   :  Any						,						lowercase__   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           if (
               pipeline_test_casse_name == "QAPipelineTests"
               and tokenizer_name is not None
               and not tokenizer_name.endswith("""Fast"""       )
           ):
                # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                return True

           return False






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Dict						,						lowercase__   :  List[Any]						,						lowercase__   :  Tuple=False       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  super()._prepare_for_class(lowercase__						,						lowercase__						,						return_labels=lowercase__       )

           if return_labels:
                if model_class.__name__ == "XLMForQuestionAnswering":
                     a_     :							int     						=  torch.zeros(
                         self.model_tester.batch_size						,						dtype=torch.long						,						device=lowercase__       )
                     a_     :							Union[str, Any]     						=  torch.zeros(
                         self.model_tester.batch_size						,						dtype=torch.long						,						device=lowercase__       )

           return inputs_dict






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  XLMModelTester(self       )
           a_     :							Dict     						=  ConfigTester(self						,						config_class=lowercase__						,						emb_dim=37       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           self.config_tester.run_common_tests()






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_model(*lowercase__       )






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_lm_head(*lowercase__       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_simple_qa(*lowercase__       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							str     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_qa(*lowercase__       )






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_sequence_classif(*lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_token_classif(*lowercase__       )






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  List[str]						,						lowercase__   :  Tuple						,						lowercase__   :  List[Any]						,						lowercase__   :  Any						,						lowercase__   :  int						,						lowercase__   :  Union[str, Any]=False						,						lowercase__   :  Union[str, Any]=1       ):


           '''simple docstring'''

           self.assertIsInstance(lowercase__						,						lowercase__       )
           self.assertListEqual(
               [isinstance(lowercase__						,						lowercase__       ) for iter_attentions in attentions]						,						[True] * len(lowercase__       )       )
           self.assertEqual(len(lowercase__       )						,						(max_length - min_length) * num_beam_groups       )

           for idx, iter_attentions in enumerate(lowercase__       ):
                # adds PAD dummy token
                a_     :							Tuple     						=  min_length + idx + 1
                a_     :							str     						=  min_length + idx + 1

                a_     :							str     						=  (
                    batch_size * num_beam_groups,
                    config.num_attention_heads,
                    tgt_len,
                    src_len,
                )
                # check attn size
                self.assertListEqual(
                    [layer_attention.shape for layer_attention in iter_attentions]						,						[expected_shape] * len(lowercase__       )       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  List[Any]						,						lowercase__   :  Tuple						,						lowercase__   :  int						,						lowercase__   :  List[Any]						,						lowercase__   :  Dict						,						lowercase__   :  int=False						,						lowercase__   :  Optional[int]=1       ):


           '''simple docstring'''

           self.assertIsInstance(lowercase__						,						lowercase__       )
           self.assertListEqual(
               [isinstance(lowercase__						,						lowercase__       ) for iter_hidden_states in hidden_states]						,						[True] * len(lowercase__       )						,						)
           self.assertEqual(len(lowercase__       )						,						(max_length - min_length) * num_beam_groups       )

           for idx, iter_hidden_states in enumerate(lowercase__       ):
                # adds PAD dummy token
                a_     :							Optional[int]     						=  min_length + idx + 1
                a_     :							List[Any]     						=  (batch_size * num_beam_groups, seq_len, config.hidden_size)
                # check hidden size
                self.assertListEqual(
                    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]						,						[expected_shape] * len(lowercase__       )						,						)
           pass






      @slow
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_     :							Dict     						=  XLMModel.from_pretrained(lowercase__       )
                self.assertIsNotNone(lowercase__       )







@require_torch
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      @slow
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							int     						=  XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""       )
           model.to(lowercase__       )
           a_     :							Dict     						=  torch.tensor([[14, 447]]						,						dtype=torch.long						,						device=lowercase__       )  # the president
           a_     :							Tuple     						=  [
               14,
               447,
               14,
               447,
               14,
               447,
               14,
               447,
               14,
               447,
               14,
               447,
               14,
               447,
               14,
               447,
               14,
               447,
               14,
               447,
           ]  # the president the president the president the president the president the president the president the president the president the president
           # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
           a_     :							List[str]     						=  model.generate(lowercase__						,						do_sample=lowercase__       )
           self.assertListEqual(output_ids[0].cpu().numpy().tolist()						,						lowercase__       )






'''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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					List[str]				):




     """simple docstring"""


     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          a_     :							Optional[int]     						=  flax_key_tuple[:-1] + ("""weight""",)
          a_     :							Optional[int]     						=  torch.permute(UpperCamelCase__ ,    (0, 2, 1)				)
     elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase__				):
          # linear layer
          a_     :							Any     						=  flax_key_tuple[:-1] + ("""weight""",)
          a_     :							Optional[int]     						=  flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          a_     :							Any     						=  flax_key_tuple[:-1] + ("""weight""",)

     return flax_key_tuple, flax_tensor



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     if "metadata" in layer:
          a_     :							Optional[Any]     						=  layer.split("""metadata"""				)
          a_     :							List[str]     						=  """""".join(split_layer[0]				)[:-1]
          a_     :							List[str]     						=  [tuple(("""metadata""" + split_layer[1]).split("""/"""				)				)]
     elif "kvstore" in layer:
          a_     :							Union[str, Any]     						=  layer.split("""kvstore"""				)
          a_     :							Tuple     						=  """""".join(split_layer[0]				)[:-1]
          a_     :							Union[str, Any]     						=  [tuple(("""kvstore""" + split_layer[1]).split("""/"""				)				)]

     else:
          a_     :							List[str]     						=  layer.split("""/"""				)
          a_     :							Union[str, Any]     						=  """/""".join(split_layer[:-1]				)
          a_     :							Union[str, Any]     						=  (split_layer[-1],)

     if "kvstore/path" in layer:
          a_     :							Any     						=  F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
     elif "kvstore/driver" in layer:
          a_     :							Optional[int]     						=  """file"""
     else:
          a_     :							str     						=  checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     a_     :							int     						=  rename_keys(UpperCamelCase__				)
     a_     :							Any     						=  {}
     for k, v in current_block.items():
          a_     :							Union[str, Any]     						=  v
     a_     :							List[str]     						=  new_current_block
     torch.save(UpperCamelCase__ ,    UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					str = WEIGHTS_NAME				):




     """simple docstring"""


     a_     :							str     						=  convert_file_size_to_int(UpperCamelCase__				)
     a_     :							str     						=  []
     a_     :							Optional[int]     						=  {}
     a_     :							Optional[int]     						=  0
     a_     :							Any     						=  0

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

     a_     :							Dict     						=  {}
     for layer in checkpoint_info.keys():
          a_						,     a_						,     a_     :							List[Any]     						=  get_key_and_tensorstore_dict(
              UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__				)
          if curr_real_layer_name in all_layers:
               a_     :							int     						=  content
          else:
               a_     :							Tuple     						=  {split_layer[-1]: content}

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

          # use the renaming pattern from the small conversion scripts
          a_						,     a_     :							Tuple     						=  rename_base_flax_keys(tuple(key.split("""/"""				)				) ,    UpperCamelCase__				)
          a_     :							Dict     						=  """/""".join(UpperCamelCase__				)

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

          a_     :							List[str]     						=  raw_weights.to(getattr(UpperCamelCase__ ,    UpperCamelCase__				)				)
          current_block_size += weight_size
          total_size += weight_size

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

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

     # Otherwise, let's build the index
     a_     :							Tuple     						=  {}
     a_     :							List[Any]     						=  {}
     for idx, shard in enumerate(UpperCamelCase__				):
          a_     :							str     						=  weights_name.replace(
              """.bin""" ,    F"-{idx+1:05d}-of-{len(UpperCamelCase__				):05d}.bin"				)  # len(sharded_state_dicts):05d}
          a_     :							Union[str, Any]     						=  os.path.join(UpperCamelCase__ ,    weights_name.replace(""".bin""" ,    F"-{idx+1:05d}-of-???.bin"				)				)
          os.rename(UpperCamelCase__ ,    os.path.join(UpperCamelCase__ ,    UpperCamelCase__				)				)
          a_     :							str     						=  shard
          for key in shard:
               a_     :							Union[str, Any]     						=  shard_file

    # Add the metadata
     a_     :							List[str]     						=  {"""total_size""": total_size}
     a_     :							str     						=  {"""metadata""": metadata, """weight_map""": weight_map}

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

     return metadata, index


if __name__ == "__main__":
 lowerCAmelCase_      :  List[Any]				     =  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.',
 )
 lowerCAmelCase_      :  List[Any]				     =  parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     a_     :							Union[str, Any]     						=  SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""				)
     config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""				)
     a_     :							List[Any]     						=  SwitchTransformersForConditionalGeneration.from_pretrained(
         """/home/arthur_huggingface_co/transformers/switch_converted""" ,    device_map="""auto"""				)

     a_     :							Union[str, Any]     						=  TaTokenizer.from_pretrained("""t5-small"""				)
     a_     :							Optional[Any]     						=  """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

     a_     :							List[Any]     						=  tokenizer(UpperCamelCase__ ,    return_tensors="""pt"""				).input_ids
     a_     :							str     						=  model.generate(UpperCamelCase__ ,    decoder_start_token_id=0				)
     print(tokenizer.decode(out[0]				)				)
