





"""simple docstring"""





import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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 (
      FlaubertForMultipleChoice,
      FlaubertForQuestionAnswering,
      FlaubertForQuestionAnsweringSimple,
      FlaubertForSequenceClassification,
      FlaubertForTokenClassification,
      FlaubertModel,
      FlaubertWithLMHeadModel,
  )
  from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=1_3       ,					__UpperCAmelCase=7       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=9_9       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=5       ,					__UpperCAmelCase=4       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=5_1_2       ,					__UpperCAmelCase=1_2       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=4       ,					__UpperCAmelCase="last"       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size )
         lowerCAmelCase__     :Union[str, Any]											=				random_attention_mask([self.batch_size, self.seq_length] )

         lowerCAmelCase__     :Union[str, Any]											=				None
         if self.use_input_lengths:
           lowerCAmelCase__     :Any											=				(
               ids_tensor([self.batch_size]       ,					vocab_size=2 ) + self.seq_length - 2
           )  # small variation of seq_length

         lowerCAmelCase__     :str											=				None
         if self.use_token_type_ids:
           lowerCAmelCase__     :str											=				ids_tensor([self.batch_size, self.seq_length]       ,					self.n_langs )

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

         lowerCAmelCase__     :Dict											=				self.get_config()

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         return FlaubertConfig(
             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       ,					)






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				FlaubertModel(config=__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()
         lowerCAmelCase__     :Any											=				model(__UpperCAmelCase       ,					lengths=__UpperCAmelCase       ,					langs=__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				model(__UpperCAmelCase       ,					langs=__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				model(__UpperCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				FlaubertWithLMHeadModel(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :List[Any]											=				model(__UpperCAmelCase       ,					token_type_ids=__UpperCAmelCase       ,					labels=__UpperCAmelCase )
         self.parent.assertEqual(result.loss.shape       ,					() )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				FlaubertForQuestionAnsweringSimple(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :List[Any]											=				model(__UpperCAmelCase )

         lowerCAmelCase__     :int											=				model(__UpperCAmelCase       ,					start_positions=__UpperCAmelCase       ,					end_positions=__UpperCAmelCase )
         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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				FlaubertForQuestionAnswering(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :Any											=				model(__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				model(
             __UpperCAmelCase       ,					start_positions=__UpperCAmelCase       ,					end_positions=__UpperCAmelCase       ,					cls_index=__UpperCAmelCase       ,					is_impossible=__UpperCAmelCase       ,					p_mask=__UpperCAmelCase       ,					)

         lowerCAmelCase__     :Union[str, Any]											=				model(
             __UpperCAmelCase       ,					start_positions=__UpperCAmelCase       ,					end_positions=__UpperCAmelCase       ,					cls_index=__UpperCAmelCase       ,					is_impossible=__UpperCAmelCase       ,					)

         ((lowerCAmelCase__)				,							)     :Dict											=				result_with_labels.to_tuple()

         lowerCAmelCase__     :List[Any]											=				model(__UpperCAmelCase       ,					start_positions=__UpperCAmelCase       ,					end_positions=__UpperCAmelCase )

         ((lowerCAmelCase__)				,							)     :int											=				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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				FlaubertForSequenceClassification(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :Dict											=				model(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				model(__UpperCAmelCase       ,					labels=__UpperCAmelCase )

         self.parent.assertEqual(result.loss.shape       ,					() )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				self.num_labels
         lowerCAmelCase__     :Dict											=				FlaubertForTokenClassification(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         lowerCAmelCase__     :List[str]											=				model(__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase       ,					labels=__UpperCAmelCase )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.num_choices
         lowerCAmelCase__     :Union[str, Any]											=				FlaubertForMultipleChoice(config=__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()
         lowerCAmelCase__     :int											=				input_ids.unsqueeze(1 ).expand(-1       ,					self.num_choices       ,					-1 ).contiguous()
         lowerCAmelCase__     :Dict											=				token_type_ids.unsqueeze(1 ).expand(-1       ,					self.num_choices       ,					-1 ).contiguous()
         lowerCAmelCase__     :Optional[int]											=				input_mask.unsqueeze(1 ).expand(-1       ,					self.num_choices       ,					-1 ).contiguous()
         lowerCAmelCase__     :List[Any]											=				model(
             __UpperCAmelCase       ,					attention_mask=__UpperCAmelCase       ,					token_type_ids=__UpperCAmelCase       ,					labels=__UpperCAmelCase       ,					)
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.prepare_config_and_inputs()
         (
             (
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							
         )     :List[Any]											=				config_and_inputs
         lowerCAmelCase__     :Union[str, Any]											=				{
             'input_ids': input_ids,
             'token_type_ids': token_type_ids,
             'lengths': input_lengths,
             'attention_mask': input_mask,
         }
         return config, inputs_dict






@require_torch
class    _lowerCAmelCase  (	a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Optional[int]						    =   (
           (
               FlaubertModel,
               FlaubertWithLMHeadModel,
               FlaubertForQuestionAnswering,
               FlaubertForQuestionAnsweringSimple,
               FlaubertForSequenceClassification,
               FlaubertForTokenClassification,
               FlaubertForMultipleChoice,
           )
           if is_torch_available()
           else ()
       )
       __magic_name__	:List[str]						    =   (
           {
               """feature-extraction""": FlaubertModel,
               """fill-mask""": FlaubertWithLMHeadModel,
               """question-answering""": FlaubertForQuestionAnsweringSimple,
               """text-classification""": FlaubertForSequenceClassification,
               """token-classification""": FlaubertForTokenClassification,
               """zero-shot""": FlaubertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				super()._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase       ,					return_labels=__UpperCAmelCase )

         if return_labels:
           if model_class.__name__ == "FlaubertForQuestionAnswering":
             lowerCAmelCase__     :List[Any]											=				torch.zeros(
                 self.model_tester.batch_size       ,					dtype=torch.long       ,					device=__UpperCAmelCase )
             lowerCAmelCase__     :Any											=				torch.zeros(
                 self.model_tester.batch_size       ,					dtype=torch.long       ,					device=__UpperCAmelCase )

         return inputs_dict






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				FlaubertModelTester(self )
         lowerCAmelCase__     :Dict											=				ConfigTester(self       ,					config_class=__UpperCAmelCase       ,					emb_dim=3_7 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.config_tester.run_common_tests()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_model(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_lm_head(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_simple_qa(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_qa(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_sequence_classif(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_token_classif(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_multiple_choice(*__UpperCAmelCase )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase__     :Optional[int]											=				FlaubertModel.from_pretrained(__UpperCAmelCase )
           self.assertIsNotNone(__UpperCAmelCase )






       @slow
       @require_torch_gpu
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[Any]											=				self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
           # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
           if model_class == FlaubertForMultipleChoice:
             return

           lowerCAmelCase__     :List[str]											=				True
           lowerCAmelCase__     :Optional[int]											=				model_class(config=__UpperCAmelCase )

           lowerCAmelCase__     :Dict											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase )
           lowerCAmelCase__     :Tuple											=				torch.jit.trace(
               __UpperCAmelCase       ,					(inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )

           with tempfile.TemporaryDirectory() as tmp:
             torch.jit.save(__UpperCAmelCase       ,					os.path.join(__UpperCAmelCase       ,					'traced_model.pt' ) )
             lowerCAmelCase__     :Optional[Any]											=				torch.jit.load(os.path.join(__UpperCAmelCase       ,					'traced_model.pt' )       ,					map_location=__UpperCAmelCase )
             loaded(inputs_dict['input_ids'].to(__UpperCAmelCase )       ,					inputs_dict['attention_mask'].to(__UpperCAmelCase ) )






@require_torch
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				FlaubertModel.from_pretrained('flaubert/flaubert_base_cased' )
         lowerCAmelCase__     :Optional[int]											=				torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
         with torch.no_grad():
           lowerCAmelCase__     :Optional[int]											=				model(__UpperCAmelCase )[0]
         lowerCAmelCase__     :Union[str, Any]											=				torch.Size((1, 1_1, 7_6_8) )
         self.assertEqual(output.shape       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				torch.tensor(
             [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]] )

         self.assertTrue(torch.allclose(output[:, :3, :3]       ,					__UpperCAmelCase       ,					atol=1E-4 ) )




import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}



def       _lowerCAmelCase    (				lowerCAmelCase_				:Any				)->Any:
   '''simple docstring'''






   snake_case_            =       list(s_dict.keys()				)
   for key in keys:
      snake_case_            =       r".*/layers_(\d+)"
      snake_case_            =       key
      if re.match(lowerCAmelCase_		,    lowerCAmelCase_				):
         snake_case_            =       re.sub(r"layers_(\d+)"		,    r"block/\1/layer"		,    lowerCAmelCase_				)

      snake_case_            =       r"(encoder|decoder)\/"

      if re.match(lowerCAmelCase_		,    lowerCAmelCase_				):
         snake_case_            =       re.match(lowerCAmelCase_		,    lowerCAmelCase_				).groups()
         if groups[0] == "encoder":
            snake_case_            =       re.sub(r"/mlp/"		,    r"/1/mlp/"		,    lowerCAmelCase_				)
            snake_case_            =       re.sub(r"/pre_mlp_layer_norm/"		,    r"/1/layer_norm/"		,    lowerCAmelCase_				)

         elif groups[0] == "decoder":
            snake_case_            =       re.sub(r"/mlp/"		,    r"/2/mlp/"		,    lowerCAmelCase_				)
            snake_case_            =       re.sub(r"/pre_mlp_layer_norm/"		,    r"/2/layer_norm/"		,    lowerCAmelCase_				)

        # 2. Convert other classic mappings
      for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
         if old_key in new_key:
            snake_case_            =       new_key.replace(lowerCAmelCase_		,    lowerCAmelCase_				)

      print(F'''{key} -> {new_key}'''				)
      snake_case_            =       s_dict.pop(lowerCAmelCase_				)

   if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      snake_case_            =       s_dict[
          "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
      ].T
   if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      snake_case_            =       s_dict[
          "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
      ].T

   # 3. Take extra care of the EXPERTS layer
   for key in list(s_dict.keys()				):
      if "expert" in key:
         snake_case_            =       s_dict[key].shape[0]
         snake_case_            =       s_dict[key]
         for idx in range(lowerCAmelCase_				):
            snake_case_            =       expert_weihts[idx]
            print(F'''{key} -> {key.replace('expert/'		,    'nested fstring'				)}'''				)

         s_dict.pop(lowerCAmelCase_				)

   return s_dict


SCREAMING_SNAKE_CASE						:Optional[Any]			=  {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Any				)->Optional[int]:
   '''simple docstring'''






   import regex as re

   with open(lowerCAmelCase_		,    "r"				) as f:
      snake_case_            =       f.read()

   snake_case_            =       re.findall(r"(.*) = ([0-9.]*)"		,    lowerCAmelCase_				)
   snake_case_            =       {}
   for param, value in regex_match:
      if param in GIN_TO_CONFIG_MAPPING and value != "":
         snake_case_            =       float(lowerCAmelCase_				) if "." in value else int(lowerCAmelCase_				)

   snake_case_            =       re.findall(r"(.*activations) = \(\'(.*)\',\)"		,    lowerCAmelCase_				)[0]
   snake_case_            =       str(activation[1]				)

   snake_case_            =       num_experts
   snake_case_            =       SwitchTransformersConfig(**lowerCAmelCase_				)
   return config



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Dict=None		,    lowerCAmelCase_				:str="./"		,    lowerCAmelCase_				:Optional[int]=8				)->List[str]:
   '''simple docstring'''






   print(F'''Loading flax weights from : {flax_checkpoint_path}'''				)
   snake_case_            =       checkpoints.load_tax_checkpoint(lowerCAmelCase_				)

   if gin_file is not None:
      snake_case_            =       convert_gin_to_config(lowerCAmelCase_		,    lowerCAmelCase_				)
   else:
      snake_case_            =       SwitchTransformersConfig.from_pretrained(lowerCAmelCase_				)

   snake_case_            =       SwitchTransformersForConditionalGeneration(lowerCAmelCase_				)

   snake_case_            =       flax_params["target"]
   snake_case_            =       flatten_dict(lowerCAmelCase_		,    sep="/"				)
   snake_case_            =       rename_keys(lowerCAmelCase_				)
   snake_case_            =       unflatten_dict(lowerCAmelCase_		,    sep="/"				)

   # Load the flax params in the PT model
   load_flax_weights_in_pytorch_model(lowerCAmelCase_		,    lowerCAmelCase_				)

   print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
   pt_model.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:int			=  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--switch_t5x_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
           ''' model architecture. If not provided, a `gin_file` has to be provided.'''
       ),
   )
   parser.add_argument(
       '''--gin_file''',
       default=None,
       type=str,
       required=False,
       help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
   )
   parser.add_argument(
       '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
   )
   parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
   SCREAMING_SNAKE_CASE						:int			=  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(
       args.switch_tax_checkpoint_path,
       args.config_name,
       args.gin_file,
       args.pytorch_dump_folder_path,
       args.num_experts,
   )




