


"""simple docstring"""



import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, 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 (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    MobileBertForMaskedLM,
		    MobileBertForMultipleChoice,
		    MobileBertForNextSentencePrediction,
		    MobileBertForPreTraining,
		    MobileBertForQuestionAnswering,
		    MobileBertForSequenceClassification,
		    MobileBertForTokenClassification,
		    MobileBertModel,
		)







class _lowerCAmelCase    :







			def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=7						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=99						,     UpperCamelCase__=64						,     UpperCamelCase__=32						,     UpperCamelCase__=5						,     UpperCamelCase__=4						,     UpperCamelCase__=37						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=512						,     UpperCamelCase__=16						,     UpperCamelCase__=2						,     UpperCamelCase__=0.02						,     UpperCamelCase__=3						,     UpperCamelCase__=4						,     UpperCamelCase__=None						,     )     ->		Optional[Any]:

							'''simple docstring'''




							snake_case   :				int				 =   parent
							snake_case   :				List[Any]				 =   batch_size
							snake_case   :				List[Any]				 =   seq_length
							snake_case   :				Optional[int]				 =   is_training
							snake_case   :				Union[str, Any]				 =   use_input_mask
							snake_case   :				List[Any]				 =   use_token_type_ids
							snake_case   :				Optional[int]				 =   use_labels
							snake_case   :				List[Any]				 =   vocab_size
							snake_case   :				int				 =   hidden_size
							snake_case   :				Dict				 =   embedding_size
							snake_case   :				Optional[Any]				 =   num_hidden_layers
							snake_case   :				str				 =   num_attention_heads
							snake_case   :				List[Any]				 =   intermediate_size
							snake_case   :				List[Any]				 =   hidden_act
							snake_case   :				int				 =   hidden_dropout_prob
							snake_case   :				str				 =   attention_probs_dropout_prob
							snake_case   :				str				 =   max_position_embeddings
							snake_case   :				Optional[int]				 =   type_vocab_size
							snake_case   :				Dict				 =   type_sequence_label_size
							snake_case   :				Optional[Any]				 =   initializer_range
							snake_case   :				Union[str, Any]				 =   num_labels
							snake_case   :				Tuple				 =   num_choices
							snake_case   :				Tuple				 =   scope







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

							'''simple docstring'''




							snake_case   :				Optional[int]				 =   ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

							snake_case   :				List[Any]				 =   None
							if self.use_input_mask:
											snake_case   :				str				 =   random_attention_mask([self.batch_size, self.seq_length]						)

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

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

							snake_case   :				Dict				 =   self.get_config()

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







			def 			lowerCamelCase		(     self						)     ->		Tuple:

							'''simple docstring'''




							return MobileBertConfig(
							    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						,     embedding_size=self.embedding_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=UpperCamelCase__						,     initializer_range=self.initializer_range						,     )







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				List[Any]				 =   MobileBertModel(config=UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				str				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     token_type_ids=UpperCamelCase__						)
							snake_case   :				Any				 =   model(UpperCamelCase__						,     token_type_ids=UpperCamelCase__						)
							snake_case   :				str				 =   model(UpperCamelCase__						)

							self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size)						)
							self.parent.assertEqual(result.pooler_output.shape						,     (self.batch_size, self.hidden_size)						)







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

							'''simple docstring'''




							snake_case   :				Any				 =   MobileBertForMaskedLM(config=UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				Any				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     token_type_ids=UpperCamelCase__						,     labels=UpperCamelCase__						)
							self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.vocab_size)						)







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

							'''simple docstring'''




							snake_case   :				Any				 =   MobileBertForNextSentencePrediction(config=UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				List[str]				 =   model(
							    UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     token_type_ids=UpperCamelCase__						,     labels=UpperCamelCase__						,     )
							self.parent.assertEqual(result.logits.shape						,     (self.batch_size, 2)						)







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

							'''simple docstring'''




							snake_case   :				str				 =   MobileBertForPreTraining(config=UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				List[Any]				 =   model(
							    UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     token_type_ids=UpperCamelCase__						,     labels=UpperCamelCase__						,     next_sentence_label=UpperCamelCase__						,     )
							self.parent.assertEqual(result.prediction_logits.shape						,     (self.batch_size, self.seq_length, self.vocab_size)						)
							self.parent.assertEqual(result.seq_relationship_logits.shape						,     (self.batch_size, 2)						)







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		int:

							'''simple docstring'''




							snake_case   :				int				 =   MobileBertForQuestionAnswering(config=UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				List[Any]				 =   model(
							    UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     token_type_ids=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 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				str				 =   self.num_labels
							snake_case   :				List[str]				 =   MobileBertForSequenceClassification(UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				Union[str, Any]				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     token_type_ids=UpperCamelCase__						,     labels=UpperCamelCase__						)
							self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels)						)







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Any:

							'''simple docstring'''




							snake_case   :				Any				 =   self.num_labels
							snake_case   :				Optional[int]				 =   MobileBertForTokenClassification(config=UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				Dict				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     token_type_ids=UpperCamelCase__						,     labels=UpperCamelCase__						)
							self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.num_labels)						)







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				List[str]				 =   self.num_choices
							snake_case   :				Optional[int]				 =   MobileBertForMultipleChoice(config=UpperCamelCase__						)
							model.to(UpperCamelCase__						)
							model.eval()
							snake_case   :				List[str]				 =   input_ids.unsqueeze(1						).expand(-1						,     self.num_choices						,     -1						).contiguous()
							snake_case   :				Optional[int]				 =   token_type_ids.unsqueeze(1						).expand(-1						,     self.num_choices						,     -1						).contiguous()
							snake_case   :				str				 =   input_mask.unsqueeze(1						).expand(-1						,     self.num_choices						,     -1						).contiguous()
							snake_case   :				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 			lowerCamelCase		(     self						)     ->		Optional[Any]:

							'''simple docstring'''




							snake_case   :				Any				 =   self.prepare_config_and_inputs()
							(
							    snake_case
							)   :				Union[str, Any]				 =   config_and_inputs
							snake_case   :				Optional[int]				 =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_torch
class _lowerCAmelCase    (				snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
			__UpperCAmelCase			:	Optional[Any]							  =  (
			    (
			        MobileBertModel,
			        MobileBertForMaskedLM,
			        MobileBertForMultipleChoice,
			        MobileBertForNextSentencePrediction,
			        MobileBertForPreTraining,
			        MobileBertForQuestionAnswering,
			        MobileBertForSequenceClassification,
			        MobileBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			__UpperCAmelCase			:	Optional[int]							  =  (
			    {
			        '''feature-extraction''': MobileBertModel,
			        '''fill-mask''': MobileBertForMaskedLM,
			        '''question-answering''': MobileBertForQuestionAnswering,
			        '''text-classification''': MobileBertForSequenceClassification,
			        '''token-classification''': MobileBertForTokenClassification,
			        '''zero-shot''': MobileBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			__UpperCAmelCase			:	Union[str, Any]							  =  True







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__=False						)     ->		List[str]:

							'''simple docstring'''




							snake_case   :				Any				 =   super()._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__						,     return_labels=UpperCamelCase__						)

							if return_labels:
											if model_class in get_values(UpperCamelCase__						):
															snake_case   :				Any				 =   torch.zeros(
															    (self.model_tester.batch_size, self.model_tester.seq_length)						,     dtype=torch.long						,     device=UpperCamelCase__						)
															snake_case   :				List[str]				 =   torch.zeros(
															    self.model_tester.batch_size						,     dtype=torch.long						,     device=UpperCamelCase__						)
							return inputs_dict







			def 			lowerCamelCase		(     self						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				List[Any]				 =   MobileBertModelTester(self						)
							snake_case   :				Optional[int]				 =   ConfigTester(self						,     config_class=UpperCamelCase__						,     hidden_size=37						)







			def 			lowerCamelCase		(     self						)     ->		Tuple:

							'''simple docstring'''




							self.config_tester.run_common_tests()







			def 			lowerCamelCase		(     self						)     ->		int:

							'''simple docstring'''




							snake_case   :				Tuple				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_model(*UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		str:

							'''simple docstring'''




							snake_case   :				List[str]				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		Dict:

							'''simple docstring'''




							snake_case   :				Optional[Any]				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCamelCase__						)







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

							'''simple docstring'''




							snake_case   :				int				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		Dict:

							'''simple docstring'''




							snake_case   :				Optional[int]				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				Union[str, Any]				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCamelCase__						)







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

							'''simple docstring'''




							snake_case   :				int				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCamelCase__						)







			def 			lowerCamelCase		(     self						)     ->		int:

							'''simple docstring'''




							snake_case   :				Optional[int]				 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCamelCase__						)







def   __lowerCAmelCase						(						lowercase  :  Any			)     ->						int:


				"""simple docstring"""





				return torch.tensor(
				    lowercase						,							dtype=torch.long						,							device=lowercase						,							)


__snake_case             =   1E-3







@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase    (				unittest.TestCase     ):







			@slow
			def 			lowerCamelCase		(     self						)     ->		Any:

							'''simple docstring'''




							snake_case   :				Optional[Any]				 =   MobileBertModel.from_pretrained("google/mobilebert-uncased"						).to(UpperCamelCase__						)
							snake_case   :				Any				 =   _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]						)
							with torch.no_grad():
											snake_case   :				Union[str, Any]				 =   model(UpperCamelCase__						)[0]
							snake_case   :				Optional[int]				 =   torch.Size((1, 9, 512)						)
							self.assertEqual(output.shape						,     UpperCamelCase__						)
							snake_case   :				int				 =   torch.tensor(
							    [
							        [
							            [-2.473_6526e07, 8.269_1656e04, 1.652_1838e05],
							            [-5.754_1704e-01, 3.905_6022e00, 4.401_1507e00],
							            [2.604_7359e00, 1.567_7652e00, -1.732_4188e-01],
							        ]
							    ]						,     device=UpperCamelCase__						,     )

							# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
							# ~1 difference, it's therefore not a good idea to measure using addition.
							# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
							# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
							snake_case   :				int				 =   torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE						)
							snake_case   :				int				 =   torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE						)

							self.assertTrue(lower_bound and upper_bound						)









"""simple docstring"""



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
__snake_case             =   {
    """/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						(						lowercase  :  Optional[int]			)     ->						List[str]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   list(s_dict.keys()			)
    for key in keys:
        snake_case   :				Any				 =   R".*/layers_(\d+)"
        snake_case   :				Tuple				 =   key
        if re.match(lowercase						,							lowercase			):
            snake_case   :				List[str]				 =   re.sub(R"layers_(\d+)"						,							R"block/\1/layer"						,							lowercase			)

        snake_case   :				Union[str, Any]				 =   R"(encoder|decoder)\/"

        if re.match(lowercase						,							lowercase			):
            snake_case   :				Any				 =   re.match(lowercase						,							lowercase			).groups()
            if groups[0] == "encoder":
                snake_case   :				Union[str, Any]				 =   re.sub(R"/mlp/"						,							R"/1/mlp/"						,							lowercase			)
                snake_case   :				int				 =   re.sub(R"/pre_mlp_layer_norm/"						,							R"/1/layer_norm/"						,							lowercase			)

            elif groups[0] == "decoder":
                snake_case   :				str				 =   re.sub(R"/mlp/"						,							R"/2/mlp/"						,							lowercase			)
                snake_case   :				List[str]				 =   re.sub(R"/pre_mlp_layer_norm/"						,							R"/2/layer_norm/"						,							lowercase			)

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

        print(F'{key} -> {new_key}'			)
        snake_case   :				Optional[Any]				 =   s_dict.pop(lowercase			)

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        snake_case   :				int				 =   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   :				Optional[Any]				 =   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   :				Tuple				 =   s_dict[key].shape[0]
            snake_case   :				int				 =   s_dict[key]
            for idx in range(lowercase			):
                snake_case   :				List[str]				 =   expert_weihts[idx]
                print(F'{key} -> {key.replace("expert/"						,							"nested fstring"			)}'			)

            s_dict.pop(lowercase			)

    return s_dict


__snake_case             =   {
    """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						(						lowercase  :  Dict						,							lowercase  :  Optional[Any]			)     ->						int:


    """simple docstring"""





    import regex as re

    with open(lowercase						,							"r"			) as f:
        snake_case   :				List[str]				 =   f.read()

    snake_case   :				Tuple				 =   re.findall(R"(.*) = ([0-9.]*)"						,							lowercase			)
    snake_case   :				Any				 =   {}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            snake_case   :				Tuple				 =   float(lowercase			) if "." in value else int(lowercase			)

    snake_case   :				List[str]				 =   re.findall(R"(.*activations) = \(\'(.*)\',\)"						,							lowercase			)[0]
    snake_case   :				List[Any]				 =   str(activation[1]			)

    snake_case   :				Optional[Any]				 =   num_experts
    snake_case   :				List[Any]				 =   SwitchTransformersConfig(**lowercase			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Tuple						,							lowercase  :  Union[str, Any]=None						,							lowercase  :  Any="./"						,							lowercase  :  int=8			)     ->						Dict:


    """simple docstring"""





    print(F'Loading flax weights from : {flax_checkpoint_path}'			)
    snake_case   :				Union[str, Any]				 =   checkpoints.load_tax_checkpoint(lowercase			)

    if gin_file is not None:
        snake_case   :				List[str]				 =   convert_gin_to_config(lowercase						,							lowercase			)
    else:
        snake_case   :				str				 =   SwitchTransformersConfig.from_pretrained(lowercase			)

    snake_case   :				Any				 =   SwitchTransformersForConditionalGeneration(lowercase			)

    snake_case   :				Optional[Any]				 =   flax_params["target"]
    snake_case   :				Optional[int]				 =   flatten_dict(lowercase						,							sep="/"			)
    snake_case   :				Optional[Any]				 =   rename_keys(lowercase			)
    snake_case   :				List[str]				 =   unflatten_dict(lowercase						,							sep="/"			)

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

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


if __name__ == "__main__":
  __snake_case             =   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""")
  __snake_case             =   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,
  )





