


"""simple docstring"""



import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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 (
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXModel,
  )







class _lowerCAmelCase    :







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=7						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=99						,     UpperCamelCase__=64						,     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						,     )     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   parent
       snake_case   :				List[str]				 =   batch_size
       snake_case   :				List[str]				 =   seq_length
       snake_case   :				Any				 =   is_training
       snake_case   :				int				 =   use_input_mask
       snake_case   :				Optional[int]				 =   use_token_type_ids
       snake_case   :				Tuple				 =   use_labels
       snake_case   :				List[str]				 =   vocab_size
       snake_case   :				Optional[Any]				 =   hidden_size
       snake_case   :				Any				 =   num_hidden_layers
       snake_case   :				Optional[Any]				 =   num_attention_heads
       snake_case   :				Dict				 =   intermediate_size
       snake_case   :				Optional[int]				 =   hidden_act
       snake_case   :				Union[str, Any]				 =   hidden_dropout_prob
       snake_case   :				Tuple				 =   attention_probs_dropout_prob
       snake_case   :				List[Any]				 =   max_position_embeddings
       snake_case   :				List[Any]				 =   type_vocab_size
       snake_case   :				Optional[int]				 =   type_sequence_label_size
       snake_case   :				int				 =   initializer_range
       snake_case   :				int				 =   num_labels
       snake_case   :				List[Any]				 =   num_choices
       snake_case   :				Dict				 =   scope
       snake_case   :				Union[str, Any]				 =   vocab_size - 1







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				List[str]				 =   ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

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

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

       snake_case   :				Any				 =   self.get_config()

       return config, input_ids, input_mask, token_labels







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       return GPTNeoXConfig(
           vocab_size=self.vocab_size						,     hidden_size=self.hidden_size						,     num_hidden_layers=self.num_hidden_layers						,     num_attention_heads=self.num_attention_heads						,     intermediate_size=self.intermediate_size						,     hidden_act=self.hidden_act						,     hidden_dropout_prob=self.hidden_dropout_prob						,     attention_probs_dropout_prob=self.attention_probs_dropout_prob						,     max_position_embeddings=self.max_position_embeddings						,     type_vocab_size=self.type_vocab_size						,     is_decoder=UpperCamelCase__						,     initializer_range=self.initializer_range						,     pad_token_id=self.pad_token_id						,     )







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case ,snake_case ,snake_case ,snake_case   :				str				 =   self.prepare_config_and_inputs()

       snake_case   :				int				 =   True

       return config, input_ids, input_mask, token_labels







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

       '''simple docstring'''




       snake_case   :				Tuple				 =   GPTNeoXModel(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				List[str]				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						)
       snake_case   :				List[Any]				 =   model(UpperCamelCase__						)
       self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size)						)







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

       '''simple docstring'''




       snake_case   :				str				 =   True
       snake_case   :				Union[str, Any]				 =   GPTNeoXModel(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				Any				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						)
       self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size)						)







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

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   GPTNeoXForCausalLM(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				Optional[int]				 =   model(UpperCamelCase__						,     attention_mask=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__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   self.num_labels
       snake_case   :				int				 =   GPTNeoXForQuestionAnswering(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				List[Any]				 =   model(UpperCamelCase__						,     attention_mask=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__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Dict				 =   self.num_labels
       snake_case   :				Tuple				 =   GPTNeoXForSequenceClassification(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				Optional[Any]				 =   ids_tensor([self.batch_size]						,     self.type_sequence_label_size						)
       snake_case   :				int				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     labels=UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels)						)







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

       '''simple docstring'''




       snake_case   :				Dict				 =   self.num_labels
       snake_case   :				Dict				 =   GPTNeoXForTokenClassification(UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()
       snake_case   :				str				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     labels=UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.num_labels)						)







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

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   True
       snake_case   :				Union[str, Any]				 =   GPTNeoXForCausalLM(config=UpperCamelCase__						)
       model.to(UpperCamelCase__						)
       model.eval()

       # first forward pass
       snake_case   :				Dict				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     use_cache=UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       snake_case   :				List[str]				 =   ids_tensor((self.batch_size, 3)						,     config.vocab_size						)
       snake_case   :				Dict				 =   ids_tensor((self.batch_size, 3)						,     vocab_size=2						)

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

       snake_case   :				Optional[int]				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     output_hidden_states=UpperCamelCase__						)
       snake_case   :				Tuple				 =   output_from_no_past["hidden_states"][0]
       snake_case   :				Any				 =   model(
           UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     past_key_values=UpperCamelCase__						,     output_hidden_states=UpperCamelCase__						,     )["hidden_states"][0]

       # select random slice
       snake_case   :				int				 =   ids_tensor((1,)						,     output_from_past.shape[-1]						).item()
       snake_case   :				Tuple				 =   output_from_no_past[:, -3:, random_slice_idx].detach()
       snake_case   :				List[Any]				 =   output_from_past[:, :, random_slice_idx].detach()

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

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







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				str				 =   self.prepare_config_and_inputs()
       snake_case ,snake_case ,snake_case ,snake_case   :				str				 =   config_and_inputs
       snake_case   :				List[str]				 =   {"input_ids": input_ids, "attention_mask": input_mask}
       return config, inputs_dict







@require_torch
class _lowerCAmelCase    (				snake_case_   ,							snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	List[Any]							  =  (
       (
           GPTNeoXModel,
           GPTNeoXForCausalLM,
           GPTNeoXForQuestionAnswering,
           GPTNeoXForSequenceClassification,
           GPTNeoXForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase			:	Union[str, Any]							  =  (GPTNeoXForCausalLM,) if is_torch_available() else ()
   __UpperCAmelCase			:	Union[str, Any]							  =  (
       {
           '''feature-extraction''': GPTNeoXModel,
           '''question-answering''': GPTNeoXForQuestionAnswering,
           '''text-classification''': GPTNeoXForSequenceClassification,
           '''text-generation''': GPTNeoXForCausalLM,
           '''token-classification''': GPTNeoXForTokenClassification,
           '''zero-shot''': GPTNeoXForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase			:	List[str]							  =  False
   __UpperCAmelCase			:	Optional[Any]							  =  False
   __UpperCAmelCase			:	Tuple							  =  False
   __UpperCAmelCase			:	List[Any]							  =  False







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

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   GPTNeoXModelTester(self						)
       snake_case   :				Tuple				 =   ConfigTester(self						,     config_class=UpperCamelCase__						,     hidden_size=64						,     num_attention_heads=8						)







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

       '''simple docstring'''




       self.config_tester.run_common_tests()







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case ,snake_case ,snake_case ,snake_case   :				List[str]				 =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)







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

       '''simple docstring'''




       snake_case ,snake_case ,snake_case ,snake_case   :				List[str]				 =   self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case ,snake_case ,snake_case ,snake_case   :				str				 =   self.model_tester.prepare_config_and_inputs_for_decoder()

       snake_case   :				List[Any]				 =   None

       self.model_tester.create_and_check_model_as_decoder(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case ,snake_case ,snake_case ,snake_case   :				Optional[int]				 =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)







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

       '''simple docstring'''




       snake_case   :				Dict				 =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_causal_lm(*UpperCamelCase__						)







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

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case   :				Dict				 =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__						)







   @unittest.skip(reason="Feed forward chunking is not implemented"						)
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       pass







   @parameterized.expand([("linear",), ("dynamic",)]						)
   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		List[Any]:

       '''simple docstring'''




       snake_case ,snake_case   :				int				 =   self.model_tester.prepare_config_and_inputs_for_common()
       snake_case   :				Optional[int]				 =   ids_tensor([1, 10]						,     config.vocab_size						)
       snake_case   :				Tuple				 =   ids_tensor([1, int(config.max_position_embeddings * 1.5						)]						,     config.vocab_size						)

       set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
       snake_case   :				Optional[Any]				 =   GPTNeoXModel(UpperCamelCase__						)
       original_model.to(UpperCamelCase__						)
       original_model.eval()
       snake_case   :				Optional[int]				 =   original_model(UpperCamelCase__						).last_hidden_state
       snake_case   :				int				 =   original_model(UpperCamelCase__						).last_hidden_state

       set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
       snake_case   :				List[str]				 =   {"type": scaling_type, "factor": 10.0}
       snake_case   :				Optional[int]				 =   GPTNeoXModel(UpperCamelCase__						)
       scaled_model.to(UpperCamelCase__						)
       scaled_model.eval()
       snake_case   :				int				 =   scaled_model(UpperCamelCase__						).last_hidden_state
       snake_case   :				List[str]				 =   scaled_model(UpperCamelCase__						).last_hidden_state

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

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







@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):







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

       '''simple docstring'''




       snake_case   :				Any				 =   AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"						)
       for checkpointing in [True, False]:
           snake_case   :				Union[str, Any]				 =   GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"						)

           if checkpointing:
               model.gradient_checkpointing_enable()
           else:
               model.gradient_checkpointing_disable()
           model.to(UpperCamelCase__						)

           snake_case   :				Optional[Any]				 =   tokenizer("My favorite food is"						,     return_tensors="pt"						).to(UpperCamelCase__						)
           # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
           # See: https://github.com/huggingface/transformers/pull/24193
           snake_case   :				List[str]				 =   "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

           snake_case   :				Optional[int]				 =   model.generate(**UpperCamelCase__						,     do_sample=UpperCamelCase__						,     max_new_tokens=20						)
           snake_case   :				Optional[int]				 =   tokenizer.batch_decode(UpperCamelCase__						)[0]

           self.assertEqual(UpperCamelCase__						,     UpperCamelCase__						)









"""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,
  )





