from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		)






class 		a__						:

					def __init__(    self      :       Optional[int],_A      :       Tuple,_A      :       List[Any]=13,_A      :       Tuple=7,_A      :       int=True,_A      :       List[str]=True,_A      :       Optional[int]=True,_A      :       Tuple=True,_A      :       str=99,_A      :       Optional[Any]=[1, 1, 2],_A      :       List[str]=1,_A      :       Tuple=32,_A      :       Any=4,_A      :       Optional[Any]=8,_A      :       Optional[Any]=37,_A      :       Any="gelu_new",_A      :       Tuple=0.1,_A      :       int=0.1,_A      :       Optional[int]=0.0,_A      :       Optional[int]=512,_A      :       Union[str, Any]=3,_A      :       Optional[Any]=0.02,_A      :       Optional[Any]=3,_A      :       List[Any]=4,_A      :       str=None,_A      :       str=False,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    parent
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    seq_length
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    is_training
							SCREAMING_SNAKE_CASE_  :		str				      =    use_input_mask
							SCREAMING_SNAKE_CASE_  :		int				      =    use_token_type_ids
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    use_labels
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    block_sizes
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    num_decoder_layers
							SCREAMING_SNAKE_CASE_  :		Any				      =    d_model
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    n_head
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    d_head
							SCREAMING_SNAKE_CASE_  :		int				      =    d_inner
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    hidden_dropout
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    attention_dropout
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    activation_dropout
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		str				      =    type_vocab_size
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    num_labels
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_choices
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    scope
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    initializer_std

							# Used in the tests to check the size of the first attention layer
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    n_head
							# Used in the tests to check the size of the first hidden state
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.d_model
							# Used in the tests to check the number of output hidden states/attentions
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    sum(self.block_sizes    ) + (0 if base else self.num_decoder_layers)
							# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
							# the last hidden state of the first block (which is the first hidden state of the decoder).
							if not base:
									SCREAMING_SNAKE_CASE_  :		str				      =    self.num_hidden_layers + 2

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    ids_tensor([self.batch_size, self.seq_length],self.vocab_size    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    None
							if self.use_input_mask:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    random_attention_mask([self.batch_size, self.seq_length]    )

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

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

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    FunnelConfig(
							    vocab_size=self.vocab_size,block_sizes=self.block_sizes,num_decoder_layers=self.num_decoder_layers,d_model=self.d_model,n_head=self.n_head,d_head=self.d_head,d_inner=self.d_inner,hidden_act=self.hidden_act,hidden_dropout=self.hidden_dropout,attention_dropout=self.attention_dropout,activation_dropout=self.activation_dropout,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,initializer_std=self.initializer_std,)

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

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int,_A      :       Tuple,_A      :       List[str],_A      :       Optional[Any],_A      :       Any,_A      :       Optional[int],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    TFFunnelModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [input_ids, input_mask]
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.d_model)    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    False
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    TFFunnelModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.d_model)    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    False
							SCREAMING_SNAKE_CASE_  :		Any				      =    TFFunnelModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.d_model)    )

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Union[str, Any],_A      :       Any,_A      :       Optional[Any],_A      :       List[Any],_A      :       List[str],_A      :       Optional[int],_A      :       int,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    TFFunnelBaseModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [input_ids, input_mask]
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, 2, self.d_model)    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    False
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TFFunnelBaseModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, 3, self.d_model)    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    False
							SCREAMING_SNAKE_CASE_  :		Any				      =    TFFunnelBaseModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, 2, self.d_model)    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Tuple,_A      :       Dict,_A      :       List[Any],_A      :       str,_A      :       int,_A      :       Optional[int],_A      :       Optional[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TFFunnelForPreTraining(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length)    )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    TFFunnelForMaskedLM(config=_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       List[Any],_A      :       Optional[int],_A      :       Optional[int],_A      :       List[str],_A      :       Optional[int],_A      :       Optional[int],_A      :       List[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		int				      =    TFFunnelForSequenceClassification(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       List[Any],_A      :       Dict,_A      :       Dict,_A      :       List[str],_A      :       Tuple,_A      :       str,_A      :       str,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.num_choices
							SCREAMING_SNAKE_CASE_  :		int				      =    TFFunnelForMultipleChoice(config=_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    tf.tile(tf.expand_dims(_A,1    ),(1, self.num_choices, 1)    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    tf.tile(tf.expand_dims(_A,1    ),(1, self.num_choices, 1)    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    tf.tile(tf.expand_dims(_A,1    ),(1, self.num_choices, 1)    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    {
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							    "token_type_ids": multiple_choice_token_type_ids,
							}
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)    )

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       str,_A      :       str,_A      :       List[str],_A      :       Union[str, Any],_A      :       Dict,_A      :       List[Any],_A      :       int,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TFFunnelForTokenClassification(config=_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Dict,_A      :       List[str],_A      :       str,_A      :       List[str],_A      :       List[str],_A      :       str,_A      :       Dict,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    TFFunnelForQuestionAnswering(config=_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A    )
							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 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






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







@require_tf
class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							(
					    (
					        TFFunnelModel,
					        TFFunnelForMaskedLM,
					        TFFunnelForPreTraining,
					        TFFunnelForQuestionAnswering,
					        TFFunnelForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					A	       =							(
					    {
					        'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
					        'fill-mask': TFFunnelForMaskedLM,
					        'question-answering': TFFunnelForQuestionAnswering,
					        'text-classification': TFFunnelForSequenceClassification,
					        'token-classification': TFFunnelForTokenClassification,
					        'zero-shot': TFFunnelForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					A	       =							False
					A	       =							False

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TFFunnelModelTester(self    )
							SCREAMING_SNAKE_CASE_  :		int				      =    ConfigTester(self,config_class=_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A    )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*_A    )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_A    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_A    )






					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_A    )







@require_tf
class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							(
					    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
					)
					A	       =							False
					A	       =							False

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    TFFunnelModelTester(self,base=_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ConfigTester(self,config_class=_A    )

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







							"""simple docstring"""






							self.config_tester.run_common_tests()

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_base_model(*_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






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






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*_A    )

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
__lowerCamelCase					:	Any       =  {
    '''/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       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    list(s_dict.keys()	)
		for key in keys:
				SCREAMING_SNAKE_CASE_  :		int				      =    R".*/layers_(\d+)"
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    key
				if re.match(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		Any				      =    re.sub(R"layers_(\d+)"   , R"block/\1/layer"   , lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    R"(encoder|decoder)\/"

				if re.match(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		str				      =    re.match(lowerCAmelCase   , lowerCAmelCase	).groups()
						if groups[0] == "encoder":
								SCREAMING_SNAKE_CASE_  :		Any				      =    re.sub(R"/mlp/"   , R"/1/mlp/"   , lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    re.sub(R"/pre_mlp_layer_norm/"   , R"/1/layer_norm/"   , lowerCAmelCase	)

						elif groups[0] == "decoder":
								SCREAMING_SNAKE_CASE_  :		List[str]				      =    re.sub(R"/mlp/"   , R"/2/mlp/"   , lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		Dict				      =    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:
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    new_key.replace(lowerCAmelCase   , lowerCAmelCase	)

				print(f'{key} -> {new_key}'	)
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    s_dict.pop(lowerCAmelCase	)

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

						s_dict.pop(lowerCAmelCase	)

		return s_dict


__lowerCamelCase					:	List[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       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		import regex as re

		with open(lowerCAmelCase   , "r"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    f.read()

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

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

		SCREAMING_SNAKE_CASE_  :		str				      =    num_experts
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    SwitchTransformersConfig(**lowerCAmelCase	)
		return config




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str=None   , lowerCAmelCase							:   Optional[Any]="./"   , lowerCAmelCase							:   Dict=8	):
		"""simple docstring"""







		print(f'Loading flax weights from : {flax_checkpoint_path}'	)
		SCREAMING_SNAKE_CASE_  :		int				      =    checkpoints.load_tax_checkpoint(lowerCAmelCase	)

		if gin_file is not None:
				SCREAMING_SNAKE_CASE_  :		int				      =    convert_gin_to_config(lowerCAmelCase   , lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    SwitchTransformersConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		str				      =    SwitchTransformersForConditionalGeneration(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    flax_params["target"]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    flatten_dict(lowerCAmelCase   , sep="/"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    rename_keys(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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__":
		__lowerCamelCase					:	List[str]       =  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''')
		__lowerCamelCase					:	Any       =  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,
		)
