


"""simple docstring"""







import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu




class 			UpperCamelCase_     (			nn.Module):

	"""simple docstring"""





	def __init__(			self      :    str       )	->     Optional[int]:
						super().__init__()
						__SCREAMING_SNAKE_CASE						       =    nn.Linear(3      ,      4       )
						__SCREAMING_SNAKE_CASE						       =    nn.BatchNormad(4       )
						__SCREAMING_SNAKE_CASE						       =    nn.Linear(4      ,      5       )




	def   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    int       )	->     Union[str, Any]:
						return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase__       )       )       )




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    List[Any]      ,      *UpperCAmelCase__      :    Any      ,      **UpperCAmelCase__      :    Optional[Any]       )	->     Tuple:
						return (args[0] + 1,) + args[1:], kwargs




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    Any       )	->     List[Any]:
						return output + 1




class 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    Any       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    ModelForTest()
						__SCREAMING_SNAKE_CASE						       =    ModelHook()

						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__       )
						self.assertEqual(test_model._hf_hook      ,      UpperCAmelCase__       )
						self.assertTrue(hasattr(UpperCAmelCase__      ,      "_old_forward"       )       )

						# Check adding the hook did not change the name or the signature
						self.assertEqual(test_model.forward.__name__      ,      "forward"       )
						self.assertListEqual(list(inspect.signature(test_model.forward       ).parameters       )      ,      ["x"]       )

						remove_hook_from_module(UpperCAmelCase__       )
						self.assertFalse(hasattr(UpperCAmelCase__      ,      "_hf_hook"       )       )
						self.assertFalse(hasattr(UpperCAmelCase__      ,      "_old_forward"       )       )




	def   UpperCAmelCase_				(			self      :    Any       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    ModelForTest()
						__SCREAMING_SNAKE_CASE						       =    ModelHook()

						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__       )
						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__      ,      append=UpperCAmelCase__       )

						self.assertEqual(isinstance(test_model._hf_hook      ,      UpperCAmelCase__       )      ,      UpperCAmelCase__       )
						self.assertEqual(len(test_model._hf_hook.hooks       )      ,      2       )
						self.assertTrue(hasattr(UpperCAmelCase__      ,      "_old_forward"       )       )

						# Check adding the hook did not change the name or the signature
						self.assertEqual(test_model.forward.__name__      ,      "forward"       )
						self.assertListEqual(list(inspect.signature(test_model.forward       ).parameters       )      ,      ["x"]       )

						remove_hook_from_module(UpperCAmelCase__       )
						self.assertFalse(hasattr(UpperCAmelCase__      ,      "_hf_hook"       )       )
						self.assertFalse(hasattr(UpperCAmelCase__      ,      "_old_forward"       )       )




	def   UpperCAmelCase_				(			self      :    List[str]       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    ModelForTest()
						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    test_model(x + 1       )
						__SCREAMING_SNAKE_CASE						       =    test_model(x + 2       )

						__SCREAMING_SNAKE_CASE						       =    PreForwardHook()
						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )
						self.assertTrue(torch.allclose(UpperCAmelCase__      ,      UpperCAmelCase__      ,      atol=1E-5       )       )

						# Attaching a hook to a model when it already has one replaces, does not chain
						__SCREAMING_SNAKE_CASE						       =    PreForwardHook()
						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )
						self.assertTrue(torch.allclose(UpperCAmelCase__      ,      UpperCAmelCase__      ,      atol=1E-5       )       )

						# You need to use the sequential hook to chain two or more hooks
						__SCREAMING_SNAKE_CASE						       =    SequentialHook(PreForwardHook()      ,      PreForwardHook()       )
						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )
						assert torch.allclose(UpperCAmelCase__      ,      UpperCAmelCase__      ,      atol=1E-5       )




	def   UpperCAmelCase_				(			self      :    str       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    ModelForTest()
						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    PostForwardHook()
						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )
						self.assertTrue(torch.allclose(UpperCAmelCase__      ,      output + 1      ,      atol=1E-5       )       )

						# Attaching a hook to a model when it already has one replaces, does not chain
						__SCREAMING_SNAKE_CASE						       =    PostForwardHook()
						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )
						self.assertTrue(torch.allclose(UpperCAmelCase__      ,      output + 1      ,      atol=1E-5       )       )

						# You need to use the sequential hook to chain two or more hooks
						__SCREAMING_SNAKE_CASE						       =    SequentialHook(PostForwardHook()      ,      PostForwardHook()       )
						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )
						assert torch.allclose(UpperCAmelCase__      ,      output + 2      ,      atol=1E-5       )




	def   UpperCAmelCase_				(			self      :    Dict       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    ModelForTest()
						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    PostForwardHook()
						add_hook_to_module(UpperCAmelCase__      ,      UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )
						self.assertTrue(torch.allclose(UpperCAmelCase__      ,      output + 1       )       )
						self.assertTrue(outputa.requires_grad       )

						__SCREAMING_SNAKE_CASE						       =    True
						__SCREAMING_SNAKE_CASE						       =    test_model(UpperCAmelCase__       )
						self.assertFalse(outputa.requires_grad       )




	@require_multi_gpu
	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    ModelForTest()
						# Everything is on CPU
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )

						# This will move each submodule on different devices
						add_hook_to_module(model.lineara      ,      AlignDevicesHook(execution_device=0       )       )
						add_hook_to_module(model.batchnorm      ,      AlignDevicesHook(execution_device=0       )       )
						add_hook_to_module(model.lineara      ,      AlignDevicesHook(execution_device=1       )       )

						self.assertEqual(model.lineara.weight.device      ,      torch.device(0       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device(0       )       )
						self.assertEqual(model.batchnorm.running_mean.device      ,      torch.device(0       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device(1       )       )

						# We can still make a forward pass. The input does not need to be on any particular device
						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.assertEqual(output.device      ,      torch.device(1       )       )

						# We can add a general hook to put back output on same device as input.
						add_hook_to_module(UpperCAmelCase__      ,      AlignDevicesHook(io_same_device=UpperCAmelCase__       )       )
						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       ).to(0       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.assertEqual(output.device      ,      torch.device(0       )       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    ModelForTest()

						# Everything is on CPU
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )

						# This will move each submodule on different devices
						__SCREAMING_SNAKE_CASE						       =    {"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}

						add_hook_to_module(model.lineara      ,      AlignDevicesHook(**UpperCAmelCase__       )       )
						add_hook_to_module(model.batchnorm      ,      AlignDevicesHook(**UpperCAmelCase__       )       )
						add_hook_to_module(model.lineara      ,      AlignDevicesHook(**UpperCAmelCase__       )       )

						# Parameters have been offloaded, so on the meta device
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						# Buffers are not included in the offload by default, so are on the execution device
						__SCREAMING_SNAKE_CASE						       =    torch.device(hook_kwargs["execution_device"]       )
						self.assertEqual(model.batchnorm.running_mean.device      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.assertEqual(output.device      ,      UpperCAmelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_module(model.lineara       )
						remove_hook_from_module(model.batchnorm       )
						remove_hook_from_module(model.lineara       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )

						# Now test with buffers included in the offload
						__SCREAMING_SNAKE_CASE						       =    {
						    "execution_device": 0 if torch.cuda.is_available() else "cpu",
						    "offload": True,
						    "offload_buffers": True,
						}

						add_hook_to_module(model.lineara      ,      AlignDevicesHook(**UpperCAmelCase__       )       )
						add_hook_to_module(model.batchnorm      ,      AlignDevicesHook(**UpperCAmelCase__       )       )
						add_hook_to_module(model.lineara      ,      AlignDevicesHook(**UpperCAmelCase__       )       )

						# Parameters have been offloaded, so on the meta device, buffers included
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.batchnorm.running_mean.device      ,      torch.device("meta"       )       )

						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.assertEqual(output.device      ,      UpperCAmelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_module(model.lineara       )
						remove_hook_from_module(model.batchnorm       )
						remove_hook_from_module(model.lineara       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    ModelForTest()

						# Everything is on CPU
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )

						# This will move each submodule on different devices
						__SCREAMING_SNAKE_CASE						       =    0 if torch.cuda.is_available() else "cpu"
						attach_align_device_hook(UpperCAmelCase__      ,      execution_device=UpperCAmelCase__      ,      offload=UpperCAmelCase__       )

						# Parameters have been offloaded, so on the meta device
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						# Buffers are not included in the offload by default, so are on the execution device
						__SCREAMING_SNAKE_CASE						       =    torch.device(UpperCAmelCase__       )
						self.assertEqual(model.batchnorm.running_mean.device      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.assertEqual(output.device      ,      UpperCAmelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_submodules(UpperCAmelCase__       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )

						# Now test with buffers included in the offload
						attach_align_device_hook(UpperCAmelCase__      ,      execution_device=UpperCAmelCase__      ,      offload=UpperCAmelCase__      ,      offload_buffers=UpperCAmelCase__       )

						# Parameters have been offloaded, so on the meta device, buffers included
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.batchnorm.running_mean.device      ,      torch.device("meta"       )       )

						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.assertEqual(output.device      ,      UpperCAmelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_submodules(UpperCAmelCase__       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )




	def   UpperCAmelCase_				(			self      :    int       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    ModelForTest()

						# Everything is on CPU
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )

						# This will move each submodule on different devices
						__SCREAMING_SNAKE_CASE						       =    0 if torch.cuda.is_available() else "cpu"
						attach_align_device_hook(
						    UpperCAmelCase__      ,      execution_device=UpperCAmelCase__      ,      offload=UpperCAmelCase__      ,      weights_map=model.state_dict()       )

						# Parameters have been offloaded, so on the meta device
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						# Buffers are not included in the offload by default, so are on the execution device
						__SCREAMING_SNAKE_CASE						       =    torch.device(UpperCAmelCase__       )
						self.assertEqual(model.batchnorm.running_mean.device      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.assertEqual(output.device      ,      UpperCAmelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_submodules(UpperCAmelCase__       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )

						# Now test with buffers included in the offload
						attach_align_device_hook(
						    UpperCAmelCase__      ,      execution_device=UpperCAmelCase__      ,      offload=UpperCAmelCase__      ,      weights_map=model.state_dict()      ,      offload_buffers=UpperCAmelCase__      ,      )

						# Parameters have been offloaded, so on the meta device, buffers included
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("meta"       )       )
						self.assertEqual(model.batchnorm.running_mean.device      ,      torch.device("meta"       )       )

						__SCREAMING_SNAKE_CASE						       =    torch.randn(2      ,      3       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.assertEqual(output.device      ,      UpperCAmelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_submodules(UpperCAmelCase__       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.batchnorm.weight.device      ,      torch.device("cpu"       )       )
						self.assertEqual(model.lineara.weight.device      ,      torch.device("cpu"       )       )




"""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
a__    :							Optional[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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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

										__SCREAMING_SNAKE_CASE						       =    R"(encoder|decoder)\/"

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

															elif groups[0] == "decoder":
																				__SCREAMING_SNAKE_CASE						       =    re.sub(R"/mlp/"  ,						R"/2/mlp/"  ,						lowerCAmelCase_       )
																				__SCREAMING_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:
																				__SCREAMING_SNAKE_CASE						       =    new_key.replace(lowerCAmelCase_  ,						lowerCAmelCase_       )

										print(f"""{key} -> {new_key}"""       )
										__SCREAMING_SNAKE_CASE						       =    s_dict.pop(lowerCAmelCase_       )

					if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
										__SCREAMING_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:
										__SCREAMING_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:
															__SCREAMING_SNAKE_CASE						       =    s_dict[key].shape[0]
															__SCREAMING_SNAKE_CASE						       =    s_dict[key]
															for idx in range(lowerCAmelCase_       ):
																				__SCREAMING_SNAKE_CASE						       =    expert_weihts[idx]
																				print(f"""{key} -> {key.replace('expert/'  ,						'nested fstring'       )}"""       )

															s_dict.pop(lowerCAmelCase_       )

					return s_dict


a__    :							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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					import regex as re

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

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

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

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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_="./"  ,						lowerCAmelCase_=8       ):




					'''simple docstring'''


					print(f"""Loading flax weights from : {flax_checkpoint_path}"""       )
					__SCREAMING_SNAKE_CASE						       =    checkpoints.load_tax_checkpoint(lowerCAmelCase_       )

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

					__SCREAMING_SNAKE_CASE						       =    SwitchTransformersForConditionalGeneration(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    flax_params["target"]
					__SCREAMING_SNAKE_CASE						       =    flatten_dict(lowerCAmelCase_  ,						sep="/"       )
					__SCREAMING_SNAKE_CASE						       =    rename_keys(lowerCAmelCase_       )
					__SCREAMING_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__":
		a__    :							List[Any]            =					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''')
		a__    :							Tuple            =					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,
		)
