




"""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							      =     {
    "/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	(   _snake_case				:   Tuple				)       ->   Optional[int]:






      '''simple docstring'''


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

            _A     =  R'(encoder|decoder)\/'

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

                  elif groups[0] == "decoder":
                        _A     =  re.sub(R'/mlp/'							,    R'/2/mlp/'							,    UpperCamelCase__				)
                        _A     =  re.sub(R'/pre_mlp_layer_norm/'							,    R'/2/layer_norm/'							,    UpperCamelCase__				)

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

            print(F'''{key} -> {new_key}'''				)
            _A     =  s_dict.pop(UpperCamelCase__				)

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

                  s_dict.pop(UpperCamelCase__				)

      return s_dict


a							      =     {
    "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	(   _snake_case				:   Optional[int]							,    _snake_case				:   Optional[int]				)       ->   Any:






      '''simple docstring'''


      import regex as re

      with open(UpperCamelCase__							,    'r'				) as f:
            _A     =  f.read()

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

      _A     =  re.findall(R'(.*activations) = \(\'(.*)\',\)'							,    UpperCamelCase__				)[0]
      _A     =  str(activation[1]				)

      _A     =  num_experts
      _A     =  SwitchTransformersConfig(**UpperCamelCase__				)
      return config






def 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   Union[str, Any]							,    _snake_case				:   Dict=None							,    _snake_case				:   Union[str, Any]="./"							,    _snake_case				:   Union[str, Any]=8				)       ->   Any:






      '''simple docstring'''


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

      if gin_file is not None:
            _A     =  convert_gin_to_config(UpperCamelCase__							,    UpperCamelCase__				)
      else:
            _A     =  SwitchTransformersConfig.from_pretrained(UpperCamelCase__				)

      _A     =  SwitchTransformersForConditionalGeneration(UpperCamelCase__				)

      _A     =  flax_params['target']
      _A     =  flatten_dict(UpperCamelCase__							,    sep='/'				)
      _A     =  rename_keys(UpperCamelCase__				)
      _A     =  unflatten_dict(UpperCamelCase__							,    sep='/'				)

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

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


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


'''simple docstring'''


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
					import torch

					from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class     A :





							def __init__(   self					:				List[str]  ,		__a					:				List[Any]  ,		__a					:				Dict=None  ,		__a					:				str=None  ,		__a					:				List[Any]=None  ,		__a					:				Union[str, Any]="resnet50"  ,		__a					:				List[Any]=3  ,		__a					:				List[Any]=3_2  ,		__a					:				List[Any]=3  ,		__a					:				Optional[int]=True  ,		__a					:				str=True  ,		)				->						Any:
								__UpperCAmelCase      					=      parent
								__UpperCAmelCase      					=      out_indices if out_indices is not None else [4]
								__UpperCAmelCase      					=      stage_names
								__UpperCAmelCase      					=      out_features
								__UpperCAmelCase      					=      backbone
								__UpperCAmelCase      					=      batch_size
								__UpperCAmelCase      					=      image_size
								__UpperCAmelCase      					=      num_channels
								__UpperCAmelCase      					=      use_pretrained_backbone
								__UpperCAmelCase      					=      is_training





							def    snake_case__    (   self					:				Optional[Any]						)				->						Optional[int]:
								__UpperCAmelCase      					=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
								__UpperCAmelCase      					=      self.get_config()

								return config, pixel_values





							def    snake_case__    (   self					:				Union[str, Any]						)				->						str:
								return TimmBackboneConfig(
								    image_size=self.image_size  ,		num_channels=self.num_channels  ,		out_features=self.out_features  ,		out_indices=self.out_indices  ,		stage_names=self.stage_names  ,		use_pretrained_backbone=self.use_pretrained_backbone  ,		backbone=self.backbone  ,		)





							def    snake_case__    (   self					:				int  ,		__a					:				Union[str, Any]  ,		__a					:				List[Any]						)				->						Tuple:
								__UpperCAmelCase      					=      TimmBackbone(config=__a						)
								model.to(__a						)
								model.eval()
								with torch.no_grad():
									__UpperCAmelCase      					=      model(__a						)
								self.parent.assertEqual(
								    result.feature_map[-1].shape  ,		(self.batch_size, model.channels[-1], 1_4, 1_4)  ,		)






							def    snake_case__    (   self					:				Dict						)				->						Tuple:
								__UpperCAmelCase      					=      self.prepare_config_and_inputs()
								__UpperCAmelCase					,      __UpperCAmelCase      					=      config_and_inputs
								__UpperCAmelCase      					=      {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
@require_timm
class     A ( UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						unittest.TestCase ):
							a_										=   (TimmBackbone,) if is_torch_available() else ()
							a_										=   {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
							a_										=   False
							a_										=   False
							a_										=   False
							a_										=   False





							def    snake_case__    (   self					:				Optional[int]						)				->						List[str]:
								__UpperCAmelCase      					=      TimmBackboneModelTester(self						)
								__UpperCAmelCase      					=      ConfigTester(self  ,		config_class=__a  ,		has_text_modality=__a						)





							def    snake_case__    (   self					:				List[str]						)				->						int:
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()





							def    snake_case__    (   self					:				List[str]						)				->						str:
								__UpperCAmelCase      					=      '''resnet18'''
								__UpperCAmelCase      					=      '''microsoft/resnet-18'''

								__UpperCAmelCase      					=      AutoBackbone.from_pretrained(__a  ,		use_timm_backbone=__a						)
								__UpperCAmelCase      					=      AutoBackbone.from_pretrained(__a						)

								self.assertEqual(len(timm_model.out_features						)  ,		len(transformers_model.out_features						)						)
								self.assertEqual(len(timm_model.stage_names						)  ,		len(transformers_model.stage_names						)						)
								self.assertEqual(timm_model.channels  ,		transformers_model.channels						)
								# Out indices are set to the last layer by default. For timm models, we don't know
								# the number of layers in advance, so we set it to (-1,), whereas for transformers
								# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
								self.assertEqual(timm_model.out_indices  ,		(-1,)						)
								self.assertEqual(transformers_model.out_indices  ,		[len(timm_model.stage_names						) - 1]						)

								__UpperCAmelCase      					=      AutoBackbone.from_pretrained(__a  ,		use_timm_backbone=__a  ,		out_indices=[1, 2, 3]						)
								__UpperCAmelCase      					=      AutoBackbone.from_pretrained(__a  ,		out_indices=[1, 2, 3]						)

								self.assertEqual(timm_model.out_indices  ,		transformers_model.out_indices						)
								self.assertEqual(len(timm_model.out_features						)  ,		len(transformers_model.out_features						)						)
								self.assertEqual(timm_model.channels  ,		transformers_model.channels						)





							@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''						)
							def    snake_case__    (   self					:				str						)				->						Optional[Any]:
								pass





							@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''						)
							def    snake_case__    (   self					:				str						)				->						List[Any]:
								pass





							@unittest.skip('''TimmBackbone initialization is managed on the timm side'''						)
							def    snake_case__    (   self					:				str						)				->						int:
								pass





							@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''						)
							def    snake_case__    (   self					:				Optional[Any]						)				->						Union[str, Any]:
								pass





							@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''						)
							def    snake_case__    (   self					:				Optional[int]						)				->						Dict:
								pass





							@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''						)
							def    snake_case__    (   self					:				str						)				->						Dict:
								pass





							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''						)
							def    snake_case__    (   self					:				Optional[Any]						)				->						Union[str, Any]:
								pass





							@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''						)
							def    snake_case__    (   self					:				Union[str, Any]						)				->						Dict:
								pass





							@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''						)
							def    snake_case__    (   self					:				List[str]						)				->						Union[str, Any]:
								pass





							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''						)
							def    snake_case__    (   self					:				str						)				->						List[Any]:
								pass





							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''						)
							def    snake_case__    (   self					:				Tuple						)				->						int:
								pass





							@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''						)
							def    snake_case__    (   self					:				Union[str, Any]						)				->						Union[str, Any]:
								pass





							@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''						)
							def    snake_case__    (   self					:				List[str]						)				->						Dict:
								pass





							@unittest.skip('''Safetensors is not supported by timm.'''						)
							def    snake_case__    (   self					:				int						)				->						int:
								pass





							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
							def    snake_case__    (   self					:				int						)				->						int:
								pass





							def    snake_case__    (   self					:				Optional[Any]						)				->						Optional[int]:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__UpperCAmelCase      					=      model_class(__a						)
									__UpperCAmelCase      					=      inspect.signature(model.forward						)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__UpperCAmelCase      					=      [*signature.parameters.keys()]

									__UpperCAmelCase      					=      ['''pixel_values''']
									self.assertListEqual(arg_names[:1]  ,		__a						)





							def    snake_case__    (   self					:				Any						)				->						int:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCAmelCase      					=      True
								__UpperCAmelCase      					=      self.has_attentions

								# no need to test all models as different heads yield the same functionality
								__UpperCAmelCase      					=      self.all_model_classes[0]
								__UpperCAmelCase      					=      model_class(__a						)
								model.to(__a						)

								__UpperCAmelCase      					=      self._prepare_for_class(__a  ,		__a						)
								__UpperCAmelCase      					=      model(**__a						)
								__UpperCAmelCase      					=      outputs[0][-1]

								# Encoder-/Decoder-only models
								__UpperCAmelCase      					=      outputs.hidden_states[0]
								hidden_states.retain_grad()

								if self.has_attentions:
									__UpperCAmelCase      					=      outputs.attentions[0]
									attentions.retain_grad()

								output.flatten()[0].backward(retain_graph=__a						)

								self.assertIsNotNone(hidden_states.grad						)

								if self.has_attentions:
									self.assertIsNotNone(attentions.grad						)






							def    snake_case__    (   self					:				Any						)				->						Union[str, Any]:
								__UpperCAmelCase					,      __UpperCAmelCase      					=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__UpperCAmelCase      					=      model_class(__a						)
									model.to(__a						)
									model.eval()
									__UpperCAmelCase      					=      model(**__a						)

									self.assertEqual(len(result.feature_maps						)  ,		len(config.out_indices						)						)
									self.assertEqual(len(model.channels						)  ,		len(config.out_indices						)						)

									# Check output of last stage is taken if out_features=None, out_indices=None
									__UpperCAmelCase      					=      copy.deepcopy(__a						)
									__UpperCAmelCase      					=      None
									__UpperCAmelCase      					=      model_class(__a						)
									model.to(__a						)
									model.eval()
									__UpperCAmelCase      					=      model(**__a						)

									self.assertEqual(len(result.feature_maps						)  ,		1						)
									self.assertEqual(len(model.channels						)  ,		1						)

									# Check backbone can be initialized with fresh weights
									__UpperCAmelCase      					=      copy.deepcopy(__a						)
									__UpperCAmelCase      					=      False
									__UpperCAmelCase      					=      model_class(__a						)
									model.to(__a						)
									model.eval()
									__UpperCAmelCase      					=      model(**__a						)
