


'''simple docstring'''




import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
       from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

class 		UpperCAmelCase_      :





     def __init__( self       :      Dict    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Union[str, Any]=3    ,      UpperCAmelCase__       :      Tuple=3_2    ,      UpperCAmelCase__       :      Union[str, Any]=3    ,      UpperCAmelCase__       :      List[str]=1_0    ,      UpperCAmelCase__       :      Any=[8, 1_6, 3_2, 6_4]    ,      UpperCAmelCase__       :      Optional[int]=[1, 1, 2, 1]    ,      UpperCAmelCase__       :      Optional[Any]=True    ,      UpperCAmelCase__       :      Dict=True    ,      UpperCAmelCase__       :      List[str]="relu"    ,      UpperCAmelCase__       :      int=3    ,      UpperCAmelCase__       :      Tuple=None    ,      UpperCAmelCase__       :      Dict=["stage2", "stage3", "stage4"]    ,      UpperCAmelCase__       :      Optional[Any]=[2, 3, 4]    ,      UpperCAmelCase__       :      List[str]=1    ,      )				->  Union[str, Any]:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			image_size
        lowerCAmelCase     =			num_channels
        lowerCAmelCase     =			embeddings_size
        lowerCAmelCase     =			hidden_sizes
        lowerCAmelCase     =			depths
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			num_labels
        lowerCAmelCase     =			scope
        lowerCAmelCase     =			len(UpperCAmelCase__    )
        lowerCAmelCase     =			out_features
        lowerCAmelCase     =			out_indices
        lowerCAmelCase     =			num_groups





     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        lowerCAmelCase     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        lowerCAmelCase     =			None
        if self.use_labels:
           lowerCAmelCase     =			ids_tensor([self.batch_size]    ,      self.num_labels    )

        lowerCAmelCase     =			self.get_config()

        return config, pixel_values, labels





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  int:
        return BitConfig(
            num_channels=self.num_channels    ,      embeddings_size=self.embeddings_size    ,      hidden_sizes=self.hidden_sizes    ,      depths=self.depths    ,      hidden_act=self.hidden_act    ,      num_labels=self.num_labels    ,      out_features=self.out_features    ,      out_indices=self.out_indices    ,      num_groups=self.num_groups    ,      )





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    )				->  str:
        lowerCAmelCase     =			BitModel(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)    ,      )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      List[Any]    )				->  Optional[int]:
        lowerCAmelCase     =			self.num_labels
        lowerCAmelCase     =			BitForImageClassification(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)    )





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      str    )				->  str:
        lowerCAmelCase     =			BitBackbone(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps    )    ,      len(config.out_features    )    )
        self.parent.assertListEqual(list(result.feature_maps[0].shape    )    ,      [self.batch_size, self.hidden_sizes[1], 4, 4]    )

        # verify channels
        self.parent.assertEqual(len(model.channels    )    ,      len(config.out_features    )    )
        self.parent.assertListEqual(model.channels    ,      config.hidden_sizes[1:]    )

        # verify backbone works with out_features=None
        lowerCAmelCase     =			None
        lowerCAmelCase     =			BitBackbone(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps    )    ,      1    )
        self.parent.assertListEqual(list(result.feature_maps[0].shape    )    ,      [self.batch_size, self.hidden_sizes[-1], 1, 1]    )

        # verify channels
        self.parent.assertEqual(len(model.channels    )    ,      1    )
        self.parent.assertListEqual(model.channels    ,      [config.hidden_sizes[-1]]    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[Any]:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			config_and_inputs
        lowerCAmelCase     =			{'pixel_values': pixel_values}
        return config, inputs_dict




@require_torch
class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	List[str]		=     (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
     lowerCamelCase				:	Union[str, Any]		=     (
         {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
         if is_torch_available()
         else {}
     )

     lowerCamelCase				:	str		=     False
     lowerCamelCase				:	List[Any]		=     False
     lowerCamelCase				:	Dict		=     False
     lowerCamelCase				:	List[str]		=     False
     lowerCamelCase				:	List[Any]		=     False





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Tuple:
        lowerCAmelCase     =			BitModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    ,      has_text_modality=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[Any]:
        self.create_and_test_config_common_properties()
        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 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Tuple:
        return





     @unittest.skip(reason='Bit does not output attentions'    )
     def 							__UpperCAmelCase      ( self       :      Any    )				->  Tuple:
        pass





     @unittest.skip(reason='Bit does not use inputs_embeds'    )
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Optional[int]:
        pass





     @unittest.skip(reason='Bit does not support input and output embeddings'    )
     def 							__UpperCAmelCase      ( self       :      Any    )				->  str:
        pass





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

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

           lowerCAmelCase     =			['pixel_values']
           self.assertListEqual(arg_names[:1]    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  str:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(config=UpperCAmelCase__    )
           for name, module in model.named_modules():
              if isinstance(UpperCAmelCase__    ,      (nn.BatchNormad, nn.GroupNorm)    ):
                 self.assertTrue(
                     torch.all(module.weight == 1    )    ,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,      )
                 self.assertTrue(
                     torch.all(module.bias == 0    )    ,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,      )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Any:
        def check_hidden_states_output(UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    ):
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           model.to(UpperCAmelCase__    )
           model.eval()

           with torch.no_grad():
              lowerCAmelCase     =			model(**self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )    )

           lowerCAmelCase     =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           lowerCAmelCase     =			self.model_tester.num_stages
           self.assertEqual(len(UpperCAmelCase__    )    ,      expected_num_stages + 1    )

           # Bit's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]    )    ,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,      )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase     =			['preactivation', 'bottleneck']
        for model_class in self.all_model_classes:
           for layer_type in layers_type:
              lowerCAmelCase     =			layer_type
              lowerCAmelCase     =			True
              check_hidden_states_output(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              lowerCAmelCase     =			True

              check_hidden_states_output(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )





     @unittest.skip(reason='Bit does not use feedforward chunking'    )
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Any:
        pass





     def 							__UpperCAmelCase      ( self       :      str    )				->  Any:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Dict:
        for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     =			BitModel.from_pretrained(UpperCAmelCase__    )
           self.assertIsNotNone(UpperCAmelCase__    )





def 	a_					( ):
   lowerCAmelCase     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
   return image

@require_torch
@require_vision
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @cached_property
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Dict:
        return (
            BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ) if is_vision_available() else None
        )





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Any:
        lowerCAmelCase     =			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(UpperCAmelCase__    )

        lowerCAmelCase     =			self.default_image_processor
        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='pt'    ).to(UpperCAmelCase__    )

        # forward pass
        with torch.no_grad():
           lowerCAmelCase     =			model(**UpperCAmelCase__    )

        # verify the logits
        lowerCAmelCase     =			torch.Size((1, 1_0_0_0)    )
        self.assertEqual(outputs.logits.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]    ).to(UpperCAmelCase__    )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )




@require_torch
class 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     (BitBackbone,) if is_torch_available() else ()
     lowerCamelCase				:	Optional[int]		=     BitConfig

     lowerCamelCase				:	Optional[Any]		=     False





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  int:
        lowerCAmelCase     =			BitModelTester(self    )












"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]					) ->str:






	'''simple docstring'''






	a      :		Union[str, Any]	=				os.path.join(args.tf_model_dir    ,       "parameters.json"					)
	a      :		str	=				json.loads(open(_lowercase					).read()					)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""					)
	if not args.output.endswith(".pt"					):
		a      :		str	=				args.output + ".pt"
	a      :		Dict	=				OrderedDict()
	with tf.device("/CPU:0"					):
		a      :		Optional[int]	=				tf.train.load_checkpoint(args.tf_model_dir					)
		a      :		Optional[Any]	=				reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			a      :		Dict	=				reader.get_tensor(_lowercase					).astype(np.floataa					)
			if key_name.endswith("/adam_m"					) or key_name.endswith("/adam_v"					):
				continue
			if key_name.startswith("pasts/"					):
				if key_name.startswith("pasts/mlp"					):
					a      :		Union[str, Any]	=				int(key_name[9]					)
				elif key_name.startswith("pasts/out"					):
					a      :		Optional[int]	=				8
				a      :		Dict	=				"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				a      :		int	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/moe"					):
				a      :		List[str]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/switch_gating/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/softmlp/kernel"					):
					a      :		Optional[int]	=				"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/wo/kernel"					) or key_name.endswith("/wi/kernel"					):
					a      :		Any	=				key_name[-9:-7]
					for i in range(16					):
						a      :		List[Any]	=				"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
						a      :		str	=				(
						    vnp[i].transpose([1, 0]					).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/mlp"					):
				a      :		Union[str, Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/p1/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.weight" % player
					a      :		Dict	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p1/bias"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.bias" % player
					a      :		List[Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/kernel"					):
					a      :		Union[str, Any]	=				"model.blocks.%d.feed_forward.mlp.wo.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/bias"					):
					a      :		Dict	=				"model.blocks.%d.feed_forward.mlp.wo.bias" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		str	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/ln"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.bias" % player
					a      :		Tuple	=				vnp.copy()  # same because it is one dimensional
					a      :		int	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.weight" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/att"					):
				a      :		Optional[Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/qkv/kernel"					):
					a      :		Union[str, Any]	=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					a      :		List[str]	=				state[:, 0, :, :]
					a      :		Dict	=				state[:, 1, :, :]
					a      :		Union[str, Any]	=				state[:, 2, :, :]
					a      :		str	=				(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Dict	=				(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]					)
					    .transpose([1, 0]					)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
					a      :		Union[str, Any]	=				torch.tensor(_lowercase					)
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					a      :		List[str]	=				torch.tensor(_lowercase					)
					a      :		Optional[Any]	=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					a      :		Optional[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/o/kernel"					):
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
					a      :		Optional[int]	=				(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/an"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[int]	=				"model.blocks.%d.self_attn.norm.bias" % player
					a      :		Union[str, Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Any	=				"model.blocks.%d.self_attn.norm.weight" % player
					a      :		str	=				vnp.copy()  # same because it is one dimensional
					a      :		Any	=				torch.tensor(_lowercase					)
			elif (
			    key_name.startswith("model/wte"					)
			    or key_name.startswith("model/wpe"					)
			    or key_name.startswith("model/ete"					)
			):
				a      :		Optional[int]	=				{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
				    key_name[-3:]
				]
				a      :		Tuple	=				"model.%s.weight" % nlayer
				a      :		Any	=				vnp.copy()  # same in embedded
				a      :		Tuple	=				torch.tensor(_lowercase					)
				if key_name.startswith("model/wte"					):
					a      :		Optional[int]	=				"lm_head.weight"
					a      :		Optional[int]	=				vnp.copy()  # same in embedded
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/wob"					):
				a      :		Optional[int]	=				"final_logits_bias"
				a      :		Optional[Any]	=				vnp.copy()  # same in embedded
				a      :		Optional[int]	=				state.reshape((1, -1)					)
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense/kernel":
				a      :		Optional[int]	=				"model.last_project.weight"
				a      :		Union[str, Any]	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense_1/bias":
				a      :		Dict	=				"model.last_project.bias"
				a      :		Optional[Any]	=				vnp.copy()  # same because it is one dimensional
				a      :		Any	=				torch.tensor(_lowercase					)
	torch.save(_lowercase    ,       args.output					)


if __name__ == "__main__":
		a						: Optional[int]     	=			argparse.ArgumentParser(
		    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
		parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
		a						: Tuple     	=			parser.parse_args()
		convert_tf_gptsan_to_pt(args)


