'''simple docstring'''

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    LevitForImageClassification,
							    LevitForImageClassificationWithTeacher,
							    LevitModel,
							)
							from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								_lowerCAmelCase										=self.config_class(**self.inputs_dict				)
								self.parent.assertTrue(hasattr(__A		,						'hidden_sizes'				)				)
								self.parent.assertTrue(hasattr(__A		,						'num_attention_heads'				)				)

class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=13		,						__A=64		,						__A=3		,						__A=3		,						__A=2		,						__A=1		,						__A=16		,						__A=[128, 256, 384]		,						__A=[4, 6, 8]		,						__A=[2, 3, 4]		,						__A=[16, 16, 16]		,						__A=0		,						__A=[2, 2, 2]		,						__A=[2, 2, 2]		,						__A=0.02		,						__A=True		,						__A=True		,						__A=2		,						) ->     Optional[Any]:
								_lowerCAmelCase										=parent
								_lowerCAmelCase										=batch_size
								_lowerCAmelCase										=image_size
								_lowerCAmelCase										=num_channels
								_lowerCAmelCase										=kernel_size
								_lowerCAmelCase										=stride
								_lowerCAmelCase										=padding
								_lowerCAmelCase										=hidden_sizes
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=depths
								_lowerCAmelCase										=key_dim
								_lowerCAmelCase										=drop_path_rate
								_lowerCAmelCase										=patch_size
								_lowerCAmelCase										=attention_ratio
								_lowerCAmelCase										=mlp_ratio
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=[
								    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]
								_lowerCAmelCase										=is_training
								_lowerCAmelCase										=use_labels
								_lowerCAmelCase										=num_labels
								_lowerCAmelCase										=initializer_range






		def 		UpperCamelCase__       (   self				) ->     int:
								_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				) ->     List[str]:
								return LevitConfig(
								    image_size=self.image_size		,						num_channels=self.num_channels		,						kernel_size=self.kernel_size		,						stride=self.stride		,						padding=self.padding		,						patch_size=self.patch_size		,						hidden_sizes=self.hidden_sizes		,						num_attention_heads=self.num_attention_heads		,						depths=self.depths		,						key_dim=self.key_dim		,						drop_path_rate=self.drop_path_rate		,						mlp_ratio=self.mlp_ratio		,						attention_ratio=self.attention_ratio		,						initializer_range=self.initializer_range		,						down_ops=self.down_ops		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										=LevitModel(config=__A				)
								model.to(__A				)
								model.eval()
								_lowerCAmelCase										=model(__A				)
								_lowerCAmelCase										=(self.image_size, self.image_size)
								_lowerCAmelCase						,				_lowerCAmelCase										=image_size[0], image_size[1]
								for _ in range(4				):
														_lowerCAmelCase										=floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
														_lowerCAmelCase										=floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,						(self.batch_size, ceil(height / 4				) * ceil(width / 4				), self.hidden_sizes[-1])		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     int:
								_lowerCAmelCase										=self.num_labels
								_lowerCAmelCase										=LevitForImageClassification(__A				)
								model.to(__A				)
								model.eval()
								_lowerCAmelCase										=model(__A		,						labels=__A				)
								self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)				)






		def 		UpperCamelCase__       (   self				) ->     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   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      List[Any]       =							(
		    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
		    if is_torch_available()
		    else ()
		)
		lowercase   :      Optional[Any]       =							(
		    {
		        'feature-extraction': LevitModel,
		        'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
		    }
		    if is_torch_available()
		    else {}
		)

		lowercase   :      Union[str, Any]       =							False
		lowercase   :      List[Any]       =							False
		lowercase   :      str       =							False
		lowercase   :      int       =							False
		lowercase   :      Dict       =							False






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										=LevitModelTester(self				)
								_lowerCAmelCase										=ConfigTester(self		,						config_class=__A		,						has_text_modality=__A		,						hidden_size=37				)






		def 		UpperCamelCase__       (   self				) ->     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[int]:
								return






		@unittest.skip(reason='Levit does not use inputs_embeds'				)
		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								pass






		@unittest.skip(reason='Levit does not support input and output embeddings'				)
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								pass






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






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														_lowerCAmelCase										=model_class(__A				)
														_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]		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     str:
								def check_hidden_states_output(__A		,						__A		,						__A				):
														_lowerCAmelCase										=model_class(__A				)
														model.to(__A				)
														model.eval()

														with torch.no_grad():
																				_lowerCAmelCase										=model(**self._prepare_for_class(__A		,						__A				)				)

														_lowerCAmelCase										=outputs.hidden_states

														_lowerCAmelCase										=len(self.model_tester.depths				) + 1
														self.assertEqual(len(__A				)		,						__A				)

														_lowerCAmelCase										=(self.model_tester.image_size, self.model_tester.image_size)
														_lowerCAmelCase						,				_lowerCAmelCase										=image_size[0], image_size[1]
														for _ in range(4				):
																				_lowerCAmelCase										=floor(
																				    (
																				        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																				        / self.model_tester.stride
																				    )
																				    + 1				)
																				_lowerCAmelCase										=floor(
																				    (
																				        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																				        / self.model_tester.stride
																				    )
																				    + 1				)
														# verify the first hidden states (first block)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]				)		,						[
														        height * width,
														        self.model_tester.hidden_sizes[0],
														    ]		,						)

								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														_lowerCAmelCase										=True
														check_hidden_states_output(__A		,						__A		,						__A				)

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

														check_hidden_states_output(__A		,						__A		,						__A				)






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






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A=False				) ->     Any:
								_lowerCAmelCase										=super()._prepare_for_class(__A		,						__A		,						return_labels=__A				)

								if return_labels:
														if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																				del inputs_dict["labels"]

								return inputs_dict






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__A				)






		def 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__A				)






		def 		UpperCamelCase__       (   self				) ->     str:
								if not self.model_tester.is_training:
														return

								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()
								_lowerCAmelCase										=True

								for model_class in self.all_model_classes:
														# LevitForImageClassificationWithTeacher supports inference-only
														if (
														    model_class in get_values(__A				)
														    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
														):
																				continue
														_lowerCAmelCase										=model_class(__A				)
														model.to(__A				)
														model.train()
														_lowerCAmelCase										=self._prepare_for_class(__A		,						__A		,						return_labels=__A				)
														_lowerCAmelCase										=model(**__A				).loss
														loss.backward()






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
														return

								_lowerCAmelCase										=False
								_lowerCAmelCase										=True

								for model_class in self.all_model_classes:
														if model_class in get_values(__A				) or not model_class.supports_gradient_checkpointing:
																				continue
														# LevitForImageClassificationWithTeacher supports inference-only
														if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																				continue
														_lowerCAmelCase										=model_class(__A				)
														model.gradient_checkpointing_enable()
														model.to(__A				)
														model.train()
														_lowerCAmelCase										=self._prepare_for_class(__A		,						__A		,						return_labels=__A				)
														_lowerCAmelCase										=model(**__A				).loss
														loss.backward()






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

								_lowerCAmelCase										=[
								    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
								    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
								    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
								]

								for model_class in self.all_model_classes:
														if (
														    model_class
														    not in [
														        *get_values(__A				),
														    ]
														    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
														):
																				continue

														for problem_type in problem_types:
																				with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''				):
																										_lowerCAmelCase										=problem_type['title']
																										_lowerCAmelCase										=problem_type['num_labels']

																										_lowerCAmelCase										=model_class(__A				)
																										model.to(__A				)
																										model.train()

																										_lowerCAmelCase										=self._prepare_for_class(__A		,						__A		,						return_labels=__A				)

																										if problem_type["num_labels"] > 1:
																																_lowerCAmelCase										=inputs['labels'].unsqueeze(1				).repeat(1		,						problem_type['num_labels']				)

																										_lowerCAmelCase										=inputs['labels'].to(problem_type['dtype']				)

																										# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																										# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																										# they have the same size." which is a symptom something in wrong for the regression problem.
																										# See https://github.com/huggingface/transformers/issues/11780
																										with warnings.catch_warnings(record=__A				) as warning_list:
																																_lowerCAmelCase										=model(**__A				).loss
																										for w in warning_list:
																																if "Using a target size that is different to the input size" in str(w.message				):
																																						raise ValueError(
																																						    F'''Something is going wrong in the regression problem: intercepted {w.message}'''				)

																										loss.backward()






		@slow
		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCAmelCase										=LevitModel.from_pretrained(__A				)
														self.assertIsNotNone(__A				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
						return image


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







		"""simple docstring"""






		@cached_property
		def 		UpperCamelCase__       (   self				) ->     Any:
								return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)






		@slow
		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
								    __A				)

								_lowerCAmelCase										=self.default_image_processor
								_lowerCAmelCase										=prepare_img()
								_lowerCAmelCase										=image_processor(images=__A		,						return_tensors='pt'				).to(__A				)

								# forward pass
								with torch.no_grad():
														_lowerCAmelCase										=model(**__A				)

								# verify the logits
								_lowerCAmelCase										=torch.Size((1, 1000)				)
								self.assertEqual(outputs.logits.shape		,						__A				)

								_lowerCAmelCase										=torch.tensor([1.0_448, -0.3_745, -1.8_317]				).to(__A				)

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






'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=os.path.join(args.tf_model_dir			,      'parameters.json'      )
						_lowerCAmelCase										=json.loads(open(a__      ).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'      ):
												_lowerCAmelCase										=args.output + '.pt'
						_lowerCAmelCase										=OrderedDict()
						with tf.device('/CPU:0'      ):
												_lowerCAmelCase										=tf.train.load_checkpoint(args.tf_model_dir      )
												_lowerCAmelCase										=reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		_lowerCAmelCase										=reader.get_tensor(a__      ).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'      ):
																														_lowerCAmelCase										=int(key_name[9]      )
																								elif key_name.startswith('pasts/out'      ):
																														_lowerCAmelCase										=8
																								_lowerCAmelCase										='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/moe'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/switch_gating/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/softmlp/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
																														_lowerCAmelCase										=key_name[-9:-7]
																														for i in range(1_6      ):
																																				_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
																																				_lowerCAmelCase										=(
																																				    vnp[i].transpose([1, 0]      ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/mlp'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/p1/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p1/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/ln'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/att'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/qkv/kernel'      ):
																														_lowerCAmelCase										=vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														_lowerCAmelCase										=state[:, 0, :, :]
																														_lowerCAmelCase										=state[:, 1, :, :]
																														_lowerCAmelCase										=state[:, 2, :, :]
																														_lowerCAmelCase										=(
																														    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
																														_lowerCAmelCase										=(
																														    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
																														_lowerCAmelCase										=(
																														    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
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/o/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
																														_lowerCAmelCase										=(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/an'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif (
																		    key_name.startswith('model/wte'      )
																		    or key_name.startswith('model/wpe'      )
																		    or key_name.startswith('model/ete'      )
																		):
																								_lowerCAmelCase										={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
																								    key_name[-3:]
																								]
																								_lowerCAmelCase										='model.%s.weight' % nlayer
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=torch.tensor(a__      )
																								if key_name.startswith('model/wte'      ):
																														_lowerCAmelCase										='lm_head.weight'
																														_lowerCAmelCase										=vnp.copy()  # same in embedded
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/wob'      ):
																								_lowerCAmelCase										='final_logits_bias'
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=state.reshape((1, -1)      )
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense/kernel":
																								_lowerCAmelCase										='model.last_project.weight'
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense_1/bias":
																								_lowerCAmelCase										='model.last_project.bias'
																								_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																								_lowerCAmelCase										=torch.tensor(a__      )
						torch.save(a__			,      args.output      )


if __name__ == "__main__":
							lowercase_     		= 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''')
							lowercase_     		= parser.parse_args()
							convert_tf_gptsan_to_pt(args)





