



'''simple docstring'''
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					snake_case_					:      Tuple    			=    'platform'

					import jax
					import jax.numpy as jnp

					from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
					    FlaxBlenderbotSmallForConditionalGeneration,
					    FlaxBlenderbotSmallModel,
					    shift_tokens_right,
					)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=None    ,    ):
						if attention_mask is None:
												_UpperCamelCase   :						Any												=  np.where(input_ids != config.pad_token_id    ,    1    ,    0				)
						if decoder_attention_mask is None:
												_UpperCamelCase   :						Union[str, Any]												=  np.where(decoder_input_ids != config.pad_token_id    ,    1    ,    0				)
						if head_mask is None:
												_UpperCamelCase   :						Optional[int]												=  np.ones((config.encoder_layers, config.encoder_attention_heads)				)
						if decoder_head_mask is None:
												_UpperCamelCase   :						Union[str, Any]												=  np.ones((config.decoder_layers, config.decoder_attention_heads)				)
						if cross_attn_head_mask is None:
												_UpperCamelCase   :						Optional[int]												=  np.ones((config.decoder_layers, config.decoder_attention_heads)				)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}






class 				lowercase__      :





	def __init__(					self   :							str					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							Optional[Any]=13					,lowerCamelCase__   :							str=7					,lowerCamelCase__   :							Union[str, Any]=True					,lowerCamelCase__   :							Optional[int]=False					,lowerCamelCase__   :							List[str]=99					,lowerCamelCase__   :							List[str]=16					,lowerCamelCase__   :							str=2					,lowerCamelCase__   :							Dict=4					,lowerCamelCase__   :							List[str]=4					,lowerCamelCase__   :							str="gelu"					,lowerCamelCase__   :							int=0.1					,lowerCamelCase__   :							str=0.1					,lowerCamelCase__   :							Dict=32					,lowerCamelCase__   :							Tuple=2					,lowerCamelCase__   :							str=1					,lowerCamelCase__   :							int=0					,lowerCamelCase__   :							List[Any]=0.0_2					,):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  parent
							_UpperCamelCase   :						str												=  batch_size
							_UpperCamelCase   :						Optional[Any]												=  seq_length
							_UpperCamelCase   :						List[str]												=  is_training
							_UpperCamelCase   :						str												=  use_labels
							_UpperCamelCase   :						Optional[Any]												=  vocab_size
							_UpperCamelCase   :						Union[str, Any]												=  hidden_size
							_UpperCamelCase   :						List[str]												=  num_hidden_layers
							_UpperCamelCase   :						Tuple												=  num_attention_heads
							_UpperCamelCase   :						List[Any]												=  intermediate_size
							_UpperCamelCase   :						Optional[int]												=  hidden_act
							_UpperCamelCase   :						Optional[int]												=  hidden_dropout_prob
							_UpperCamelCase   :						Union[str, Any]												=  attention_probs_dropout_prob
							_UpperCamelCase   :						Dict												=  max_position_embeddings
							_UpperCamelCase   :						Tuple												=  eos_token_id
							_UpperCamelCase   :						Optional[Any]												=  pad_token_id
							_UpperCamelCase   :						Dict												=  bos_token_id
							_UpperCamelCase   :						Tuple												=  initializer_range





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,self.vocab_size					)					,3					,self.vocab_size					)
							_UpperCamelCase   :						List[str]												=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,dtype=np.intaa					))					,-1					)

							_UpperCamelCase   :						int												=  shift_tokens_right(lowerCamelCase__					,1					,2					)

							_UpperCamelCase   :						str												=  BlenderbotSmallConfig(
							    vocab_size=self.vocab_size					,d_model=self.hidden_size					,encoder_layers=self.num_hidden_layers					,decoder_layers=self.num_hidden_layers					,encoder_attention_heads=self.num_attention_heads					,decoder_attention_heads=self.num_attention_heads					,encoder_ffn_dim=self.intermediate_size					,decoder_ffn_dim=self.intermediate_size					,dropout=self.hidden_dropout_prob					,attention_dropout=self.attention_probs_dropout_prob					,max_position_embeddings=self.max_position_embeddings					,eos_token_id=self.eos_token_id					,bos_token_id=self.bos_token_id					,pad_token_id=self.pad_token_id					,initializer_range=self.initializer_range					,use_cache=lowerCamelCase__					,)
							_UpperCamelCase   :						List[Any]												=  prepare_blenderbot_inputs_dict(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)
							return config, inputs_dict





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						List[Any]												=  self.prepare_config_and_inputs()
							return config, inputs_dict





	def       UpperCamelCase_    (					self   :							Optional[int]					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  20
							_UpperCamelCase   :						Optional[Any]												=  model_class_name(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  model.encode(inputs_dict['input_ids']					)

							_UpperCamelCase							,     _UpperCamelCase   :						Optional[int]												=  (
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							_UpperCamelCase   :						List[Any]												=  model.init_cache(decoder_input_ids.shape[0]					,lowerCamelCase__					,lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,dtype='i4'					)

							_UpperCamelCase   :						Optional[Any]												=  jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]					,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,)
							_UpperCamelCase   :						Tuple												=  model.decode(
							    decoder_input_ids[:, :-1]					,lowerCamelCase__					,decoder_attention_mask=lowerCamelCase__					,past_key_values=lowerCamelCase__					,decoder_position_ids=lowerCamelCase__					,)

							_UpperCamelCase   :						Optional[Any]												=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,dtype='i4'					)
							_UpperCamelCase   :						Dict												=  model.decode(
							    decoder_input_ids[:, -1:]					,lowerCamelCase__					,decoder_attention_mask=lowerCamelCase__					,past_key_values=outputs_cache.past_key_values					,decoder_position_ids=lowerCamelCase__					,)

							_UpperCamelCase   :						str												=  model.decode(lowerCamelCase__					,lowerCamelCase__					)

							_UpperCamelCase   :						Union[str, Any]												=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
							self.parent.assertTrue(diff < 1E-3					,msg=F'Max diff is {diff}'					)





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							str					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  20
							_UpperCamelCase   :						Optional[int]												=  model_class_name(lowerCamelCase__					)

							_UpperCamelCase   :						int												=  model.encode(inputs_dict['input_ids']					)

							_UpperCamelCase							,     _UpperCamelCase   :						List[Any]												=  (
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							_UpperCamelCase   :						int												=  jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
							    ]					,axis=-1					,)

							_UpperCamelCase   :						Optional[Any]												=  model.init_cache(decoder_input_ids.shape[0]					,lowerCamelCase__					,lowerCamelCase__					)
							_UpperCamelCase   :						str												=  jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]					,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,)

							_UpperCamelCase   :						List[str]												=  model.decode(
							    decoder_input_ids[:, :-1]					,lowerCamelCase__					,decoder_attention_mask=lowerCamelCase__					,past_key_values=lowerCamelCase__					,decoder_position_ids=lowerCamelCase__					,)
							_UpperCamelCase   :						Dict												=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,dtype='i4'					)
							_UpperCamelCase   :						Any												=  model.decode(
							    decoder_input_ids[:, -1:]					,lowerCamelCase__					,past_key_values=outputs_cache.past_key_values					,decoder_attention_mask=lowerCamelCase__					,decoder_position_ids=lowerCamelCase__					,)

							_UpperCamelCase   :						Optional[int]												=  model.decode(lowerCamelCase__					,lowerCamelCase__					,decoder_attention_mask=lowerCamelCase__					)

							_UpperCamelCase   :						List[str]												=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
							self.parent.assertTrue(diff < 1E-3					,msg=F'Max diff is {diff}'					)






@require_flax
class 				lowercase__      (				unittest.TestCase  ):
	lowercase__    							=     99





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ]					,dtype=np.intaa					,)

							_UpperCamelCase   :						List[Any]												=  input_ids.shape[0]
							_UpperCamelCase   :						int												=  BlenderbotSmallConfig(
							    vocab_size=self.vocab_size					,d_model=24					,encoder_layers=2					,decoder_layers=2					,encoder_attention_heads=2					,decoder_attention_heads=2					,encoder_ffn_dim=32					,decoder_ffn_dim=32					,max_position_embeddings=48					,eos_token_id=2					,pad_token_id=1					,bos_token_id=0					,)
							return config, input_ids, batch_size





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Dict												=  self._get_config_and_data()
							_UpperCamelCase   :						Optional[Any]												=  FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  lm_model(input_ids=lowerCamelCase__					)
							_UpperCamelCase   :						str												=  (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['logits'].shape					,lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  BlenderbotSmallConfig(
							    vocab_size=self.vocab_size					,d_model=14					,encoder_layers=2					,decoder_layers=2					,encoder_attention_heads=2					,decoder_attention_heads=2					,encoder_ffn_dim=8					,decoder_ffn_dim=8					,max_position_embeddings=48					,)
							_UpperCamelCase   :						Optional[int]												=  FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__					)
							_UpperCamelCase   :						int												=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,dtype=np.intaa					)
							_UpperCamelCase   :						Tuple												=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,dtype=np.intaa					)
							_UpperCamelCase   :						Optional[Any]												=  lm_model(input_ids=lowerCamelCase__					,decoder_input_ids=lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  (*summary.shape, config.vocab_size)
							self.assertEqual(outputs['logits'].shape					,lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,dtype=np.intaa					)
							_UpperCamelCase   :						str												=  shift_tokens_right(lowerCamelCase__					,1					,2					)
							_UpperCamelCase   :						Dict												=  np.equal(lowerCamelCase__					,1					).astype(np.floataa					).sum()
							_UpperCamelCase   :						Union[str, Any]												=  np.equal(lowerCamelCase__					,1					).astype(np.floataa					).sum()
							self.assertEqual(shifted.shape					,input_ids.shape					)
							self.assertEqual(lowerCamelCase__					,n_pad_before - 1					)
							self.assertTrue(np.equal(shifted[:, 0]					,2					).all()					)






@require_flax
class 				lowercase__      (				lowercase		,   unittest.TestCase		,   lowercase  ):
	lowercase__    							=     True
	lowercase__    							=     (
	    (
	        FlaxBlenderbotSmallModel,
	        FlaxBlenderbotSmallForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	lowercase__    							=     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  FlaxBlenderbotSmallModelTester(self					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						Any												=  self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						Any												=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__					):
																			_UpperCamelCase   :						Optional[Any]												=  self._prepare_for_class(lowerCamelCase__					,lowerCamelCase__					)
																			_UpperCamelCase   :						Union[str, Any]												=  model_class(lowerCamelCase__					)

																			@jax.jit
																			def encode_jitted(lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							Any=None					,**lowerCamelCase__   :							List[str]					):
																									return model.encode(input_ids=lowerCamelCase__					,attention_mask=lowerCamelCase__					)

																			with self.subTest('JIT Enabled'					):
																									_UpperCamelCase   :						Any												=  encode_jitted(**lowerCamelCase__					).to_tuple()

																			with self.subTest('JIT Disabled'					):
																									with jax.disable_jit():
																															_UpperCamelCase   :						List[Any]												=  encode_jitted(**lowerCamelCase__					).to_tuple()

																			self.assertEqual(len(lowerCamelCase__					)					,len(lowerCamelCase__					)					)
																			for jitted_output, output in zip(lowerCamelCase__					,lowerCamelCase__					):
																									self.assertEqual(jitted_output.shape					,output.shape					)





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__					):
																			_UpperCamelCase   :						Tuple												=  model_class(lowerCamelCase__					)
																			_UpperCamelCase   :						List[Any]												=  model.encode(inputs_dict['input_ids']					,inputs_dict['attention_mask']					)

																			_UpperCamelCase   :						Optional[int]												=  {
																			    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																			    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																			    'encoder_outputs': encoder_outputs,
																			}

																			@jax.jit
																			def decode_jitted(lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Dict					,lowerCamelCase__   :							Optional[Any]					):
																									return model.decode(
																									    decoder_input_ids=lowerCamelCase__					,decoder_attention_mask=lowerCamelCase__					,encoder_outputs=lowerCamelCase__					,)

																			with self.subTest('JIT Enabled'					):
																									_UpperCamelCase   :						Dict												=  decode_jitted(**lowerCamelCase__					).to_tuple()

																			with self.subTest('JIT Disabled'					):
																									with jax.disable_jit():
																															_UpperCamelCase   :						List[Any]												=  decode_jitted(**lowerCamelCase__					).to_tuple()

																			self.assertEqual(len(lowerCamelCase__					)					,len(lowerCamelCase__					)					)
																			for jitted_output, output in zip(lowerCamelCase__					,lowerCamelCase__					):
																									self.assertEqual(jitted_output.shape					,output.shape					)





	@slow
	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							for model_class_name in self.all_model_classes:
													_UpperCamelCase   :						List[Any]												=  model_class_name.from_pretrained('facebook/blenderbot_small-90M'					)
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													_UpperCamelCase   :						Optional[Any]												=  np.ones((1, 1)					) * model.config.eos_token_id
													_UpperCamelCase   :						Optional[int]												=  model(lowerCamelCase__					)
													self.assertIsNotNone(lowerCamelCase__					)







'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def A__       (	UpperCAmelCase_				):
						if "model" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('model.'    ,    ''				)
						if "norm1" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('norm1'    ,    'attention.output.LayerNorm'				)
						if "norm2" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('norm2'    ,    'output.LayerNorm'				)
						if "norm" in orig_key:
												_UpperCamelCase   :						str												=  orig_key.replace('norm'    ,    'LayerNorm'				)
						if "transformer" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.split('.'				)[0].split('_'				)[-1]
												_UpperCamelCase   :						Tuple												=  orig_key.replace(f'transformer_{layer_num}'    ,    f'encoder.layer.{layer_num}'				)
						if "mha.attn" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('mha.attn'    ,    'attention.self'				)
						if "mha" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('mha'    ,    'attention'				)
						if "W_q" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('W_q'    ,    'self.query'				)
						if "W_k" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_k'    ,    'self.key'				)
						if "W_v" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_v'    ,    'self.value'				)
						if "ff1" in orig_key:
												_UpperCamelCase   :						Optional[Any]												=  orig_key.replace('ff1'    ,    'intermediate.dense'				)
						if "ff2" in orig_key:
												_UpperCamelCase   :						List[str]												=  orig_key.replace('ff2'    ,    'output.dense'				)
						if "ff" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('ff'    ,    'output.dense'				)
						if "mlm_class" in orig_key:
												_UpperCamelCase   :						Tuple												=  orig_key.replace('mlm.mlm_class'    ,    'cls.predictions.decoder'				)
						if "mlm" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('mlm'    ,    'cls.predictions.transform'				)
						if "cls" not in orig_key:
												_UpperCamelCase   :						Optional[int]												=  'yoso.' + orig_key

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

						print(model.load_state_dict(UpperCAmelCase_				)				)
						model.eval()
						model.save_pretrained(UpperCAmelCase_				)

						print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'				)


if __name__ == "__main__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


