


'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TFDebertaForMaskedLM,
	    TFDebertaForQuestionAnswering,
	    TFDebertaForSequenceClassification,
	    TFDebertaForTokenClassification,
	    TFDebertaModel,
	)






class        UpperCamelCase__:




				def __init__(		self			: Optional[Any]      ,    lowerCAmelCase			: List[str]      ,    lowerCAmelCase			: Tuple=13      ,    lowerCAmelCase			: Dict=7      ,    lowerCAmelCase			: List[str]=True      ,    lowerCAmelCase			: List[str]=True      ,    lowerCAmelCase			: List[str]=True      ,    lowerCAmelCase			: int=True      ,    lowerCAmelCase			: Optional[Any]=99      ,    lowerCAmelCase			: Union[str, Any]=32      ,    lowerCAmelCase			: int=2      ,    lowerCAmelCase			: Tuple=4      ,    lowerCAmelCase			: Dict=37      ,    lowerCAmelCase			: Optional[Any]="gelu"      ,    lowerCAmelCase			: List[Any]=0.1      ,    lowerCAmelCase			: Dict=0.1      ,    lowerCAmelCase			: Tuple=512      ,    lowerCAmelCase			: Union[str, Any]=16      ,    lowerCAmelCase			: List[Any]=2      ,    lowerCAmelCase			: Optional[int]=0.02      ,    lowerCAmelCase			: List[str]=3      ,    lowerCAmelCase			: Optional[Any]=4      ,    lowerCAmelCase			: int=None      ,    )->     Optional[int]:

										"""simple docstring"""




										UpperCAmelCase     				=	parent
										UpperCAmelCase     				=	13
										UpperCAmelCase     				=	7
										UpperCAmelCase     				=	True
										UpperCAmelCase     				=	True
										UpperCAmelCase     				=	True
										UpperCAmelCase     				=	True
										UpperCAmelCase     				=	99
										UpperCAmelCase     				=	32
										UpperCAmelCase     				=	2
										UpperCAmelCase     				=	4
										UpperCAmelCase     				=	37
										UpperCAmelCase     				=	"gelu"
										UpperCAmelCase     				=	0.1
										UpperCAmelCase     				=	0.1
										UpperCAmelCase     				=	512
										UpperCAmelCase     				=	16
										UpperCAmelCase     				=	False
										UpperCAmelCase     				=	-1
										UpperCAmelCase     				=	True
										UpperCAmelCase     				=	2
										UpperCAmelCase     				=	0.02
										UpperCAmelCase     				=	3
										UpperCAmelCase     				=	4
										UpperCAmelCase     				=	None




				def      a__(		self			: List[str]      )->     Dict:

										"""simple docstring"""




										UpperCAmelCase     				=	ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size      )

										UpperCAmelCase     				=	None
										if self.use_input_mask:
																UpperCAmelCase     				=	random_attention_mask([self.batch_size, self.seq_length]      )

										UpperCAmelCase     				=	None
										if self.use_token_type_ids:
																UpperCAmelCase     				=	ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size      )

										UpperCAmelCase     				=	None
										UpperCAmelCase     				=	None
										UpperCAmelCase     				=	None
										if self.use_labels:
																UpperCAmelCase     				=	ids_tensor([self.batch_size]      ,    self.type_sequence_label_size      )
																UpperCAmelCase     				=	ids_tensor([self.batch_size, self.seq_length]      ,    self.num_labels      )

										UpperCAmelCase     				=	DebertaConfig(
										    vocab_size=self.vocab_size      ,    hidden_size=self.hidden_size      ,    num_hidden_layers=self.num_hidden_layers      ,    num_attention_heads=self.num_attention_heads      ,    intermediate_size=self.intermediate_size      ,    hidden_act=self.hidden_act      ,    hidden_dropout_prob=self.hidden_dropout_prob      ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,    max_position_embeddings=self.max_position_embeddings      ,    type_vocab_size=self.type_vocab_size      ,    relative_attention=self.relative_attention      ,    max_relative_positions=self.max_relative_positions      ,    position_biased_input=self.position_biased_input      ,    initializer_range=self.initializer_range      ,    return_dict=__a      ,    )

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




				def      a__(		self			: int      ,    lowerCAmelCase			: Any      ,    lowerCAmelCase			: Optional[Any]      ,    lowerCAmelCase			: List[Any]      ,    lowerCAmelCase			: Optional[Any]      ,    lowerCAmelCase			: int      ,    lowerCAmelCase			: Optional[Any]      ,    lowerCAmelCase			: Any      )->     Tuple:

										"""simple docstring"""




										UpperCAmelCase     				=	TFDebertaModel(config=__a      )
										UpperCAmelCase     				=	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

										UpperCAmelCase     				=	[input_ids, input_mask]
										UpperCAmelCase     				=	model(__a      )

										UpperCAmelCase     				=	model(__a      )

										self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size)      )




				def      a__(		self			: Union[str, Any]      ,    lowerCAmelCase			: List[str]      ,    lowerCAmelCase			: Union[str, Any]      ,    lowerCAmelCase			: List[Any]      ,    lowerCAmelCase			: Tuple      ,    lowerCAmelCase			: List[str]      ,    lowerCAmelCase			: List[Any]      ,    lowerCAmelCase			: str      )->     Dict:

										"""simple docstring"""




										UpperCAmelCase     				=	TFDebertaForMaskedLM(config=__a      )
										UpperCAmelCase     				=	{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										UpperCAmelCase     				=	model(__a      )
										self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size)      )




				def      a__(		self			: Union[str, Any]      ,    lowerCAmelCase			: List[str]      ,    lowerCAmelCase			: List[str]      ,    lowerCAmelCase			: Union[str, Any]      ,    lowerCAmelCase			: int      ,    lowerCAmelCase			: int      ,    lowerCAmelCase			: List[str]      ,    lowerCAmelCase			: Dict      )->     Optional[int]:

										"""simple docstring"""




										UpperCAmelCase     				=	self.num_labels
										UpperCAmelCase     				=	TFDebertaForSequenceClassification(config=__a      )
										UpperCAmelCase     				=	{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										UpperCAmelCase     				=	model(__a      )
										self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_labels)      )




				def      a__(		self			: Optional[Any]      ,    lowerCAmelCase			: Any      ,    lowerCAmelCase			: Dict      ,    lowerCAmelCase			: int      ,    lowerCAmelCase			: Tuple      ,    lowerCAmelCase			: Union[str, Any]      ,    lowerCAmelCase			: Optional[Any]      ,    lowerCAmelCase			: int      )->     Tuple:

										"""simple docstring"""




										UpperCAmelCase     				=	self.num_labels
										UpperCAmelCase     				=	TFDebertaForTokenClassification(config=__a      )
										UpperCAmelCase     				=	{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										UpperCAmelCase     				=	model(__a      )
										self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.num_labels)      )




				def      a__(		self			: str      ,    lowerCAmelCase			: int      ,    lowerCAmelCase			: List[str]      ,    lowerCAmelCase			: Union[str, Any]      ,    lowerCAmelCase			: Optional[int]      ,    lowerCAmelCase			: Tuple      ,    lowerCAmelCase			: Optional[Any]      ,    lowerCAmelCase			: Union[str, Any]      )->     List[str]:

										"""simple docstring"""




										UpperCAmelCase     				=	TFDebertaForQuestionAnswering(config=__a      )
										UpperCAmelCase     				=	{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										UpperCAmelCase     				=	model(__a      )
										self.parent.assertEqual(result.start_logits.shape      ,    (self.batch_size, self.seq_length)      )
										self.parent.assertEqual(result.end_logits.shape      ,    (self.batch_size, self.seq_length)      )





				def      a__(		self			: Tuple      )->     Tuple:

										"""simple docstring"""




										UpperCAmelCase     				=	self.prepare_config_and_inputs()
										(
										    UpperCAmelCase     
										)     				=	config_and_inputs
										UpperCAmelCase     				=	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict







@require_tf
class        UpperCamelCase__(   _UpperCamelCase						,				_UpperCamelCase						,				unittest.TestCase       ):
				__magic_name__				:      List[str]  		=       (
				    (
				        TFDebertaModel,
				        TFDebertaForMaskedLM,
				        TFDebertaForQuestionAnswering,
				        TFDebertaForSequenceClassification,
				        TFDebertaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				__magic_name__				:      int  		=       (
				    {
				        "feature-extraction": TFDebertaModel,
				        "fill-mask": TFDebertaForMaskedLM,
				        "question-answering": TFDebertaForQuestionAnswering,
				        "text-classification": TFDebertaForSequenceClassification,
				        "token-classification": TFDebertaForTokenClassification,
				        "zero-shot": TFDebertaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				__magic_name__				:      Optional[Any]  		=       False
				__magic_name__				:      Union[str, Any]  		=       False




				def      a__(		self			: Optional[int]      )->     List[str]:

										"""simple docstring"""




										UpperCAmelCase     				=	TFDebertaModelTester(self      )
										UpperCAmelCase     				=	ConfigTester(self      ,    config_class=__a      ,    hidden_size=37      )




				def      a__(		self			: Union[str, Any]      )->     Tuple:

										"""simple docstring"""




										self.config_tester.run_common_tests()




				def      a__(		self			: List[Any]      )->     List[str]:

										"""simple docstring"""




										UpperCAmelCase     				=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a      )




				def      a__(		self			: Optional[int]      )->     Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase     				=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__a      )




				def      a__(		self			: Any      )->     int:

										"""simple docstring"""




										UpperCAmelCase     				=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__a      )




				def      a__(		self			: List[str]      )->     List[str]:

										"""simple docstring"""




										UpperCAmelCase     				=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__a      )




				def      a__(		self			: Tuple      )->     Any:

										"""simple docstring"""




										UpperCAmelCase     				=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__a      )





				@slow
				def      a__(		self			: Dict      )->     List[str]:

										"""simple docstring"""




										UpperCAmelCase     				=	TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''      )
										self.assertIsNotNone(__a      )







@require_tf
class        UpperCamelCase__(   unittest.TestCase       ):




				@unittest.skip(reason='''Model not available yet'''      )
				def      a__(		self			: List[Any]      )->     Dict:

										"""simple docstring"""




										pass





				@slow
				def      a__(		self			: int      )->     str:

										"""simple docstring"""




										UpperCAmelCase     				=	TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''      )
										UpperCAmelCase     				=	tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]      )
										UpperCAmelCase     				=	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
										UpperCAmelCase     				=	model(__a      ,    attention_mask=__a      )[0]

										UpperCAmelCase     				=	tf.constant(
										    [
										        [
										            [-0.59855896, -0.80552566, -0.8462135],
										            [1.4484025, -0.93483794, -0.80593085],
										            [0.3122741, 0.00316059, -1.4131377],
										        ]
										    ]      )
										tf.debugging.assert_near(output[:, 1:4, 1:4]      ,    __a      ,    atol=1E-4      )












"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

      # Save pytorch-model
      _UpperCamelCase					:		str  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _UpperCamelCase					:		Any  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
      torch.save(model.state_dict()						,lowercase_	)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
      with open(lowercase_						,"w"						,encoding="utf-8"	) as f:
            f.write(config.to_json_string()	)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
