


'''simple docstring'''







import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.electra.modeling_flax_electra import (
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForMultipleChoice,
           FlaxElectraForPreTraining,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForSequenceClassification,
           FlaxElectraForTokenClassification,
           FlaxElectraModel,
       )
class 		UpperCamelCase__    (       unittest.TestCase		):



     """simple docstring"""






     def __init__(       self					,		snake_case__					,		snake_case__=13					,		snake_case__=7					,		snake_case__=True					,		snake_case__=True					,		snake_case__=True					,		snake_case__=True					,		snake_case__=99					,		snake_case__=24					,		snake_case__=32					,		snake_case__=5					,		snake_case__=4					,		snake_case__=37					,		snake_case__="gelu"					,		snake_case__=0.1					,		snake_case__=0.1					,		snake_case__=512					,		snake_case__=16					,		snake_case__=2					,		snake_case__=0.02					,		snake_case__=4					,		):

            '''simple docstring'''

            _lowerCAmelCase			:							Optional[Any]      	=				parent
            _lowerCAmelCase			:							Dict      	=				batch_size
            _lowerCAmelCase			:							Optional[int]      	=				seq_length
            _lowerCAmelCase			:							Optional[int]      	=				is_training
            _lowerCAmelCase			:							str      	=				use_attention_mask
            _lowerCAmelCase			:							Optional[Any]      	=				use_token_type_ids
            _lowerCAmelCase			:							Dict      	=				use_labels
            _lowerCAmelCase			:							Union[str, Any]      	=				vocab_size
            _lowerCAmelCase			:							Tuple      	=				hidden_size
            _lowerCAmelCase			:							List[str]      	=				embedding_size
            _lowerCAmelCase			:							Union[str, Any]      	=				num_hidden_layers
            _lowerCAmelCase			:							int      	=				num_attention_heads
            _lowerCAmelCase			:							Optional[Any]      	=				intermediate_size
            _lowerCAmelCase			:							Optional[Any]      	=				hidden_act
            _lowerCAmelCase			:							Tuple      	=				hidden_dropout_prob
            _lowerCAmelCase			:							List[Any]      	=				attention_probs_dropout_prob
            _lowerCAmelCase			:							str      	=				max_position_embeddings
            _lowerCAmelCase			:							List[str]      	=				type_vocab_size
            _lowerCAmelCase			:							List[str]      	=				type_sequence_label_size
            _lowerCAmelCase			:							Dict      	=				initializer_range
            _lowerCAmelCase			:							Dict      	=				num_choices







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Any      	=				ids_tensor([self.batch_size, self.seq_length]					,		self.vocab_size      )

            _lowerCAmelCase			:							Optional[Any]      	=				None
            if self.use_attention_mask:
                   _lowerCAmelCase			:							List[Any]      	=				random_attention_mask([self.batch_size, self.seq_length]      )

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

            _lowerCAmelCase			:							Optional[int]      	=				ElectraConfig(
                vocab_size=self.vocab_size					,		hidden_size=self.hidden_size					,		embedding_size=self.embedding_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					,		initializer_range=self.initializer_range					,		)

            return config, input_ids, token_type_ids, attention_mask







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Dict      	=				self.prepare_config_and_inputs()
            _lowerCAmelCase			:							str      	=				config_and_inputs
            _lowerCAmelCase			:							Dict      	=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
            return config, inputs_dict
@require_flax
class 		UpperCamelCase__    (       _UpperCamelCase			,    unittest.TestCase		):



     """simple docstring"""






     __magic_name__				=			True

     __magic_name__				=			(
         (
             FlaxElectraModel,
             FlaxElectraForCausalLM,
             FlaxElectraForMaskedLM,
             FlaxElectraForPreTraining,
             FlaxElectraForTokenClassification,
             FlaxElectraForQuestionAnswering,
             FlaxElectraForMultipleChoice,
             FlaxElectraForSequenceClassification,
         )
         if is_flax_available()
         else ()
     )







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Union[str, Any]      	=				FlaxElectraModelTester(self      )







     @slow
     def a		(       self      ):

            '''simple docstring'''

            for model_class_name in self.all_model_classes:
                   if model_class_name == FlaxElectraForMaskedLM:
                          _lowerCAmelCase			:							int      	=				model_class_name.from_pretrained('google/electra-small-generator'      )
                   else:
                          _lowerCAmelCase			:							List[Any]      	=				model_class_name.from_pretrained('google/electra-small-discriminator'      )
                   _lowerCAmelCase			:							List[str]      	=				model(np.ones((1, 1)      )      )
                   self.assertIsNotNone(__a      )








"""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
  )
