



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase :				List[Any]							=logging.get_logger(__name__)

UpperCAmelCase :				List[str]							={
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """longt5"""
				lowercase__     =    ["""past_key_values"""]
				lowercase__     =    {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

				def __init__(			self  ,			snake_case__=3_2128  ,			snake_case__=512  ,			snake_case__=64  ,			snake_case__=2048  ,			snake_case__=6  ,			snake_case__=None  ,			snake_case__=8  ,			snake_case__=127  ,			snake_case__=16  ,			snake_case__=32  ,			snake_case__=128  ,			snake_case__=0.1  ,			snake_case__=1e-6  ,			snake_case__=1.0  ,			snake_case__="relu"  ,			snake_case__=True  ,			snake_case__="local"  ,			snake_case__=True  ,			snake_case__=0  ,			snake_case__=1  ,			**snake_case__  ,			):

									'''simple docstring'''


									UpperCamelCase_			= vocab_size
									UpperCamelCase_			= d_model
									UpperCamelCase_			= d_kv
									UpperCamelCase_			= d_ff
									UpperCamelCase_			= num_layers
									# default = symmetry
									UpperCamelCase_			= num_decoder_layers if num_decoder_layers is not None else self.num_layers
									UpperCamelCase_			= num_heads
									UpperCamelCase_			= local_radius
									UpperCamelCase_			= global_block_size
									UpperCamelCase_			= relative_attention_num_buckets
									UpperCamelCase_			= relative_attention_max_distance
									UpperCamelCase_			= dropout_rate
									UpperCamelCase_			= layer_norm_epsilon
									UpperCamelCase_			= initializer_factor
									UpperCamelCase_			= feed_forward_proj
									UpperCamelCase_			= encoder_attention_type
									UpperCamelCase_			= use_cache

									UpperCamelCase_			= self.feed_forward_proj.split("-"     )
									UpperCamelCase_			= act_info[-1]
									UpperCamelCase_			= act_info[0] == "gated"

									if len(snake_case__     ) > 1 and act_info[0] != "gated" or len(snake_case__     ) > 2:
														raise ValueError(
														    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
														    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
														    "'gated-gelu' or 'relu'"     )

									# for backwards compatibility
									if feed_forward_proj == "gated-gelu":
														UpperCamelCase_			= "gelu_new"

									super().__init__(
									    pad_token_id=snake_case__  ,			eos_token_id=snake_case__  ,			is_encoder_decoder=snake_case__  ,			**snake_case__  ,			)




class    _lowercase (a_   ):
				'''simple docstring'''







				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= {
									    "input_ids": {0: "batch", 1: "encoder_sequence"},
									    "attention_mask": {0: "batch", 1: "encoder_sequence"},
									}
									if self.use_past:
														UpperCamelCase_			= "past_encoder_sequence + sequence"
														UpperCamelCase_			= {0: "batch"}
														UpperCamelCase_			= {0: "batch", 1: "past_decoder_sequence + sequence"}
									else:
														UpperCamelCase_			= {0: "batch", 1: "decoder_sequence"}
														UpperCamelCase_			= {0: "batch", 1: "decoder_sequence"}

									if self.use_past:
														self.fill_with_past_key_values_(snake_case__  ,			direction="inputs"     )

									return common_inputs

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return 13





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 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

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

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


if __name__ == "__main__":
							UpperCAmelCase__  =			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."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
