
import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowerCamelCase_						: Tuple         =  logging.get_logger(__name__)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Dict					      = """linear"""
			__UpperCamelCase      :						List[Any]					      = """cosine"""
			__UpperCamelCase      :						int					      = """cosine_with_restarts"""
			__UpperCamelCase      :						List[str]					      = """polynomial"""
			__UpperCamelCase      :						Any					      = """constant"""
			__UpperCamelCase      :						List[str]					      = """constant_with_warmup"""
			__UpperCamelCase      :						str					      = """piecewise_constant"""







def        A__					(						lowerCamelCase     ,    lowerCamelCase = -1						)  ->	Any:
				return LambdaLR(lowerCamelCase     ,    lambda lowerCamelCase						: 1     ,    last_epoch=lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = -1						)  ->	Dict:

				def lr_lambda(lowerCamelCase						):
								if current_step < num_warmup_steps:
												return float(lowerCamelCase						) / float(max(1.0     ,    lowerCamelCase						)						)
								return 1.0

				return LambdaLR(lowerCamelCase     ,    lowerCamelCase     ,    last_epoch=lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = -1						)  ->	Optional[int]:
				UpperCamelCase_:							Dict	=						{}
				UpperCamelCase_:							List[str]	=						step_rules.split(""","""						)
				for rule_str in rule_list[:-1]:
								UpperCamelCase_,   UpperCamelCase_:							Tuple	=						rule_str.split(""":"""						)
								UpperCamelCase_:							Dict	=						int(lowerCamelCase						)
								UpperCamelCase_:							Dict	=						float(lowerCamelCase						)
								UpperCamelCase_:							Any	=						value
				UpperCamelCase_:							Optional[int]	=						float(rule_list[-1]						)

				def create_rules_function(lowerCamelCase     ,    lowerCamelCase						):
								def rule_func(lowerCamelCase						) -> float:
												UpperCamelCase_:							Dict	=						sorted(rules_dict.keys()						)
												for i, sorted_step in enumerate(lowerCamelCase						):
																if steps < sorted_step:
																				return rules_dict[sorted_steps[i]]
												return last_lr_multiple

								return rule_func

				UpperCamelCase_:							Optional[Any]	=						create_rules_function(lowerCamelCase     ,    lowerCamelCase						)

				return LambdaLR(lowerCamelCase     ,    lowerCamelCase     ,    last_epoch=lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=-1						)  ->	Optional[Any]:

				def lr_lambda(lowerCamelCase						):
								if current_step < num_warmup_steps:
												return float(lowerCamelCase						) / float(max(1     ,    lowerCamelCase						)						)
								return max(
								    0.0     ,    float(num_training_steps - current_step						) / float(max(1     ,    num_training_steps - num_warmup_steps						)						)						)

				return LambdaLR(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = 0.5     ,    lowerCamelCase = -1						)  ->	Dict:

				def lr_lambda(lowerCamelCase						):
								if current_step < num_warmup_steps:
												return float(lowerCamelCase						) / float(max(1     ,    lowerCamelCase						)						)
								UpperCamelCase_:							Optional[int]	=						float(current_step - num_warmup_steps						) / float(max(1     ,    num_training_steps - num_warmup_steps						)						)
								return max(0.0     ,    0.5 * (1.0 + math.cos(math.pi * float(lowerCamelCase						) * 2.0 * progress						))						)

				return LambdaLR(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = 1     ,    lowerCamelCase = -1						)  ->	Optional[Any]:

				def lr_lambda(lowerCamelCase						):
								if current_step < num_warmup_steps:
												return float(lowerCamelCase						) / float(max(1     ,    lowerCamelCase						)						)
								UpperCamelCase_:							Optional[int]	=						float(current_step - num_warmup_steps						) / float(max(1     ,    num_training_steps - num_warmup_steps						)						)
								if progress >= 1.0:
												return 0.0
								return max(0.0     ,    0.5 * (1.0 + math.cos(math.pi * ((float(lowerCamelCase						) * progress) % 1.0)						))						)

				return LambdaLR(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=1E-7     ,    lowerCamelCase=1.0     ,    lowerCamelCase=-1						)  ->	List[str]:
				UpperCamelCase_:							List[Any]	=						optimizer.defaults["""lr"""]
				if not (lr_init > lr_end):
								raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''						)

				def lr_lambda(lowerCamelCase						):
								if current_step < num_warmup_steps:
												return float(lowerCamelCase						) / float(max(1     ,    lowerCamelCase						)						)
								elif current_step > num_training_steps:
												return lr_end / lr_init  # as LambdaLR multiplies by lr_init
								else:
												UpperCamelCase_:							Optional[int]	=						lr_init - lr_end
												UpperCamelCase_:							Optional[Any]	=						num_training_steps - num_warmup_steps
												UpperCamelCase_:							Optional[Any]	=						1 - (current_step - num_warmup_steps) / decay_steps
												UpperCamelCase_:							Optional[Any]	=						lr_range * pct_remaining**power + lr_end
												return decay / lr_init  # as LambdaLR multiplies by lr_init

				return LambdaLR(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)


lowerCamelCase_						: Optional[Any]         =  {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = None     ,    lowerCamelCase = None     ,    lowerCamelCase = None     ,    lowerCamelCase = 1     ,    lowerCamelCase = 1.0     ,    lowerCamelCase = -1     ,    )  ->	Optional[int]:
				UpperCamelCase_:							List[Any]	=						SchedulerType(lowerCamelCase						)
				UpperCamelCase_:							Union[str, Any]	=						TYPE_TO_SCHEDULER_FUNCTION[name]
				if name == SchedulerType.CONSTANT:
								return schedule_func(lowerCamelCase     ,    last_epoch=lowerCamelCase						)

				if name == SchedulerType.PIECEWISE_CONSTANT:
								return schedule_func(lowerCamelCase     ,    step_rules=lowerCamelCase     ,    last_epoch=lowerCamelCase						)

				# All other schedulers require `num_warmup_steps`
				if num_warmup_steps is None:
								raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''						)

				if name == SchedulerType.CONSTANT_WITH_WARMUP:
								return schedule_func(lowerCamelCase     ,    num_warmup_steps=lowerCamelCase     ,    last_epoch=lowerCamelCase						)

				# All other schedulers require `num_training_steps`
				if num_training_steps is None:
								raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''						)

				if name == SchedulerType.COSINE_WITH_RESTARTS:
								return schedule_func(
								    lowerCamelCase     ,    num_warmup_steps=lowerCamelCase     ,    num_training_steps=lowerCamelCase     ,    num_cycles=lowerCamelCase     ,    last_epoch=lowerCamelCase     ,    )

				if name == SchedulerType.POLYNOMIAL:
								return schedule_func(
								    lowerCamelCase     ,    num_warmup_steps=lowerCamelCase     ,    num_training_steps=lowerCamelCase     ,    power=lowerCamelCase     ,    last_epoch=lowerCamelCase     ,    )

				return schedule_func(
				    lowerCamelCase     ,    num_warmup_steps=lowerCamelCase     ,    num_training_steps=lowerCamelCase     ,    last_epoch=lowerCamelCase						)








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






