




"""simple docstring"""




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


a_	   =    logging.get_logger(__name__)

class    UpperCAmelCase_  (       lowerCAmelCase_						):
		UpperCamelCase						 ="linear"
		UpperCamelCase						 ="cosine"
		UpperCamelCase						 ="cosine_with_restarts"
		UpperCamelCase						 ="polynomial"
		UpperCamelCase						 ="constant"
		UpperCamelCase						 ="constant_with_warmup"
		UpperCamelCase						 ="piecewise_constant"







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase = -1     ):
			return LambdaLR(__lowerCamelCase     ,							lambda __UpperCamelCase     : 1     ,							last_epoch=__lowerCamelCase     )







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = -1     ):

			def lr_lambda(__UpperCamelCase     ):
						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 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = -1     ):
			__lowercase       :   List[Any]        =    {}
			__lowercase       :   Tuple        =    step_rules.split(''','''     )
			for rule_str in rule_list[:-1]:
						__lowercase       :   List[Any]        =    rule_str.split(''':'''     )
						__lowercase       :   Any        =    int(__lowerCamelCase     )
						__lowercase       :   int        =    float(__lowerCamelCase     )
						__lowercase       :   Tuple        =    value
			__lowercase       :   Union[str, Any]        =    float(rule_list[-1]     )

			def create_rules_function(__UpperCamelCase     ,							__UpperCamelCase     ):
						def rule_func(__UpperCamelCase     ) -> float:
									__lowercase       :   Optional[int]        =    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

			__lowercase       :   Any        =    create_rules_function(__lowerCamelCase     ,							__lowerCamelCase     )

			return LambdaLR(__lowerCamelCase     ,							__lowerCamelCase     ,							last_epoch=__lowerCamelCase     )







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=-1     ):

			def lr_lambda(__UpperCamelCase     ):
						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 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = 0.5     ,							__UpperCamelCase = -1     ):

			def lr_lambda(__UpperCamelCase     ):
						if current_step < num_warmup_steps:
									return float(__lowerCamelCase     ) / float(max(1     ,							__lowerCamelCase     )     )
						__lowercase       :   Tuple        =    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 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = 1     ,							__UpperCamelCase = -1     ):

			def lr_lambda(__UpperCamelCase     ):
						if current_step < num_warmup_steps:
									return float(__lowerCamelCase     ) / float(max(1     ,							__lowerCamelCase     )     )
						__lowercase       :   Dict        =    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 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=1e-7     ,							__UpperCamelCase=1.0     ,							__UpperCamelCase=-1     ):
			__lowercase       :   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(__UpperCamelCase     ):
						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:
									__lowercase       :   Optional[int]        =    lr_init - lr_end
									__lowercase       :   Union[str, Any]        =    num_training_steps - num_warmup_steps
									__lowercase       :   Optional[Any]        =    1 - (current_step - num_warmup_steps) / decay_steps
									__lowercase       :   str        =    lr_range * pct_remaining**power + lr_end
									return decay / lr_init  # as LambdaLR multiplies by lr_init

			return LambdaLR(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     )


a_	   =    {
    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 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 1     ,							__UpperCamelCase = 1.0     ,							__UpperCamelCase = -1     ,							):
			__lowercase       :   int        =    SchedulerType(__lowerCamelCase     )
			__lowercase       :   Dict        =    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     )








"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['image_processor', 'tokenizer']
					_a   			=							'BlipImageProcessor'
					_a   			=							'AutoTokenizer'



					def __init__(       self							:  Tuple,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict     )->					str:
								lowerCamelCase__		:		Any			=False
								super().__init__(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=self.image_processor



					def __call__(       self							:  Union[str, Any],  lowerCamelCase							:  ImageInput = None,  lowerCamelCase							:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[bool, str, PaddingStrategy] = False,  lowerCamelCase							:  Union[bool, str, TruncationStrategy] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  int = 0,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  **lowerCamelCase							:  List[str],  )->					BatchEncoding:
								if images is None and text is None:
											raise ValueError('''You have to specify either images or text.'''     )

								# Get only text
								if images is None:
											lowerCamelCase__		:		str			=self.tokenizer
											lowerCamelCase__		:		str			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
											return text_encoding

								# add pixel_values
								lowerCamelCase__		:		Optional[int]			=self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase     )

								if text is not None:
											lowerCamelCase__		:		Union[str, Any]			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
											lowerCamelCase__		:		Optional[Any]			=None

								if text_encoding is not None:
											encoding_image_processor.update(lowerCamelCase     )

								return encoding_image_processor



					def 		snake_case       (       self							:  str,  *lowerCamelCase							:  Any,  **lowerCamelCase							:  List[str]     )->					Union[str, Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Dict,  *lowerCamelCase							:  str,  **lowerCamelCase							:  str     )->					Union[str, Any]:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase     )




					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								lowerCamelCase__		:		Union[str, Any]			=self.tokenizer.model_input_names
								lowerCamelCase__		:		List[str]			=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




