


'''simple docstring'''






import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
							import tensorflow as tf
class 						__UpperCamelCase	(						enum.Enum							):
							lowercase			:		str   		=  0
							lowercase			:		Optional[Any]   		=  1
							lowercase			:		Any   		=  2





@add_end_docstrings(lowercase__							)
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Any   		=  '\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '





							def __init__(  self		:Optional[Any]  ,*_UpperCamelCase		:List[Any]  ,**_UpperCamelCase		:List[str]			):
									super().__init__(*_UpperCamelCase  ,**_UpperCamelCase			)
									self.check_model_type(
									    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_CAUSAL_LM_MAPPING			)
									if "prefix" not in self._preprocess_params:
											# This is very specific. The logic is quite complex and needs to be done
											# as a "default".
											# It also defines both some preprocess_kwargs and generate_kwargs
											# which is why we cannot put them in their respective methods.
											snake_case_		:							Any			       =						None
											if self.model.config.prefix is not None:
													snake_case_		:							Optional[int]			       =						self.model.config.prefix
											if prefix is None and self.model.__class__.__name__ in [
											    "XLNetLMHeadModel",
											    "TransfoXLLMHeadModel",
											    "TFXLNetLMHeadModel",
											    "TFTransfoXLLMHeadModel",
											]:
													# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
													snake_case_		:							int			       =						self.XL_PREFIX
											if prefix is not None:
													# Recalculate some generate_kwargs linked to prefix.
													snake_case_		:							List[str]			       =						self._sanitize_parameters(prefix=_UpperCamelCase  ,**self._forward_params			)
													snake_case_		:							int			       =						{**self._preprocess_params, **preprocess_params}
													snake_case_		:							Tuple			       =						{**self._forward_params, **forward_params}





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Union[str, Any]=None  ,_UpperCamelCase		:List[str]=None  ,_UpperCamelCase		:List[Any]=None  ,_UpperCamelCase		:int=None  ,_UpperCamelCase		:Dict=None  ,_UpperCamelCase		:Dict=None  ,_UpperCamelCase		:Dict=None  ,_UpperCamelCase		:Union[str, Any]=None  ,**_UpperCamelCase		:List[Any]  ,):
									snake_case_		:							List[Any]			       =						{}
									if prefix is not None:
											snake_case_		:							str			       =						prefix
									if prefix:
											snake_case_		:							List[Any]			       =						self.tokenizer(
											    _UpperCamelCase  ,padding=_UpperCamelCase  ,add_special_tokens=_UpperCamelCase  ,return_tensors=self.framework			)
											snake_case_		:							List[str]			       =						prefix_inputs["""input_ids"""].shape[-1]

									if handle_long_generation is not None:
											if handle_long_generation not in {"hole"}:
													raise ValueError(
													    F'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
													    """ [None, 'hole']"""			)
											snake_case_		:							Optional[Any]			       =						handle_long_generation

									preprocess_params.update(_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						generate_kwargs

									snake_case_		:							List[Any]			       =						{}
									if return_full_text is not None and return_type is None:
											if return_text is not None:
													raise ValueError("""`return_text` is mutually exclusive with `return_full_text`"""			)
											if return_tensors is not None:
													raise ValueError("""`return_full_text` is mutually exclusive with `return_tensors`"""			)
											snake_case_		:							Optional[int]			       =						ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
									if return_tensors is not None and return_type is None:
											if return_text is not None:
													raise ValueError("""`return_text` is mutually exclusive with `return_tensors`"""			)
											snake_case_		:							Optional[Any]			       =						ReturnType.TENSORS
									if return_type is not None:
											snake_case_		:							Tuple			       =						return_type
									if clean_up_tokenization_spaces is not None:
											snake_case_		:							Tuple			       =						clean_up_tokenization_spaces

									if stop_sequence is not None:
											snake_case_		:							Union[str, Any]			       =						self.tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
											if len(_UpperCamelCase			) > 1:
													warnings.warn(
													    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
													    """ the stop sequence will be used as the stop sequence string in the interim."""			)
											snake_case_		:							Optional[int]			       =						stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params





							def 		a__   (  self		:Optional[int]  ,*_UpperCamelCase		:Optional[int]  ,**_UpperCamelCase		:Dict			):
									# Parse arguments
									if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
											kwargs.update({"""add_space_before_punct_symbol""": True}			)

									return super()._parse_and_tokenize(*_UpperCamelCase  ,**_UpperCamelCase			)





							def __call__(  self		:Optional[int]  ,_UpperCamelCase		:Dict  ,**_UpperCamelCase		:Optional[Any]			):
									return super().__call__(_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Optional[int]=""  ,_UpperCamelCase		:Optional[Any]=None  ,**_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							int			       =						self.tokenizer(
									    prefix + prompt_text  ,padding=_UpperCamelCase  ,add_special_tokens=_UpperCamelCase  ,return_tensors=self.framework			)
									snake_case_		:							List[str]			       =						prompt_text

									if handle_long_generation == "hole":
											snake_case_		:							List[Any]			       =						inputs["""input_ids"""].shape[-1]
											if "max_new_tokens" in generate_kwargs:
													snake_case_		:							List[Any]			       =						generate_kwargs["""max_new_tokens"""]
											else:
													snake_case_		:							List[Any]			       =						generate_kwargs.get("""max_length"""  ,self.model.config.max_length			) - cur_len
													if new_tokens < 0:
															raise ValueError("""We cannot infer how many new tokens are expected"""			)
											if cur_len + new_tokens > self.tokenizer.model_max_length:
													snake_case_		:							str			       =						self.tokenizer.model_max_length - new_tokens
													if keep_length <= 0:
															raise ValueError(
															    """We cannot use `hole` to handle this generation the number of desired tokens exceeds the"""
															    """ models max length"""			)

													snake_case_		:							Optional[int]			       =						inputs["""input_ids"""][:, -keep_length:]
													if "attention_mask" in inputs:
															snake_case_		:							List[Any]			       =						inputs["""attention_mask"""][:, -keep_length:]

									return inputs





							def 		a__   (  self		:Any  ,_UpperCamelCase		:str  ,**_UpperCamelCase		:List[Any]			):
									snake_case_		:							List[Any]			       =						model_inputs["""input_ids"""]
									snake_case_		:							List[str]			       =						model_inputs.get("""attention_mask"""  ,_UpperCamelCase			)
									# Allow empty prompts
									if input_ids.shape[1] == 0:
											snake_case_		:							List[str]			       =						None
											snake_case_		:							Union[str, Any]			       =						None
											snake_case_		:							Union[str, Any]			       =						1
									else:
											snake_case_		:							Dict			       =						input_ids.shape[0]
									snake_case_		:							int			       =						model_inputs.pop("""prompt_text"""			)

									# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
									# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
									snake_case_		:							int			       =						generate_kwargs.pop("""prefix_length"""  ,0			)
									if prefix_length > 0:
											snake_case_		:							Union[str, Any]			       =						"""max_new_tokens""" in generate_kwargs or (
											    """generation_config""" in generate_kwargs
											    and generate_kwargs["""generation_config"""].max_new_tokens is not None
											)
											if not has_max_new_tokens:
													snake_case_		:							Optional[Any]			       =						generate_kwargs.get("""max_length"""			) or self.model.config.max_length
													generate_kwargs["max_length"] += prefix_length
											snake_case_		:							str			       =						"""min_new_tokens""" in generate_kwargs or (
											    """generation_config""" in generate_kwargs
											    and generate_kwargs["""generation_config"""].min_new_tokens is not None
											)
											if not has_min_new_tokens and "min_length" in generate_kwargs:
													generate_kwargs["min_length"] += prefix_length

        # BS x SL
									snake_case_		:							Optional[int]			       =						self.model.generate(input_ids=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,**_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						generated_sequence.shape[0]
									if self.framework == "pt":
											snake_case_		:							Union[str, Any]			       =						generated_sequence.reshape(_UpperCamelCase  ,out_b // in_b  ,*generated_sequence.shape[1:]			)
									elif self.framework == "tf":
											snake_case_		:							Union[str, Any]			       =						tf.reshape(_UpperCamelCase  ,(in_b, out_b // in_b, *generated_sequence.shape[1:])			)
									return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:str=ReturnType.FULL_TEXT  ,_UpperCamelCase		:List[Any]=True			):
									snake_case_		:							Optional[Any]			       =						model_outputs["""generated_sequence"""][0]
									snake_case_		:							Any			       =						model_outputs["""input_ids"""]
									snake_case_		:							int			       =						model_outputs["""prompt_text"""]
									snake_case_		:							Union[str, Any]			       =						generated_sequence.numpy().tolist()
									snake_case_		:							Union[str, Any]			       =						[]
									for sequence in generated_sequence:
											if return_type == ReturnType.TENSORS:
													snake_case_		:							List[str]			       =						{"""generated_token_ids""": sequence}
											elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
													# Decode text
													snake_case_		:							Any			       =						self.tokenizer.decode(
													    _UpperCamelCase  ,skip_special_tokens=_UpperCamelCase  ,clean_up_tokenization_spaces=_UpperCamelCase  ,)

													# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
													if input_ids is None:
															snake_case_		:							Dict			       =						0
													else:
															snake_case_		:							Optional[Any]			       =						len(
															    self.tokenizer.decode(
															        input_ids[0]  ,skip_special_tokens=_UpperCamelCase  ,clean_up_tokenization_spaces=_UpperCamelCase  ,)			)

													if return_type == ReturnType.FULL_TEXT:
															snake_case_		:							List[str]			       =						prompt_text + text[prompt_length:]
													else:
															snake_case_		:							str			       =						text[prompt_length:]

													snake_case_		:							int			       =						{"""generated_text""": all_text}
											records.append(_UpperCamelCase			)

									return records



'''simple docstring'''






import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


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





def      UpperCAmelCase      (		lowerCamelCase_  :nn.ModuleList				,      lowerCamelCase_  :nn.ModuleList				,      lowerCamelCase_  :List[int] ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						nn.ModuleList([src_layers[i] for i in layers_to_copy] )
		assert len(lowerCamelCase_ ) == len(lowerCamelCase_ ), F'''{len(lowerCamelCase_ )} != {len(lowerCamelCase_ )}'''
		dest_layers.load_state_dict(layers_to_copy.state_dict() )


__A	:	Optional[int]     =     {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__A	:	Dict     =     {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		try:
				snake_case_		:							int			       =						LAYERS_TO_COPY[n_teacher][n_student]
				return val
		except KeyError:
				if n_student != n_teacher:
						warnings.warn(
						    F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
						    F''' {n_student}''' )
				return list(range(lowerCamelCase_ ) )





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		if n_student > n_teacher:
				raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
		elif n_teacher == n_student:
				return list(range(lowerCamelCase_ ) )
		elif n_student == 1:
				return [n_teacher - 1]
		else:
				return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, PreTrainedModel]				,      lowerCamelCase_  :Union[str, Path] = "student"				,      lowerCamelCase_  :Union[int, None] = None				,      lowerCamelCase_  :Union[int, None] = None				,      lowerCamelCase_  :List[str]=False				,      lowerCamelCase_  :Optional[int]=None				,      lowerCamelCase_  :Optional[Any]=None				,      **lowerCamelCase_  :Dict				,      ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						"""encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
		assert (e is not None) or (d is not None), _msg
		if isinstance(lowerCamelCase_				,      lowerCamelCase_ ):
				AutoTokenizer.from_pretrained(lowerCamelCase_ ).save_pretrained(lowerCamelCase_ )  # purely for convenience
				snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_ ).eval()
		else:
				assert isinstance(lowerCamelCase_				,      lowerCamelCase_ ), F'''teacher must be a model or string got type {type(lowerCamelCase_ )}'''
		snake_case_		:							Any			       =						teacher.config.to_diff_dict()

		try:
				snake_case_       ,      snake_case_		:							List[str]			       =						teacher.config.encoder_layers, teacher.config.decoder_layers
				if e is None:
						snake_case_		:							Dict			       =						teacher_e
				if d is None:
						snake_case_		:							List[Any]			       =						teacher_d
				init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
		except AttributeError:  # T5
				if hasattr(teacher.config				,      """num_encoder_layers""" ):
						snake_case_       ,      snake_case_		:							Tuple			       =						teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
				else:
						snake_case_       ,      snake_case_		:							Union[str, Any]			       =						teacher.config.num_layers, teacher.config.num_decoder_layers
				if e is None:
						snake_case_		:							Optional[int]			       =						teacher_e
				if d is None:
						snake_case_		:							List[str]			       =						teacher_d
				if hasattr(teacher.config				,      """num_encoder_layers""" ):
						init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
				else:
						init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
		init_kwargs.update(lowerCamelCase_ )

		# Copy weights
		snake_case_		:							List[Any]			       =						teacher.config_class(**lowerCamelCase_ )
		snake_case_		:							Tuple			       =						AutoModelForSeqaSeqLM.from_config(lowerCamelCase_ )
		# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
		snake_case_		:							Tuple			       =						student.load_state_dict(teacher.state_dict()				,      strict=lowerCamelCase_ )
		assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

		if copy_first_teacher_layers:  # Our copying is done. We just log and save
				snake_case_       ,      snake_case_		:							List[str]			       =						list(range(lowerCamelCase_ ) ), list(range(lowerCamelCase_ ) )
				logger.info(
				    F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
				    F''' {save_path}''' )
				student.save_pretrained(lowerCamelCase_ )
				return student, e_layers_to_copy, d_layers_to_copy

		# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
		if e_layers_to_copy is None:
				snake_case_		:							List[int]			       =						pick_layers_to_copy(lowerCamelCase_				,      lowerCamelCase_ )
		if d_layers_to_copy is None:
				snake_case_		:							List[int]			       =						pick_layers_to_copy(lowerCamelCase_				,      lowerCamelCase_ )

		try:
				if hasattr(
				    lowerCamelCase_				,      """prophetnet""" ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
						copy_layers(teacher.prophetnet.encoder.layers				,      student.prophetnet.encoder.layers				,      lowerCamelCase_ )
						copy_layers(teacher.prophetnet.decoder.layers				,      student.prophetnet.decoder.layers				,      lowerCamelCase_ )
				else:
						copy_layers(teacher.model.encoder.layers				,      student.model.encoder.layers				,      lowerCamelCase_ )
						copy_layers(teacher.model.decoder.layers				,      student.model.decoder.layers				,      lowerCamelCase_ )
		except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
				copy_layers(teacher.encoder.block				,      student.encoder.block				,      lowerCamelCase_ )
				copy_layers(teacher.decoder.block				,      student.decoder.block				,      lowerCamelCase_ )
		logger.info(
		    F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
		snake_case_		:							Any			       =						{
		    """teacher_type""": teacher.config.model_type,
		    """copied_encoder_layers""": e_layers_to_copy,
		    """copied_decoder_layers""": d_layers_to_copy,
		}
		student.save_pretrained(lowerCamelCase_ )
		# Save information about copying for easier reproducibility

		return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
							fire.Fire(create_student_by_copying_alternating_layers)