import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Any:
	return 1.0 / (1.0 + np.exp(-_outputs      ))






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Optional[Any] 	=			np.max(_outputs     ,			axis=-1     ,			keepdims=lowercase__      )
	__lowerCamelCase    :    List[Any] 	=			np.exp(_outputs - maxes      )
	return shifted_exp / shifted_exp.sum(axis=-1     ,			keepdims=lowercase__      )






class 	A_		(	A__    ):
							_UpperCAmelCase   :  Tuple										=				"sigmoid"
							_UpperCAmelCase   :  Dict										=				"softmax"
							_UpperCAmelCase   :  Tuple										=				"none"






@add_end_docstrings(
    A__      ,							r'''\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    '''      ,							)
class 	A_		(	A__    ):
							_UpperCAmelCase   :  Optional[Any]										=				False
							_UpperCAmelCase   :  List[Any]										=				ClassificationFunction.NONE
							def __init__(		self		:      Optional[Any]       ,**SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								super().__init__(**__A)

								self.check_model_type(
								    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=None       ,SCREAMING_SNAKE_CASE__		:      List[str]=None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=""       ,**SCREAMING_SNAKE_CASE__		:      str):
								# Using "" as default argument because we're going to use `top_k=None` in user code to declare
								# "No top_k"
								__lowerCamelCase    :    Tuple 	=			tokenizer_kwargs

								__lowerCamelCase    :    List[str] 	=			{}
								if hasattr(self.model.config       ,'return_all_scores') and return_all_scores is None:
									__lowerCamelCase    :    int 	=			self.model.config.return_all_scores

								if isinstance(__A       ,__A) or top_k is None:
									__lowerCamelCase    :    Any 	=			top_k
									__lowerCamelCase    :    Optional[int] 	=			False
								elif return_all_scores is not None:
									warnings.warn(
									    '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
									    ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'       ,__A       ,)
									if return_all_scores:
										__lowerCamelCase    :    Optional[Any] 	=			None
									else:
										__lowerCamelCase    :    Dict 	=			1

								if isinstance(__A       ,__A):
									__lowerCamelCase    :    Any 	=			ClassificationFunction[function_to_apply.upper()]

								if function_to_apply is not None:
									__lowerCamelCase    :    Optional[Any] 	=			function_to_apply
								return preprocess_params, {}, postprocess_params
							def __call__(		self		:      str       ,*SCREAMING_SNAKE_CASE__		:      Optional[int]       ,**SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    Dict 	=			super().__call__(*__A       ,**__A)
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								__lowerCamelCase    :    Optional[int] 	=			"""top_k""" not in kwargs
								if isinstance(args[0]       ,__A) and _legacy:
									# This pipeline is odd, and return a list when single item is run
									return [result]
								else:
									return result
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,**SCREAMING_SNAKE_CASE__		:      Any):
								__lowerCamelCase    :    List[str] 	=			self.framework
								if isinstance(__A       ,__A):
									return self.tokenizer(**__A       ,return_tensors=__A       ,**__A)
								elif isinstance(__A       ,__A) and len(__A) == 1 and isinstance(inputs[0]       ,__A) and len(inputs[0]) == 2:
									# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
									return self.tokenizer(
									    text=inputs[0][0]       ,text_pair=inputs[0][1]       ,return_tensors=__A       ,**__A)
								elif isinstance(__A       ,__A):
									# This is likely an invalid usage of the pipeline attempting to pass text pairs.
									raise ValueError(
									    'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
									    ' dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.')
								return self.tokenizer(__A       ,return_tensors=__A       ,**__A)
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								return self.model(**__A)


							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      int=None       ,SCREAMING_SNAKE_CASE__		:      List[Any]=1       ,SCREAMING_SNAKE_CASE__		:      List[Any]=True):
								# `_legacy` is used to determine if we're running the naked pipeline and in backward
								# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
								# the more natural result containing the list.
								# Default value before `set_parameters`
								if function_to_apply is None:
									if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
										__lowerCamelCase    :    List[str] 	=			ClassificationFunction.SIGMOID
									elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
										__lowerCamelCase    :    Any 	=			ClassificationFunction.SOFTMAX
									elif hasattr(self.model.config       ,'function_to_apply') and function_to_apply is None:
										__lowerCamelCase    :    Optional[int] 	=			self.model.config.function_to_apply
									else:
										__lowerCamelCase    :    List[Any] 	=			ClassificationFunction.NONE

								__lowerCamelCase    :    Any 	=			model_outputs["""logits"""][0]
								__lowerCamelCase    :    Optional[Any] 	=			outputs.numpy()

								if function_to_apply == ClassificationFunction.SIGMOID:
									__lowerCamelCase    :    Optional[int] 	=			sigmoid(__A)
								elif function_to_apply == ClassificationFunction.SOFTMAX:
									__lowerCamelCase    :    Optional[int] 	=			softmax(__A)
								elif function_to_apply == ClassificationFunction.NONE:
									__lowerCamelCase    :    List[Any] 	=			outputs
								else:
									raise ValueError(F"Unrecognized `function_to_apply` argument: {function_to_apply}")

								if top_k == 1 and _legacy:
									return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

								__lowerCamelCase    :    int 	=			[
								    {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(__A)
								]
								if not _legacy:
									dict_scores.sort(key=lambda SCREAMING_SNAKE_CASE__: x["score"]       ,reverse=__A)
									if top_k is not None:
										__lowerCamelCase    :    List[str] 	=			dict_scores[:top_k]
								return dict_scores







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		=logging.get_logger(__name__)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    Tuple 	=			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		={
    # 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		={
    # 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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	try:
		__lowerCamelCase    :    List[str] 	=			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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[int]:
	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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = "student"     ,			lowerCamelCase__ = None     ,			lowerCamelCase__ = None     ,			lowerCamelCase__=False     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			**lowerCamelCase__     ,			)  ->					Tuple[PreTrainedModel, List[int], List[int]]:
	__lowerCamelCase    :    int 	=			'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
		__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__      ).eval()
	else:
		assert isinstance(lowerCamelCase__     ,			lowerCamelCase__      ), F"teacher must be a model or string got type {type(lowerCamelCase__      )}"
	__lowerCamelCase    :    str 	=			teacher.config.to_diff_dict()

	try:
		__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			__lowerCamelCase    :    Optional[int] 	=			teacher_e
		if d is None:
			__lowerCamelCase    :    Optional[Any] 	=			teacher_d
		init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}      )
	except AttributeError:  # T5
		if hasattr(teacher.config     ,			'num_encoder_layers'      ):
			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			__lowerCamelCase    :    Union[str, Any] 	=			teacher_e
		if d is None:
			__lowerCamelCase    :    Any 	=			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
	__lowerCamelCase    :    str 	=			teacher.config_class(**lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSeqaSeqLM.from_config(lowerCamelCase__      )
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	__lowerCamelCase    :    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
		__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			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:
		__lowerCamelCase    :    List[int] 	=			pick_layers_to_copy(lowerCamelCase__     ,			lowerCamelCase__      )
	if d_layers_to_copy is None:
		__lowerCamelCase    :    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}"      )
	__lowerCamelCase    :    Dict 	=			{
	    '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)




