


import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch


A      = logging.get_logger(__name__)


class     lowercase__  :






  def __init__(					self      :					List[str]		,						_lowercase      :					str = None		,						_lowercase      :					uuid.UUID = None		,						_lowercase      :					Dict=None		,						_lowercase      :					Any=None ):




         """simple docstring"""







         if not conversation_id:
                UpperCAmelCase__										=     uuid.uuida()
         if past_user_inputs is None:
                UpperCAmelCase__										=     []
         if generated_responses is None:
                UpperCAmelCase__										=     []

         UpperCAmelCase__										=     conversation_id
         UpperCAmelCase__										=     past_user_inputs
         UpperCAmelCase__										=     generated_responses
         UpperCAmelCase__										=     text






  def __eq__(					self      :					Any		,						_lowercase      :					str ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                return False
         if self.uuid == other.uuid:
                return True
         return (
             self.new_user_input == other.new_user_input
             and self.past_user_inputs == other.past_user_inputs
             and self.generated_responses == other.generated_responses
         )






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					str		,						_lowercase      :					bool = False ):




         """simple docstring"""







         if self.new_user_input:
                if overwrite:
                       logger.warning(
                           F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
                           F"""with: \"{text}\".""" )
                       UpperCAmelCase__										=     text
                else:
                       logger.warning(
                           F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
                           F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
         else:
                UpperCAmelCase__										=     text






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         if self.new_user_input:
                self.past_user_inputs.append(self.new_user_input )
         UpperCAmelCase__										=     None






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					str ):




         """simple docstring"""







         self.generated_responses.append(_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         for user_input, generated_response in zip(self.past_user_inputs		,						self.generated_responses ):
                yield True, user_input
                yield False, generated_response
         if self.new_user_input:
                yield True, self.new_user_input






  def __repr__(					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     F"""Conversation id: {self.uuid} \n"""
         for is_user, text in self.iter_texts():
                UpperCAmelCase__										=     "user" if is_user else "bot"
                output += F"""{name} >> {text} \n"""
         return output




@add_end_docstrings(
    __SCREAMING_SNAKE_CASE					,							R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '					,							)
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					str		,						*_lowercase      :					Union[str, Any]		,						**_lowercase      :					Any ):




         """simple docstring"""







         super().__init__(*_lowercase		,						**_lowercase )
         if self.tokenizer.pad_token_id is None:
                UpperCAmelCase__										=     self.tokenizer.eos_token






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					int=None		,						_lowercase      :					Any=None		,						_lowercase      :					List[str]=None		,						**_lowercase      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     {}
         UpperCAmelCase__										=     {}
         UpperCAmelCase__										=     {}

         if min_length_for_response is not None:
                UpperCAmelCase__										=     min_length_for_response
         if minimum_tokens is not None:
                UpperCAmelCase__										=     minimum_tokens

         if "max_length" in generate_kwargs:
                UpperCAmelCase__										=     generate_kwargs["max_length"]
                # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
         if clean_up_tokenization_spaces is not None:
                UpperCAmelCase__										=     clean_up_tokenization_spaces

         if generate_kwargs:
                forward_params.update(_lowercase )
         return preprocess_params, forward_params, postprocess_params






  def __call__(					self      :					Dict		,						_lowercase      :					Union[Conversation, List[Conversation]]		,						_lowercase      :					List[str]=0		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     super().__call__(_lowercase		,						num_workers=_lowercase		,						**_lowercase )
         if isinstance(_lowercase		,						_lowercase ) and len(_lowercase ) == 1:
                return outputs[0]
         return outputs






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					Conversation		,						_lowercase      :					List[Any]=32 ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                raise ValueError("ConversationalPipeline, expects Conversation as inputs" )
         if conversation.new_user_input is None:
                raise ValueError(
                    F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
                    "Add user inputs with the conversation's `add_user_input` method" )
         if hasattr(self.tokenizer		,						"_build_conversation_input_ids" ):
                UpperCAmelCase__										=     self.tokenizer._build_conversation_input_ids(_lowercase )
         else:
                # If the tokenizer cannot handle conversations, we default to only the old version
                UpperCAmelCase__										=     self._legacy_parse_and_tokenize(_lowercase )

         if self.framework == "pt":
                UpperCAmelCase__										=     torch.LongTensor([input_ids] )
         elif self.framework == "tf":
                UpperCAmelCase__										=     tf.constant([input_ids] )
         return {"input_ids": input_ids, "conversation": conversation}






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					List[Any]=10		,						**_lowercase      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     generate_kwargs.get("max_length"		,						self.model.config.max_length )

         UpperCAmelCase__										=     model_inputs["input_ids"].shape[1]
         if max_length - minimum_tokens < n:
                logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
                UpperCAmelCase__										=     max_length - minimum_tokens
                UpperCAmelCase__										=     model_inputs["input_ids"][:, -trim:]
                if "attention_mask" in model_inputs:
                       UpperCAmelCase__										=     model_inputs["attention_mask"][:, -trim:]
         UpperCAmelCase__										=     model_inputs.pop("conversation" )
         UpperCAmelCase__										=     max_length
         UpperCAmelCase__										=     self.model.generate(**_lowercase		,						**_lowercase )
         if self.model.config.is_encoder_decoder:
                UpperCAmelCase__										=     1
         else:
                UpperCAmelCase__										=     n
         return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Optional[Any]=True ):




         """simple docstring"""







         UpperCAmelCase__										=     model_outputs["output_ids"]
         UpperCAmelCase__										=     self.tokenizer.decode(
             output_ids[0]		,						skip_special_tokens=_lowercase		,						clean_up_tokenization_spaces=_lowercase		,						)
         UpperCAmelCase__										=     model_outputs["conversation"]
         conversation.mark_processed()
         conversation.append_response(_lowercase )
         return conversation






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					Conversation ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer.eos_token_id
         UpperCAmelCase__										=     []
         for is_user, text in conversation.iter_texts():
                if eos_token_id is not None:
                       input_ids.extend(self.tokenizer.encode(_lowercase		,						add_special_tokens=_lowercase ) + [eos_token_id] )
                else:
                       input_ids.extend(self.tokenizer.encode(_lowercase		,						add_special_tokens=_lowercase ) )

         if len(_lowercase ) > self.tokenizer.model_max_length:
                UpperCAmelCase__										=     input_ids[-self.tokenizer.model_max_length :]
         return input_ids









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 __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     nn.ModuleList([src_layers[i] for i in layers_to_copy]			)
       assert len(__A			) == len(__A			), F"""{len(__A			)} != {len(__A			)}"""
       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 __UpperCAmelCase       (		__A ,						__A			)						->					int:



       '''simple docstring'''





       try:
              UpperCAmelCase__										=     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(__A			)			)





def __UpperCAmelCase       (		__A ,						__A			)						->					List[int]:



       '''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(__A			)			)
       elif n_student == 1:
              return [n_teacher - 1]
       else:
              return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def __UpperCAmelCase       (		__A ,						__A = "student" ,						__A = None ,						__A = None ,						__A=False ,						__A=None ,						__A=None ,						**__A ,						)						->					Tuple[PreTrainedModel, List[int], List[int]]:



       '''simple docstring'''





       UpperCAmelCase__										=     "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(__A ,						__A			):
              AutoTokenizer.from_pretrained(__A			).save_pretrained(__A			)  # purely for convenience
              UpperCAmelCase__										=     AutoModelForSeqaSeqLM.from_pretrained(__A			).eval()
       else:
              assert isinstance(__A ,						__A			), F"""teacher must be a model or string got type {type(__A			)}"""
       UpperCAmelCase__										=     teacher.config.to_diff_dict()

       try:
              UpperCAmelCase__						,			UpperCAmelCase__										=     teacher.config.encoder_layers, teacher.config.decoder_layers
              if e is None:
                     UpperCAmelCase__										=     teacher_e
              if d is None:
                     UpperCAmelCase__										=     teacher_d
              init_kwargs.update({"encoder_layers": e, "decoder_layers": d}			)
       except AttributeError:  # T5
              if hasattr(teacher.config ,						"num_encoder_layers"			):
                     UpperCAmelCase__						,			UpperCAmelCase__										=     teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
              else:
                     UpperCAmelCase__						,			UpperCAmelCase__										=     teacher.config.num_layers, teacher.config.num_decoder_layers
              if e is None:
                     UpperCAmelCase__										=     teacher_e
              if d is None:
                     UpperCAmelCase__										=     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(__A			)

       # Copy weights
       UpperCAmelCase__										=     teacher.config_class(**__A			)
       UpperCAmelCase__										=     AutoModelForSeqaSeqLM.from_config(__A			)
       # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
       UpperCAmelCase__										=     student.load_state_dict(teacher.state_dict() ,						strict=__A			)
       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
              UpperCAmelCase__						,			UpperCAmelCase__										=     list(range(__A			)			), list(range(__A			)			)
              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(__A			)
              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:
              UpperCAmelCase__										=     pick_layers_to_copy(__A ,						__A			)
       if d_layers_to_copy is None:
              UpperCAmelCase__										=     pick_layers_to_copy(__A ,						__A			)

       try:
              if hasattr(
                  __A ,						"prophetnet"			):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
                     copy_layers(teacher.prophetnet.encoder.layers ,						student.prophetnet.encoder.layers ,						__A			)
                     copy_layers(teacher.prophetnet.decoder.layers ,						student.prophetnet.decoder.layers ,						__A			)
              else:
                     copy_layers(teacher.model.encoder.layers ,						student.model.encoder.layers ,						__A			)
                     copy_layers(teacher.model.decoder.layers ,						student.model.decoder.layers ,						__A			)
       except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
              copy_layers(teacher.encoder.block ,						student.encoder.block ,						__A			)
              copy_layers(teacher.decoder.block ,						student.decoder.block ,						__A			)
       logger.info(
           F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""			)
       UpperCAmelCase__										=     {
           "teacher_type": teacher.config.model_type,
           "copied_encoder_layers": e_layers_to_copy,
           "copied_decoder_layers": d_layers_to_copy,
       }
       student.save_pretrained(__A			)
       # 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)





