
"""simple docstring"""




import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
  from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder



def 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       List[str]=None       ,    lowerCAmelCase	:       int=None       ,    lowerCAmelCase	:       List[Any]=None       ,    lowerCAmelCase	:       List[str]=None       ,    lowerCAmelCase	:       str=None       ,    ):






     '''simple docstring'''
     if attention_mask is None:
          UpperCAmelCase__							: Optional[Any] 							=						input_ids.ne(config.pad_token_id		)
     if decoder_attention_mask is None:
          UpperCAmelCase__							: Any 							=						decoder_input_ids.ne(config.pad_token_id		)
     if head_mask is None:
          UpperCAmelCase__							: Optional[int] 							=						torch.ones(config.encoder_layers       ,    config.encoder_attention_heads       ,    device=lowerCAmelCase		)
     if decoder_head_mask is None:
          UpperCAmelCase__							: Optional[Any] 							=						torch.ones(config.decoder_layers       ,    config.decoder_attention_heads       ,    device=lowerCAmelCase		)
     if cross_attn_head_mask is None:
          UpperCAmelCase__							: Optional[int] 							=						torch.ones(config.decoder_layers       ,    config.decoder_attention_heads       ,    device=lowerCAmelCase		)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }







class   _lowercase						:


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase=13 ,			__UpperCamelCase=7 ,			__UpperCamelCase=True ,			__UpperCamelCase=False ,			__UpperCamelCase=99 ,			__UpperCamelCase=16 ,			__UpperCamelCase=2 ,			__UpperCamelCase=4 ,			__UpperCamelCase=4 ,			__UpperCamelCase="relu" ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=20 ,			__UpperCamelCase=2 ,			__UpperCamelCase=1 ,			__UpperCamelCase=0 ,			)->					Dict:
          UpperCAmelCase__							: Any 							=						parent
          UpperCAmelCase__							: Dict 							=						batch_size
          UpperCAmelCase__							: Tuple 							=						seq_length
          UpperCAmelCase__							: Dict 							=						is_training
          UpperCAmelCase__							: List[Any] 							=						use_labels
          UpperCAmelCase__							: Dict 							=						vocab_size
          UpperCAmelCase__							: Union[str, Any] 							=						hidden_size
          UpperCAmelCase__							: Optional[int] 							=						num_hidden_layers
          UpperCAmelCase__							: List[str] 							=						num_attention_heads
          UpperCAmelCase__							: str 							=						intermediate_size
          UpperCAmelCase__							: Optional[int] 							=						hidden_act
          UpperCAmelCase__							: Optional[int] 							=						hidden_dropout_prob
          UpperCAmelCase__							: Optional[Any] 							=						attention_probs_dropout_prob
          UpperCAmelCase__							: Optional[Any] 							=						encoder_layerdrop
          UpperCAmelCase__							: List[str] 							=						decoder_layerdrop
          UpperCAmelCase__							: Optional[int] 							=						max_position_embeddings
          UpperCAmelCase__							: List[str] 							=						eos_token_id
          UpperCAmelCase__							: Union[str, Any] 							=						pad_token_id
          UpperCAmelCase__							: int 							=						bos_token_id



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: List[Any] 							=						ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )
          UpperCAmelCase__							: Optional[Any] 							=						self.eos_token_id  # Eos Token
          UpperCAmelCase__							: int 							=						ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for M2M100 the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          UpperCAmelCase__							: Tuple 							=						input_ids.clamp(self.pad_token_id + 1  )
          UpperCAmelCase__							: List[str] 							=						decoder_input_ids.clamp(self.pad_token_id + 1  )

          UpperCAmelCase__							: Dict 							=						self.get_config()
          UpperCAmelCase__							: Optional[int] 							=						prepare_mam_aaa_inputs_dict(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )
          return config, inputs_dict



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          return MaMaaaConfig(
              vocab_size=self.vocab_size ,			d_model=self.hidden_size ,			encoder_layers=self.num_hidden_layers ,			decoder_layers=self.num_hidden_layers ,			encoder_attention_heads=self.num_attention_heads ,			decoder_attention_heads=self.num_attention_heads ,			encoder_ffn_dim=self.intermediate_size ,			decoder_ffn_dim=self.intermediate_size ,			dropout=self.hidden_dropout_prob ,			attention_dropout=self.attention_probs_dropout_prob ,			encoder_layerdrop=self.encoder_layerdrop ,			decoder_layerdrop=self.decoder_layerdrop ,			max_position_embeddings=self.max_position_embeddings ,			eos_token_id=self.eos_token_id ,			bos_token_id=self.bos_token_id ,			pad_token_id=self.pad_token_id ,			)



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Dict 							=						self.prepare_config_and_inputs()
          return config, inputs_dict



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Dict:
          UpperCAmelCase__							: Optional[Any] 							=						MaMaaaModel(config=__UpperCamelCase  ).get_decoder().to(__UpperCamelCase  ).eval()
          UpperCAmelCase__							: Dict 							=						inputs_dict["input_ids"]
          UpperCAmelCase__							: str 							=						inputs_dict["attention_mask"]
          UpperCAmelCase__							: List[str] 							=						inputs_dict["head_mask"]

          # first forward pass
          UpperCAmelCase__							: Dict 							=						model(__UpperCamelCase ,			attention_mask=__UpperCamelCase ,			head_mask=__UpperCamelCase ,			use_cache=__UpperCamelCase  )

          UpperCAmelCase__							: str 							=						outputs.to_tuple()

          # create hypothetical multiple next token and extent to next_input_ids
          UpperCAmelCase__							: str 							=						ids_tensor((self.batch_size, 3) ,			config.vocab_size  )
          UpperCAmelCase__							: int 							=						ids_tensor((self.batch_size, 3) ,			2  )

          # append to next input_ids and
          UpperCAmelCase__							: Union[str, Any] 							=						torch.cat([input_ids, next_tokens] ,			dim=-1  )
          UpperCAmelCase__							: List[str] 							=						torch.cat([attention_mask, next_attn_mask] ,			dim=-1  )

          UpperCAmelCase__							: Optional[Any] 							=						model(__UpperCamelCase ,			attention_mask=__UpperCamelCase  )["last_hidden_state"]
          UpperCAmelCase__							: Optional[Any] 							=						model(__UpperCamelCase ,			attention_mask=__UpperCamelCase ,			past_key_values=__UpperCamelCase  )[
              "last_hidden_state"
          ]

          # select random slice
          UpperCAmelCase__							: Tuple 							=						ids_tensor((1,) ,			output_from_past.shape[-1]  ).item()
          UpperCAmelCase__							: str 							=						output_from_no_past[:, -3:, random_slice_idx].detach()
          UpperCAmelCase__							: Union[str, Any] 							=						output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(__UpperCamelCase ,			__UpperCamelCase ,			atol=1E-2  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Any:
          UpperCAmelCase__							: Tuple 							=						MaMaaaModel(config=__UpperCamelCase  ).to(__UpperCamelCase  ).eval()
          UpperCAmelCase__							: List[str] 							=						model(**__UpperCamelCase  )

          UpperCAmelCase__							: str 							=						outputs.encoder_last_hidden_state
          UpperCAmelCase__							: str 							=						outputs.last_hidden_state

          with tempfile.TemporaryDirectory() as tmpdirname:
               UpperCAmelCase__							: List[str] 							=						model.get_encoder()
               encoder.save_pretrained(__UpperCamelCase  )
               UpperCAmelCase__							: Dict 							=						MaMaaaEncoder.from_pretrained(__UpperCamelCase  ).to(__UpperCamelCase  )

          UpperCAmelCase__							: Optional[Any] 							=						encoder(inputs_dict["input_ids"] ,			attention_mask=inputs_dict["attention_mask"]  )[
              0
          ]

          self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3  )

          with tempfile.TemporaryDirectory() as tmpdirname:
               UpperCAmelCase__							: Optional[Any] 							=						model.get_decoder()
               decoder.save_pretrained(__UpperCamelCase  )
               UpperCAmelCase__							: Any 							=						MaMaaaDecoder.from_pretrained(__UpperCamelCase  ).to(__UpperCamelCase  )

          UpperCAmelCase__							: Union[str, Any] 							=						decoder(
              input_ids=inputs_dict["decoder_input_ids"] ,			attention_mask=inputs_dict["decoder_attention_mask"] ,			encoder_hidden_states=__UpperCamelCase ,			encoder_attention_mask=inputs_dict["attention_mask"] ,			)[0]

          self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3  )







@require_torch
class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							(
         (
             MaMaaaModel,
             MaMaaaForConditionalGeneration,
         )
         if is_torch_available()
         else ()
     )
     _A								=							(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
     _A								=							(
         {
             'conversational': MaMaaaForConditionalGeneration,
             'feature-extraction': MaMaaaModel,
             'summarization': MaMaaaForConditionalGeneration,
             'text2text-generation': MaMaaaForConditionalGeneration,
             'translation': MaMaaaForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     _A								=							True
     _A								=							True
     _A								=							False
     _A								=							False



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[Any]:
          if pipeline_test_casse_name == "TranslationPipelineTests":
               # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
               # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
               return True

          return False



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: Optional[int] 							=						MaMaaaModelTester(self  )
          UpperCAmelCase__							: Optional[Any] 							=						ConfigTester(self ,			config_class=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          self.config_tester.run_common_tests()



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Union[str, Any] 							=						self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
               UpperCAmelCase__							: Optional[Any] 							=						model_class(__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(__UpperCamelCase  )
                    UpperCAmelCase__							: Optional[int] 							=						model_class.from_pretrained(__UpperCamelCase ,			output_loading_info=__UpperCamelCase  )
               self.assertEqual(info["missing_keys"] ,			[]  )



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Union[str, Any] 							=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: str 							=						self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Optional[int] 							=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
               UpperCAmelCase__							: List[str] 							=						model_class(__UpperCamelCase  )
               model.to(__UpperCamelCase  )
               model.eval()

               UpperCAmelCase__							: List[Any] 							=						copy.deepcopy(self._prepare_for_class(__UpperCamelCase ,			__UpperCamelCase  )  )

               if not self.is_encoder_decoder:
                    UpperCAmelCase__							: Tuple 							=						inputs["input_ids"]
                    del inputs["input_ids"]
               else:
                    UpperCAmelCase__							: Tuple 							=						inputs["input_ids"]
                    UpperCAmelCase__							: Any 							=						inputs.get("decoder_input_ids" ,			__UpperCamelCase  )
                    del inputs["input_ids"]
                    inputs.pop("decoder_input_ids" ,			__UpperCamelCase  )

               UpperCAmelCase__							: int 							=						model.get_input_embeddings()
               if not self.is_encoder_decoder:
                    UpperCAmelCase__							: int 							=						wte(__UpperCamelCase  )
               else:
                    UpperCAmelCase__							: Optional[int] 							=						wte(__UpperCamelCase  )
                    UpperCAmelCase__							: int 							=						wte(__UpperCamelCase  )

               with torch.no_grad():
                    model(**__UpperCamelCase  )[0]



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: List[Any] 							=						self.model_tester.prepare_config_and_inputs()
          UpperCAmelCase__							: Any 							=						input_dict["input_ids"]
          UpperCAmelCase__							: Union[str, Any] 							=						input_ids.ne(1  ).to(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						MaMaaaForConditionalGeneration(__UpperCamelCase  ).eval().to(__UpperCamelCase  )
          if torch_device == "cuda":
               model.half()
          model.generate(__UpperCamelCase ,			attention_mask=__UpperCamelCase  )
          model.generate(num_beams=4 ,			do_sample=__UpperCamelCase ,			early_stopping=__UpperCamelCase ,			num_return_sequences=3  )





def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     return torch.tensor(lowerCAmelCase       ,    dtype=torch.long       ,    device=lowerCAmelCase		)


A__      :	Dict          =    1e-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @cached_property
     def  lowerCAmelCase__       (      self  )->					Tuple:
          return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"  )



     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: Union[str, Any] 							=						MaMaaaModel.from_pretrained("facebook/m2m100_418M"  ).to(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]  )
          UpperCAmelCase__							: Optional[int] 							=						_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]  )
          UpperCAmelCase__							: Optional[Any] 							=						prepare_mam_aaa_inputs_dict(model.config ,			__UpperCamelCase ,			__UpperCamelCase  )
          with torch.no_grad():
               UpperCAmelCase__							: Dict 							=						model(**__UpperCamelCase  )[0]
          UpperCAmelCase__							: Any 							=						torch.Size((1, 11, 10_24)  )
          self.assertEqual(output.shape ,			__UpperCamelCase  )
          # change to expected output here
          UpperCAmelCase__							: Dict 							=						torch.tensor(
              [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] ,			device=__UpperCamelCase  )
          self.assertTrue(torch.allclose(output[:, :3, :3] ,			__UpperCamelCase ,			atol=__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Optional[Any] 							=						MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(__UpperCamelCase  )

          # change to intended input
          UpperCAmelCase__							: Union[str, Any] 							=						_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]  )
          UpperCAmelCase__							: List[str] 							=						_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]  )
          UpperCAmelCase__							: List[Any] 							=						prepare_mam_aaa_inputs_dict(model.config ,			__UpperCamelCase ,			__UpperCamelCase  )
          with torch.no_grad():
               UpperCAmelCase__							: Optional[int] 							=						model(**__UpperCamelCase  )[0]
          UpperCAmelCase__							: str 							=						torch.Size((1, 11, model.config.vocab_size)  )
          self.assertEqual(output.shape ,			__UpperCamelCase  )
          # change to expected output here
          UpperCAmelCase__							: Tuple 							=						torch.tensor(
              [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] ,			device=__UpperCamelCase  )
          self.assertTrue(torch.allclose(output[:, :3, :3] ,			__UpperCamelCase ,			atol=__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Union[str, Any] 							=						MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" ,			src_lang="fr" ,			tgt_lang="en"  )

          UpperCAmelCase__							: Tuple 							=						[
              "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
              "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
              "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
              " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
              " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
          ]

          # The below article tests that we don't add any hypotheses outside of the top n_beams
          UpperCAmelCase__							: str 							=						tokenizer(__UpperCamelCase ,			padding=__UpperCamelCase ,			return_tensors="pt"  )

          UpperCAmelCase__							: Dict 							=						model.generate(
              input_ids=dct["input_ids"].to(__UpperCamelCase  ) ,			attention_mask=dct["attention_mask"].to(__UpperCamelCase  ) ,			num_beams=5 ,			forced_bos_token_id=tokenizer.get_lang_id("en"  ) ,			)

          UpperCAmelCase__							: int 							=						[
              "The NSA case highlights the total absence of intelligence debate",
              "I think there are two levels of response from the French government.",
              "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
              " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
              " communications in France.",
          ]

          UpperCAmelCase__							: Dict 							=						tokenizer.batch_decode(
              hypotheses_batch.tolist() ,			clean_up_tokenization_spaces=__UpperCamelCase ,			skip_special_tokens=__UpperCamelCase  )
          assert generated == expected_en





"""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__      :	Tuple          =    logging.get_logger(__name__)



def 							a__ (				lowerCAmelCase	:       nn.ModuleList       ,    lowerCAmelCase	:       nn.ModuleList       ,    lowerCAmelCase	:       List[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: 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__      :	List[Any]          =    {
    # 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__      :	List[Any]          =    {
    # 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 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Dict		):






     '''simple docstring'''
     try:
          UpperCAmelCase__							: Tuple 							=						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 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Tuple		):






     '''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 							a__ (				lowerCAmelCase	:       Union[str, PreTrainedModel]       ,    lowerCAmelCase	:       Union[str, Path] = "student"       ,    lowerCAmelCase	:       Union[int, None] = None       ,    lowerCAmelCase	:       Union[int, None] = None       ,    lowerCAmelCase	:       List[str]=False       ,    lowerCAmelCase	:       List[str]=None       ,    lowerCAmelCase	:       List[str]=None       ,    **lowerCAmelCase	:       List[str]       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: List[str] 							=						"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
          UpperCAmelCase__							: List[str] 							=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase		).eval()
     else:
          assert isinstance(lowerCAmelCase       ,    lowerCAmelCase		), F"teacher must be a model or string got type {type(lowerCAmelCase		)}"
     UpperCAmelCase__							: int 							=						teacher.config.to_diff_dict()

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



