
"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


A__      :	List[Any]          =    get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

A__      :	Tuple          =    256_047
A__      :	Dict          =    256_145







@require_sentencepiece
@require_tokenizers
class   _lowercase						(       UpperCAmelCase__	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							NllbTokenizer
     _A								=							NllbTokenizerFast
     _A								=							True
     _A								=							True
     _A								=							{}



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__							: Union[str, Any] 							=						NllbTokenizer(lowerCamelCase__ ,			keep_accents=lowerCamelCase__  )
          tokenizer.save_pretrained(self.tmpdirname  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Dict 							=						NllbTokenizer(lowerCamelCase__ ,			keep_accents=lowerCamelCase__  )

          UpperCAmelCase__							: Tuple 							=						tokenizer.tokenize("This is a test"  )
          self.assertListEqual(lowerCamelCase__ ,			["▁This", "▁is", "▁a", "▁t", "est"]  )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(lowerCamelCase__  ) ,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,			)

          UpperCAmelCase__							: str 							=						tokenizer.tokenize("I was born in 92000, and this is falsé."  )
          self.assertListEqual(
              lowerCamelCase__ ,			[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "9",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "é",
                  ".",
              ] ,			)
          UpperCAmelCase__							: Any 							=						tokenizer.convert_tokens_to_ids(lowerCamelCase__  )
          self.assertListEqual(
              lowerCamelCase__ ,			[
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              ] ,			)

          UpperCAmelCase__							: Optional[Any] 							=						tokenizer.convert_ids_to_tokens(lowerCamelCase__  )
          self.assertListEqual(
              lowerCamelCase__ ,			[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "<unk>",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "<unk>",
                  ".",
              ] ,			)



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Dict 							=						(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                    UpperCAmelCase__							: Any 							=						self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ ,			**lowerCamelCase__  )
                    UpperCAmelCase__							: Any 							=						self.tokenizer_class.from_pretrained(lowerCamelCase__ ,			**lowerCamelCase__  )

                    UpperCAmelCase__							: int 							=						tempfile.mkdtemp()

                    UpperCAmelCase__							: Tuple 							=						tokenizer_r.save_pretrained(lowerCamelCase__  )
                    UpperCAmelCase__							: Any 							=						tokenizer_p.save_pretrained(lowerCamelCase__  )

                    # Checks it save with the same files + the tokenizer.json file for the fast one
                    self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files  )  )
                    UpperCAmelCase__							: List[Any] 							=						tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f  )
                    self.assertSequenceEqual(lowerCamelCase__ ,			lowerCamelCase__  )

                    # Checks everything loads correctly in the same way
                    UpperCAmelCase__							: Tuple 							=						tokenizer_r.from_pretrained(lowerCamelCase__  )
                    UpperCAmelCase__							: Optional[Any] 							=						tokenizer_p.from_pretrained(lowerCamelCase__  )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(lowerCamelCase__ ,			lowerCamelCase__  )  )

                    shutil.rmtree(lowerCamelCase__  )

                    # Save tokenizer rust, legacy_format=True
                    UpperCAmelCase__							: Optional[Any] 							=						tempfile.mkdtemp()

                    UpperCAmelCase__							: List[str] 							=						tokenizer_r.save_pretrained(lowerCamelCase__ ,			legacy_format=lowerCamelCase__  )
                    UpperCAmelCase__							: List[str] 							=						tokenizer_p.save_pretrained(lowerCamelCase__  )

                    # Checks it save with the same files
                    self.assertSequenceEqual(lowerCamelCase__ ,			lowerCamelCase__  )

                    # Checks everything loads correctly in the same way
                    UpperCAmelCase__							: Union[str, Any] 							=						tokenizer_r.from_pretrained(lowerCamelCase__  )
                    UpperCAmelCase__							: Optional[Any] 							=						tokenizer_p.from_pretrained(lowerCamelCase__  )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(lowerCamelCase__ ,			lowerCamelCase__  )  )

                    shutil.rmtree(lowerCamelCase__  )

                    # Save tokenizer rust, legacy_format=False
                    UpperCAmelCase__							: Optional[int] 							=						tempfile.mkdtemp()

                    UpperCAmelCase__							: Dict 							=						tokenizer_r.save_pretrained(lowerCamelCase__ ,			legacy_format=lowerCamelCase__  )
                    UpperCAmelCase__							: List[Any] 							=						tokenizer_p.save_pretrained(lowerCamelCase__  )

                    # Checks it saved the tokenizer.json file
                    self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files  )  )

                    # Checks everything loads correctly in the same way
                    UpperCAmelCase__							: int 							=						tokenizer_r.from_pretrained(lowerCamelCase__  )
                    UpperCAmelCase__							: int 							=						tokenizer_p.from_pretrained(lowerCamelCase__  )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(lowerCamelCase__ ,			lowerCamelCase__  )  )

                    shutil.rmtree(lowerCamelCase__  )



     @require_torch
     def  lowerCAmelCase__       (      self  )->					List[str]:







          if not self.test_seqaseq:
               return

          UpperCAmelCase__							: int 							=						self.get_tokenizers()
          for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"  ):
                    # Longer text that will definitely require truncation.
                    UpperCAmelCase__							: Tuple 							=						[
                        " UN Chief Says There Is No Military Solution in Syria",
                        " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
                        " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
                        " will only worsen the violence and misery for millions of people.",
                    ]
                    UpperCAmelCase__							: Dict 							=						[
                        "Şeful ONU declară că nu există o soluţie militară în Siria",
                        "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
                        " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
                        " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
                    ]
                    try:
                         UpperCAmelCase__							: str 							=						tokenizer.prepare_seqaseq_batch(
                             src_texts=lowerCamelCase__ ,			tgt_texts=lowerCamelCase__ ,			max_length=3 ,			max_target_length=10 ,			return_tensors="pt" ,			src_lang="eng_Latn" ,			tgt_lang="ron_Latn" ,			)
                    except NotImplementedError:
                         return
                    self.assertEqual(batch.input_ids.shape[1] ,			3  )
                    self.assertEqual(batch.labels.shape[1] ,			10  )
                    # max_target_length will default to max_length if not specified
                    UpperCAmelCase__							: Union[str, Any] 							=						tokenizer.prepare_seqaseq_batch(
                        lowerCamelCase__ ,			tgt_texts=lowerCamelCase__ ,			max_length=3 ,			return_tensors="pt"  )
                    self.assertEqual(batch.input_ids.shape[1] ,			3  )
                    self.assertEqual(batch.labels.shape[1] ,			3  )

                    UpperCAmelCase__							: Union[str, Any] 							=						tokenizer.prepare_seqaseq_batch(
                        src_texts=lowerCamelCase__ ,			max_length=3 ,			max_target_length=10 ,			return_tensors="pt"  )
                    self.assertEqual(batch_encoder_only.input_ids.shape[1] ,			3  )
                    self.assertEqual(batch_encoder_only.attention_mask.shape[1] ,			3  )
                    self.assertNotIn("decoder_input_ids" ,			lowerCamelCase__  )



     @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."  )
     def  lowerCAmelCase__       (      self  )->					Tuple:
          pass



     def  lowerCAmelCase__       (      self  )->					str:







          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                    UpperCAmelCase__							: str 							=						[AddedToken("<special>" ,			lstrip=lowerCamelCase__  )]

                    UpperCAmelCase__							: int 							=						self.rust_tokenizer_class.from_pretrained(
                        lowerCamelCase__ ,			additional_special_tokens=lowerCamelCase__ ,			**lowerCamelCase__  )
                    UpperCAmelCase__							: str 							=						tokenizer_r.encode("Hey this is a <special> token"  )

                    UpperCAmelCase__							: str 							=						tokenizer_r.encode("<special>" ,			add_special_tokens=lowerCamelCase__  )[0]

                    self.assertTrue(special_token_id in r_output  )

                    if self.test_slow_tokenizer:
                         UpperCAmelCase__							: Optional[int] 							=						self.rust_tokenizer_class.from_pretrained(
                             lowerCamelCase__ ,			additional_special_tokens=lowerCamelCase__ ,			**lowerCamelCase__ ,			)
                         UpperCAmelCase__							: Optional[int] 							=						self.tokenizer_class.from_pretrained(
                             lowerCamelCase__ ,			additional_special_tokens=lowerCamelCase__ ,			**lowerCamelCase__  )

                         UpperCAmelCase__							: Tuple 							=						tokenizer_p.encode("Hey this is a <special> token"  )

                         UpperCAmelCase__							: Dict 							=						tokenizer_cr.encode("Hey this is a <special> token"  )

                         self.assertEqual(lowerCamelCase__ ,			lowerCamelCase__  )
                         self.assertEqual(lowerCamelCase__ ,			lowerCamelCase__  )
                         self.assertTrue(special_token_id in p_output  )
                         self.assertTrue(special_token_id in cr_output  )







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


     '''simple docstring'''





     _A								=							"facebook/nllb-200-distilled-600M"
     _A								=							[
         " UN Chief Says There Is No Military Solution in Syria",
         " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
     ]
     _A								=							[
         "Şeful ONU declară că nu există o soluţie militară în Siria",
         "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
         " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
         " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
     ]
     _A								=							[
         25_6047,
         1_6297,
         13_4408,
         8165,
         24_8066,
         1_4734,
         950,
         1135,
         10_5721,
         3573,
         83,
         2_7352,
         108,
         4_9486,
         2,
     ]



     @classmethod
     def  lowerCAmelCase__       (      cls  )->					Union[str, Any]:
          UpperCAmelCase__							: NllbTokenizer 							=						NllbTokenizer.from_pretrained(
              cls.checkpoint_name ,			src_lang="eng_Latn" ,			tgt_lang="ron_Latn"  )
          UpperCAmelCase__							: str 							=						1
          return cls



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"] ,			25_60_01  )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"] ,			25_60_02  )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"] ,			25_60_57  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Optional[int] 							=						self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens ,			lowerCamelCase__  )



     def  lowerCAmelCase__       (      self  )->					List[str]:
          self.assertIn(lowerCamelCase__ ,			self.tokenizer.all_special_ids  )
          # fmt: off
          UpperCAmelCase__							: List[Any] 							=						[RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
          # fmt: on

          UpperCAmelCase__							: Optional[Any] 							=						self.tokenizer.decode(lowerCamelCase__ ,			skip_special_tokens=lowerCamelCase__  )
          UpperCAmelCase__							: Dict 							=						self.tokenizer.decode(generated_ids[1:] ,			skip_special_tokens=lowerCamelCase__  )
          self.assertEqual(lowerCamelCase__ ,			lowerCamelCase__  )
          self.assertNotIn(self.tokenizer.eos_token ,			lowerCamelCase__  )



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Optional[int] 							=						["this is gunna be a long sentence " * 20]
          assert isinstance(src_text[0] ,			lowerCamelCase__  )
          UpperCAmelCase__							: Tuple 							=						10
          UpperCAmelCase__							: Union[str, Any] 							=						self.tokenizer(lowerCamelCase__ ,			max_length=lowerCamelCase__ ,			truncation=lowerCamelCase__  ).input_ids[0]
          self.assertEqual(ids[-1] ,			2  )
          self.assertEqual(ids[0] ,			lowerCamelCase__  )
          self.assertEqual(len(lowerCamelCase__  ) ,			lowerCamelCase__  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]  ) ,			[25_62_03, 3]  )



     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: Optional[int] 							=						tempfile.mkdtemp()
          UpperCAmelCase__							: Any 							=						self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(lowerCamelCase__  )
          UpperCAmelCase__							: int 							=						NllbTokenizer.from_pretrained(lowerCamelCase__  )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,			lowerCamelCase__  )



     @require_torch
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: List[Any] 							=						self.tokenizer(
              self.src_text ,			text_target=self.tgt_text ,			padding=lowerCamelCase__ ,			truncation=lowerCamelCase__ ,			max_length=len(self.expected_src_tokens  ) ,			return_tensors="pt" ,			)
          UpperCAmelCase__							: Optional[int] 							=						shift_tokens_right(
              batch["labels"] ,			self.tokenizer.pad_token_id ,			self.tokenizer.lang_code_to_id["ron_Latn"]  )

          self.assertIsInstance(lowerCamelCase__ ,			lowerCamelCase__  )

          self.assertEqual((2, 15) ,			batch.input_ids.shape  )
          self.assertEqual((2, 15) ,			batch.attention_mask.shape  )
          UpperCAmelCase__							: str 							=						batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens ,			lowerCamelCase__  )
          self.assertEqual(lowerCamelCase__ ,			batch.decoder_input_ids[0, 0]  )  # EOS
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens ,			[EN_CODE]  )
          self.assertEqual(self.tokenizer.suffix_tokens ,			[self.tokenizer.eos_token_id]  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Optional[Any] 							=						self.tokenizer(self.src_text ,			padding=lowerCamelCase__ ,			truncation=lowerCamelCase__ ,			max_length=3 ,			return_tensors="pt"  )
          UpperCAmelCase__							: List[Any] 							=						self.tokenizer(
              text_target=self.tgt_text ,			padding=lowerCamelCase__ ,			truncation=lowerCamelCase__ ,			max_length=10 ,			return_tensors="pt"  )
          UpperCAmelCase__							: List[Any] 							=						targets["input_ids"]
          UpperCAmelCase__							: Tuple 							=						shift_tokens_right(
              lowerCamelCase__ ,			self.tokenizer.pad_token_id ,			decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] ,			)

          self.assertEqual(batch.input_ids.shape[1] ,			3  )
          self.assertEqual(batch.decoder_input_ids.shape[1] ,			10  )



     @require_torch
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Optional[Any] 							=						self.tokenizer._build_translation_inputs(
              "A test" ,			return_tensors="pt" ,			src_lang="eng_Latn" ,			tgt_lang="fra_Latn"  )

          self.assertEqual(
              nested_simplify(lowerCamelCase__  ) ,			{
                  # A, test, EOS, en_XX
                  "input_ids": [[25_60_47, 70, 73_56, 2]],
                  "attention_mask": [[1, 1, 1, 1]],
                  # ar_AR
                  "forced_bos_token_id": 25_60_57,
              } ,			)



     @require_torch
     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Union[str, Any] 							=						True
          UpperCAmelCase__							: Optional[Any] 							=						self.tokenizer(
              "UN Chief says there is no military solution in Syria" ,			src_lang="eng_Latn" ,			tgt_lang="fra_Latn"  )
          self.assertEqual(
              inputs.input_ids ,			[1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]  )

          UpperCAmelCase__							: Union[str, Any] 							=						False
          UpperCAmelCase__							: Dict 							=						self.tokenizer(
              "UN Chief says there is no military solution in Syria" ,			src_lang="eng_Latn" ,			tgt_lang="fra_Latn"  )
          self.assertEqual(
              inputs.input_ids ,			[25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]  )





"""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)



