


'''simple docstring'''






import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_MAPPING,
							    SegformerForImageClassification,
							    SegformerForSemanticSegmentation,
							    SegformerModel,
							)
							from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import SegformerImageProcessor
class 						__UpperCamelCase	(						lowercase__							):





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							int			       =						self.config_class(**self.inputs_dict			)
									self.parent.assertTrue(hasattr(_UpperCamelCase  ,"""hidden_sizes"""			)			)
									self.parent.assertTrue(hasattr(_UpperCamelCase  ,"""num_attention_heads"""			)			)
									self.parent.assertTrue(hasattr(_UpperCamelCase  ,"""num_encoder_blocks"""			)			)





class 						__UpperCamelCase	:





							def __init__(  self		:str  ,_UpperCamelCase		:int  ,_UpperCamelCase		:List[str]=1_3  ,_UpperCamelCase		:Dict=6_4  ,_UpperCamelCase		:List[str]=3  ,_UpperCamelCase		:List[str]=4  ,_UpperCamelCase		:Any=[2, 2, 2, 2]  ,_UpperCamelCase		:Optional[Any]=[8, 4, 2, 1]  ,_UpperCamelCase		:str=[1_6, 3_2, 6_4, 1_2_8]  ,_UpperCamelCase		:List[str]=[1, 4, 8, 1_6]  ,_UpperCamelCase		:Dict=[1, 2, 4, 8]  ,_UpperCamelCase		:Union[str, Any]=True  ,_UpperCamelCase		:Optional[Any]=True  ,_UpperCamelCase		:Optional[Any]="gelu"  ,_UpperCamelCase		:Any=0.1  ,_UpperCamelCase		:Tuple=0.1  ,_UpperCamelCase		:List[Any]=0.02  ,_UpperCamelCase		:List[str]=3  ,_UpperCamelCase		:Dict=None  ,):
									snake_case_		:							List[str]			       =						parent
									snake_case_		:							Optional[Any]			       =						batch_size
									snake_case_		:							Dict			       =						image_size
									snake_case_		:							List[Any]			       =						num_channels
									snake_case_		:							Optional[int]			       =						num_encoder_blocks
									snake_case_		:							str			       =						sr_ratios
									snake_case_		:							List[Any]			       =						depths
									snake_case_		:							Optional[Any]			       =						hidden_sizes
									snake_case_		:							int			       =						downsampling_rates
									snake_case_		:							str			       =						num_attention_heads
									snake_case_		:							Tuple			       =						is_training
									snake_case_		:							List[str]			       =						use_labels
									snake_case_		:							str			       =						hidden_act
									snake_case_		:							Optional[Any]			       =						hidden_dropout_prob
									snake_case_		:							List[str]			       =						attention_probs_dropout_prob
									snake_case_		:							List[Any]			       =						initializer_range
									snake_case_		:							Optional[Any]			       =						num_labels
									snake_case_		:							List[Any]			       =						scope





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Any			       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

									snake_case_		:							int			       =						None
									if self.use_labels:
											snake_case_		:							Union[str, Any]			       =						ids_tensor([self.batch_size, self.image_size, self.image_size]  ,self.num_labels			)

									snake_case_		:							Dict			       =						self.get_config()
									return config, pixel_values, labels





							def 		a__   (  self		:Any			):
									return SegformerConfig(
									    image_size=self.image_size  ,num_channels=self.num_channels  ,num_encoder_blocks=self.num_encoder_blocks  ,depths=self.depths  ,hidden_sizes=self.hidden_sizes  ,num_attention_heads=self.num_attention_heads  ,hidden_act=self.hidden_act  ,hidden_dropout_prob=self.hidden_dropout_prob  ,attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,initializer_range=self.initializer_range  ,)





							def 		a__   (  self		:Any  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Any			):
									snake_case_		:							Tuple			       =						SegformerModel(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							int			       =						model(_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						self.image_size // (self.downsampling_rates[-1] * 2)
									self.parent.assertEqual(
									    result.last_hidden_state.shape  ,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Tuple			):
									snake_case_		:							Union[str, Any]			       =						self.num_labels
									snake_case_		:							Tuple			       =						SegformerForSemanticSegmentation(_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							Optional[int]			       =						model(_UpperCamelCase			)
									self.parent.assertEqual(
									    result.logits.shape  ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
									snake_case_		:							Tuple			       =						model(_UpperCamelCase  ,labels=_UpperCamelCase			)
									self.parent.assertEqual(
									    result.logits.shape  ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
									self.parent.assertGreater(result.loss  ,0.0			)





							def 		a__   (  self		:str  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:List[str]			):
									snake_case_		:							str			       =						1
									snake_case_		:							Any			       =						SegformerForSemanticSegmentation(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							Optional[Any]			       =						torch.randint(0  ,1  ,(self.batch_size, self.image_size, self.image_size)			).to(_UpperCamelCase			)
									snake_case_		:							str			       =						model(_UpperCamelCase  ,labels=_UpperCamelCase			)
									self.parent.assertGreater(result.loss  ,0.0			)





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Dict			       =						self.prepare_config_and_inputs()
									snake_case_       ,      snake_case_       ,      snake_case_		:							Any			       =						config_and_inputs
									snake_case_		:							List[Any]			       =						{"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_torch
class 						__UpperCamelCase	(						lowercase__						,  lowercase__						,  unittest.TestCase							):
							lowercase			:		int   		=  (
							    (
							        SegformerModel,
							        SegformerForSemanticSegmentation,
							        SegformerForImageClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase			:		Dict   		=  (
							    {
							        'feature-extraction': SegformerModel,
							        'image-classification': SegformerForImageClassification,
							        'image-segmentation': SegformerForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowercase			:		Any   		=  True
							lowercase			:		Union[str, Any]   		=  False
							lowercase			:		Tuple   		=  False
							lowercase			:		Dict   		=  False





							def 		a__   (  self		:Dict			):
									snake_case_		:							Dict			       =						SegformerModelTester(self			)
									snake_case_		:							str			       =						SegformerConfigTester(self  ,config_class=_UpperCamelCase			)





							def 		a__   (  self		:Dict			):
									self.config_tester.run_common_tests()





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Tuple			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							List[str]			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_binary_image_segmentation(*_UpperCamelCase			)





							def 		a__   (  self		:Any			):
									snake_case_		:							Tuple			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_segmentation(*_UpperCamelCase			)





							@unittest.skip("""SegFormer does not use inputs_embeds"""			)
							def 		a__   (  self		:Optional[Any]			):
									pass





							@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""			)
							def 		a__   (  self		:Optional[int]			):
									pass





							def 		a__   (  self		:str			):
									snake_case_       ,      snake_case_		:							str			       =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											snake_case_		:							str			       =						model_class(_UpperCamelCase			)
											snake_case_		:							Optional[int]			       =						inspect.signature(model.forward			)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											snake_case_		:							int			       =						[*signature.parameters.keys()]

											snake_case_		:							int			       =						["""pixel_values"""]
											self.assertListEqual(arg_names[:1]  ,_UpperCamelCase			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_       ,      snake_case_		:							str			       =						self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_		:							int			       =						True

									for model_class in self.all_model_classes:
											snake_case_		:							int			       =						True
											snake_case_		:							str			       =						False
											snake_case_		:							str			       =						True
											snake_case_		:							Optional[int]			       =						model_class(_UpperCamelCase			)
											model.to(_UpperCamelCase			)
											model.eval()
											with torch.no_grad():
													snake_case_		:							Optional[int]			       =						model(**self._prepare_for_class(_UpperCamelCase  ,_UpperCamelCase			)			)
											snake_case_		:							Any			       =						outputs.attentions

											snake_case_		:							Dict			       =						sum(self.model_tester.depths			)
											self.assertEqual(len(_UpperCamelCase			)  ,_UpperCamelCase			)

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											snake_case_		:							Optional[int]			       =						True
											snake_case_		:							List[str]			       =						model_class(_UpperCamelCase			)
											model.to(_UpperCamelCase			)
											model.eval()
											with torch.no_grad():
													snake_case_		:							Union[str, Any]			       =						model(**self._prepare_for_class(_UpperCamelCase  ,_UpperCamelCase			)			)
											snake_case_		:							Dict			       =						outputs.attentions

											self.assertEqual(len(_UpperCamelCase			)  ,_UpperCamelCase			)

											# verify the first attentions (first block, first layer)
											snake_case_		:							Optional[int]			       =						(self.model_tester.image_size // 4) ** 2
											snake_case_		:							int			       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
											self.assertListEqual(
											    list(attentions[0].shape[-3:]			)  ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]  ,)

											# verify the last attentions (last block, last layer)
											snake_case_		:							List[Any]			       =						(self.model_tester.image_size // 3_2) ** 2
											snake_case_		:							int			       =						(self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
											self.assertListEqual(
											    list(attentions[-1].shape[-3:]			)  ,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]  ,)
											snake_case_		:							int			       =						len(_UpperCamelCase			)

											# Check attention is always last and order is fine
											snake_case_		:							Any			       =						True
											snake_case_		:							int			       =						True
											snake_case_		:							int			       =						model_class(_UpperCamelCase			)
											model.to(_UpperCamelCase			)
											model.eval()
											with torch.no_grad():
													snake_case_		:							Union[str, Any]			       =						model(**self._prepare_for_class(_UpperCamelCase  ,_UpperCamelCase			)			)

											self.assertEqual(out_len + 1  ,len(_UpperCamelCase			)			)

											snake_case_		:							Optional[Any]			       =						outputs.attentions

											self.assertEqual(len(_UpperCamelCase			)  ,_UpperCamelCase			)
											# verify the first attentions (first block, first layer)
											snake_case_		:							Optional[int]			       =						(self.model_tester.image_size // 4) ** 2
											snake_case_		:							List[str]			       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
											self.assertListEqual(
											    list(self_attentions[0].shape[-3:]			)  ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]  ,)





							def 		a__   (  self		:Optional[Any]			):
									def check_hidden_states_output(_UpperCamelCase		:Tuple  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int			):
											snake_case_		:							List[str]			       =						model_class(_UpperCamelCase			)
											model.to(_UpperCamelCase			)
											model.eval()

											with torch.no_grad():
													snake_case_		:							Dict			       =						model(**self._prepare_for_class(_UpperCamelCase  ,_UpperCamelCase			)			)

											snake_case_		:							Any			       =						outputs.hidden_states

											snake_case_		:							Union[str, Any]			       =						self.model_tester.num_encoder_blocks
											self.assertEqual(len(_UpperCamelCase			)  ,_UpperCamelCase			)

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]			)  ,[
											        self.model_tester.hidden_sizes[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]  ,)

									snake_case_       ,      snake_case_		:							Any			       =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											snake_case_		:							Optional[int]			       =						True
											check_hidden_states_output(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											snake_case_		:							List[Any]			       =						True

											check_hidden_states_output(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)





							def 		a__   (  self		:Any			):
									if not self.model_tester.is_training:
											return

									snake_case_       ,      snake_case_		:							List[str]			       =						self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_		:							Tuple			       =						True

									for model_class in self.all_model_classes:
											if model_class in get_values(_UpperCamelCase			):
													continue

											snake_case_		:							Any			       =						model_class(_UpperCamelCase			)
											model.to(_UpperCamelCase			)
											model.train()
											snake_case_		:							int			       =						self._prepare_for_class(_UpperCamelCase  ,_UpperCamelCase  ,return_labels=_UpperCamelCase			)
											snake_case_		:							Dict			       =						model(**_UpperCamelCase			).loss
											loss.backward()





							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
							def 		a__   (  self		:int			):
									pass





							@slow
							def 		a__   (  self		:List[str]			):
									for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_		:							List[Any]			       =						SegformerModel.from_pretrained(_UpperCamelCase			)
											self.assertIsNotNone(_UpperCamelCase			)





def      UpperCAmelCase      (		):







		'''simple docstring'''
		snake_case_		:							str			       =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
		return image






@require_torch
class 						__UpperCamelCase	(						unittest.TestCase							):





							@slow
							def 		a__   (  self		:Tuple			):
									# only resize + normalize
									snake_case_		:							str			       =						SegformerImageProcessor(
									    image_scale=(5_1_2, 5_1_2)  ,keep_ratio=_UpperCamelCase  ,align=_UpperCamelCase  ,do_random_crop=_UpperCamelCase			)
									snake_case_		:							str			       =						SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""			).to(
									    _UpperCamelCase			)

									snake_case_		:							List[Any]			       =						prepare_img()
									snake_case_		:							int			       =						image_processor(images=_UpperCamelCase  ,return_tensors="""pt"""			)
									snake_case_		:							List[str]			       =						encoded_inputs.pixel_values.to(_UpperCamelCase			)

									with torch.no_grad():
											snake_case_		:							List[str]			       =						model(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)			)
									self.assertEqual(outputs.logits.shape  ,_UpperCamelCase			)

									snake_case_		:							Optional[Any]			       =						torch.tensor(
									    [
									        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
									        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
									        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
									    ]			).to(_UpperCamelCase			)
									self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]  ,_UpperCamelCase  ,atol=1E-4			)			)





							@slow
							def 		a__   (  self		:Any			):
									# only resize + normalize
									snake_case_		:							str			       =						SegformerImageProcessor(
									    image_scale=(5_1_2, 5_1_2)  ,keep_ratio=_UpperCamelCase  ,align=_UpperCamelCase  ,do_random_crop=_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						SegformerForSemanticSegmentation.from_pretrained(
									    """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""			).to(_UpperCamelCase			)

									snake_case_		:							Union[str, Any]			       =						prepare_img()
									snake_case_		:							Dict			       =						image_processor(images=_UpperCamelCase  ,return_tensors="""pt"""			)
									snake_case_		:							Dict			       =						encoded_inputs.pixel_values.to(_UpperCamelCase			)

									with torch.no_grad():
											snake_case_		:							int			       =						model(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)			)
									self.assertEqual(outputs.logits.shape  ,_UpperCamelCase			)

									snake_case_		:							Dict			       =						torch.tensor(
									    [
									        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
									        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
									        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
									    ]			).to(_UpperCamelCase			)
									self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]  ,_UpperCamelCase  ,atol=1E-1			)			)





							@slow
							def 		a__   (  self		:Optional[Any]			):
									# only resize + normalize
									snake_case_		:							Union[str, Any]			       =						SegformerImageProcessor(
									    image_scale=(5_1_2, 5_1_2)  ,keep_ratio=_UpperCamelCase  ,align=_UpperCamelCase  ,do_random_crop=_UpperCamelCase			)
									snake_case_		:							Tuple			       =						SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""			).to(
									    _UpperCamelCase			)

									snake_case_		:							List[str]			       =						prepare_img()
									snake_case_		:							str			       =						image_processor(images=_UpperCamelCase  ,return_tensors="""pt"""			)
									snake_case_		:							Optional[int]			       =						encoded_inputs.pixel_values.to(_UpperCamelCase			)

									with torch.no_grad():
											snake_case_		:							str			       =						model(_UpperCamelCase			)

									snake_case_		:							int			       =						outputs.logits.detach().cpu()

									snake_case_		:							Tuple			       =						image_processor.post_process_semantic_segmentation(outputs=_UpperCamelCase  ,target_sizes=[(5_0_0, 3_0_0)]			)
									snake_case_		:							Union[str, Any]			       =						torch.Size((5_0_0, 3_0_0)			)
									self.assertEqual(segmentation[0].shape  ,_UpperCamelCase			)

									snake_case_		:							Any			       =						image_processor.post_process_semantic_segmentation(outputs=_UpperCamelCase			)
									snake_case_		:							str			       =						torch.Size((1_2_8, 1_2_8)			)
									self.assertEqual(segmentation[0].shape  ,_UpperCamelCase			)



'''simple docstring'''






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

import fire
from torch import nn

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


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





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







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


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





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







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





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







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





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







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

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

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

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

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

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

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

		return student, e_layers_to_copy, d_layers_to_copy


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