




import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import (
		    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SqueezeBertForMaskedLM,
		    SqueezeBertForMultipleChoice,
		    SqueezeBertForQuestionAnswering,
		    SqueezeBertForSequenceClassification,
		    SqueezeBertForTokenClassification,
		    SqueezeBertModel,
		)




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Optional[int]=13					,      __lowerCamelCase				:     List[str]=7					,      __lowerCamelCase				:     Union[str, Any]=True					,      __lowerCamelCase				:     Dict=True					,      __lowerCamelCase				:     Optional[int]=False					,      __lowerCamelCase				:     str=True					,      __lowerCamelCase				:     List[str]=99					,      __lowerCamelCase				:     List[Any]=32					,      __lowerCamelCase				:     Any=5					,      __lowerCamelCase				:     str=4					,      __lowerCamelCase				:     List[Any]=64					,      __lowerCamelCase				:     Dict="gelu"					,      __lowerCamelCase				:     int=0.1					,      __lowerCamelCase				:     List[str]=0.1					,      __lowerCamelCase				:     Union[str, Any]=5_12					,      __lowerCamelCase				:     List[Any]=16					,      __lowerCamelCase				:     str=2					,      __lowerCamelCase				:     Optional[Any]=0.02					,      __lowerCamelCase				:     Optional[int]=3					,      __lowerCamelCase				:     List[Any]=4					,      __lowerCamelCase				:     Tuple=None					,      __lowerCamelCase				:     List[Any]=2					,      __lowerCamelCase				:     List[Any]=2					,      __lowerCamelCase				:     Optional[Any]=2					,      __lowerCamelCase				:     str=2					,      __lowerCamelCase				:     Dict=4					,      __lowerCamelCase				:     Optional[int]=1					,      )					->     Any:
							a    =			parent
							a    =			batch_size
							a    =			seq_length
							a    =			is_training
							a    =			use_input_mask
							a    =			use_token_type_ids
							a    =			use_labels
							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			intermediate_size
							a    =			hidden_act
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			type_sequence_label_size
							a    =			initializer_range
							a    =			num_labels
							a    =			num_choices
							a    =			scope
							a    =			q_groups
							a    =			k_groups
							a    =			v_groups
							a    =			post_attention_groups
							a    =			intermediate_groups
							a    =			output_groups





				def 							__UpperCAmelCase							(      self				:     int							)					->     Optional[int]:
							a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

							a    =			None
							if self.use_input_mask:
										a    =			random_attention_mask([self.batch_size, self.seq_length]							)

							a    =			None
							a    =			None
							a    =			None
							if self.use_labels:
										a    =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size							)
										a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels							)
										a    =			ids_tensor([self.batch_size]					,      self.num_choices							)

							a    =			self.get_config()

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							return SqueezeBertConfig(
							    embedding_size=self.hidden_size					,      vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      attention_probs_dropout_prob=self.hidden_dropout_prob					,      attention_dropout=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      initializer_range=self.initializer_range					,      q_groups=self.q_groups					,      k_groups=self.k_groups					,      v_groups=self.v_groups					,      post_attention_groups=self.post_attention_groups					,      intermediate_groups=self.intermediate_groups					,      output_groups=self.output_groups					,      )





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[Any]							)					->     str:
							a    =			SqueezeBertModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      __lowerCamelCase							)
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     List[Any]							)					->     Dict:
							a    =			SqueezeBertForMaskedLM(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     str							)					->     Union[str, Any]:
							a    =			SqueezeBertForQuestionAnswering(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(
							    __lowerCamelCase					,      attention_mask=__lowerCamelCase					,      start_positions=__lowerCamelCase					,      end_positions=__lowerCamelCase							)
							self.parent.assertEqual(result.start_logits.shape					,      (self.batch_size, self.seq_length)							)
							self.parent.assertEqual(result.end_logits.shape					,      (self.batch_size, self.seq_length)							)





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     str							)					->     Any:
							a    =			self.num_labels
							a    =			SqueezeBertForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Tuple							)					->     Any:
							a    =			self.num_labels
							a    =			SqueezeBertForTokenClassification(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Tuple							)					->     Optional[int]:
							a    =			self.num_choices
							a    =			SqueezeBertForMultipleChoice(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			input_ids.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
							a    =			input_mask.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
							a    =			model(
							    __lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase					,      )
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_choices)							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[int]:
							a    =			self.prepare_config_and_inputs()
							((a)    ,		(a)    ,		(a)    ,		(a)    ,		(a)    ,		(a))    =			config_and_inputs
							a    =			{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_torch
class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					(
				    (
				        SqueezeBertModel,
				        SqueezeBertForMaskedLM,
				        SqueezeBertForMultipleChoice,
				        SqueezeBertForQuestionAnswering,
				        SqueezeBertForSequenceClassification,
				        SqueezeBertForTokenClassification,
				    )
				    if is_torch_available()
				    else None
				)
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					(
				    {
				        """feature-extraction""": SqueezeBertModel,
				        """fill-mask""": SqueezeBertForMaskedLM,
				        """question-answering""": SqueezeBertForQuestionAnswering,
				        """text-classification""": SqueezeBertForSequenceClassification,
				        """token-classification""": SqueezeBertForTokenClassification,
				        """zero-shot""": SqueezeBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					False
				SCREAMING_SNAKE_CASE_       :							str							 =					True
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					False





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Any:
							a    =			SqueezeBertModelTester(self							)
							a    =			ConfigTester(self					,      config_class=__lowerCamelCase					,      dim=37							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     int:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_masked_lm(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Optional[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_question_answering(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Any:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     str:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_token_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Any							)					->     Any:
							for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a    =			SqueezeBertModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)




@require_sentencepiece
@require_tokenizers
@require_torch
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(      self				:     str							)					->     Union[str, Any]:
							a    =			SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"							)

							a    =			torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]							)
							a    =			model(__lowerCamelCase							)[0]
							a    =			torch.Size((1, 3)							)
							self.assertEqual(output.shape					,      __lowerCamelCase							)
							a    =			torch.tensor([[0.6_401, -0.0_349, -0.6_041]]							)
							self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1e-4							)							)













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


UpperCAmelCase						:   Any			  =     logging.get_logger(__name__)







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      None:
					__A	:						int       =				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()  )


UpperCAmelCase						:   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))},
}
UpperCAmelCase						:   Optional[int]			  =     {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Dict:
					try:
										__A	:						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(a  )  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      List[int]:
					if n_student > n_teacher:
										raise ValueError(F"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}"""  )
					elif n_teacher == n_student:
										return list(range(a  )  )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def  _SCREAMING_SNAKE_CASE				(   a					,						a = "student"					,						a = None					,						a = None					,						a=False					,						a=None					,						a=None					,						**a					,						)      ->      Tuple[PreTrainedModel, List[int], List[int]]:
					__A	:						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(a					,						a  ):
										AutoTokenizer.from_pretrained(a  ).save_pretrained(a  )  # purely for convenience
										__A	:						Optional[int]       =				AutoModelForSeqaSeqLM.from_pretrained(a  ).eval()
					else:
										assert isinstance(a					,						a  ), F"""teacher must be a model or string got type {type(a  )}"""
					__A	:						int       =				teacher.config.to_diff_dict()

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

					# Copy weights
					__A	:						Dict       =				teacher.config_class(**a  )
					__A	:						int       =				AutoModelForSeqaSeqLM.from_config(a  )
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					__A	:						Any       =				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
										__A    ,				__A	:						Optional[int]       =				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:
										__A	:						List[int]       =				pick_layers_to_copy(a					,						a  )
					if d_layers_to_copy is None:
										__A	:						List[int]       =				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}"""  )
					__A	:						Optional[int]       =				{
					    '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)




