





'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     tempfile.mkdtemp()

						UpperCAmelCase_								=     BlipImageProcessor()
						UpperCAmelCase_								=     BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""		)

						UpperCAmelCase_								=     BlipProcessor(_lowercase ,					_lowercase		)

						processor.save_pretrained(self.tmpdirname		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					**__lowercase    :  Optional[int]		):






						'''simple docstring'''







						return AutoProcessor.from_pretrained(self.tmpdirname ,					**_lowercase		).tokenizer





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					**__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







						return AutoProcessor.from_pretrained(self.tmpdirname ,					**_lowercase		).image_processor





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						shutil.rmtree(self.tmpdirname		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     [np.random.randint(2_55 ,					size=(3, 30, 4_00) ,					dtype=np.uinta		)]

						UpperCAmelCase_								=     [Image.fromarray(np.moveaxis(_lowercase ,					0 ,					-1		)		) for x in image_inputs]

						return image_inputs





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     BlipProcessor(tokenizer=self.get_tokenizer() ,					image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						UpperCAmelCase_								=     self.get_tokenizer(bos_token="""(BOS)""" ,					eos_token="""(EOS)"""		)
						UpperCAmelCase_								=     self.get_image_processor(do_normalize=_lowercase ,					padding_value=1.0		)

						UpperCAmelCase_								=     BlipProcessor.from_pretrained(
						    self.tmpdirname ,					bos_token="""(BOS)""" ,					eos_token="""(EOS)""" ,					do_normalize=_lowercase ,					padding_value=1.0		)

						self.assertEqual(processor.tokenizer.get_vocab() ,					tokenizer_add_kwargs.get_vocab()		)
						self.assertIsInstance(processor.tokenizer ,					_lowercase		)

						self.assertEqual(processor.image_processor.to_json_string() ,					image_processor_add_kwargs.to_json_string()		)
						self.assertIsInstance(processor.image_processor ,					_lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     BlipProcessor(tokenizer=_lowercase ,					image_processor=_lowercase		)

						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     image_processor(_lowercase ,					return_tensors="""np"""		)
						UpperCAmelCase_								=     processor(images=_lowercase ,					return_tensors="""np"""		)

						for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum() ,					input_processor[key].sum() ,					delta=1e-2		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     BlipProcessor(tokenizer=_lowercase ,					image_processor=_lowercase		)

						UpperCAmelCase_								=     """lower newer"""

						UpperCAmelCase_								=     processor(text=_lowercase		)

						UpperCAmelCase_								=     tokenizer(_lowercase ,					return_token_type_ids=_lowercase		)

						for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key] ,					encoded_processor[key]		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     BlipProcessor(tokenizer=_lowercase ,					image_processor=_lowercase		)

						UpperCAmelCase_								=     """lower newer"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=_lowercase ,					images=_lowercase		)

						self.assertListEqual(list(inputs.keys()		) ,					["""pixel_values""", """input_ids""", """attention_mask"""]		)

						# test if it raises when no input is passed
						with pytest.raises(_lowercase		):
							processor()





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     BlipProcessor(tokenizer=_lowercase ,					image_processor=_lowercase		)

						UpperCAmelCase_								=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCAmelCase_								=     processor.batch_decode(_lowercase		)
						UpperCAmelCase_								=     tokenizer.batch_decode(_lowercase		)

						self.assertListEqual(_lowercase ,					_lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     BlipProcessor(tokenizer=_lowercase ,					image_processor=_lowercase		)

						UpperCAmelCase_								=     """lower newer"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=_lowercase ,					images=_lowercase		)

						# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
						self.assertListEqual(list(inputs.keys()		) ,					["""pixel_values""", """input_ids""", """attention_mask"""]		)









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__   : Tuple					  = logging.get_logger(__name__)




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     nn.ModuleList([src_layers[i] for i in layers_to_copy]   )
	assert len(A   ) == len(A   ), f"""{len(A   )} != {len(A   )}"""
	dest_layers.load_state_dict(layers_to_copy.state_dict()   )


UpperCamelCase__   : str					  = {
    # 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__   : 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_(			A				,      A   ):
	try:
		UpperCAmelCase_								=     LAYERS_TO_COPY[n_teacher][n_student]
		return val
	except KeyError:
		if n_student != n_teacher:
			warnings.warn(
			    f"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
			    f""" {n_student}"""   )
		return list(range(A   )   )




def    A_(			A				,      A   ):
	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    A_(			A				,      A = "student"				,      A = None				,      A = None				,      A=False				,      A=None				,      A=None				,      **A				,      ):
	UpperCAmelCase_								=     """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
	assert (e is not None) or (d is not None), _msg
	if isinstance(A				,      A   ):
		AutoTokenizer.from_pretrained(A   ).save_pretrained(A   )  # purely for convenience
		UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_pretrained(A   ).eval()
	else:
		assert isinstance(A				,      A   ), f"""teacher must be a model or string got type {type(A   )}"""
	UpperCAmelCase_								=     teacher.config.to_diff_dict()

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

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

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

	if copy_first_teacher_layers:  # Our copying is done. We just log and save
		UpperCAmelCase_						,	UpperCAmelCase_								=     list(range(A   )   ), list(range(A   )   )
		logger.info(
		    f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
		    f""" {save_path}"""   )
		student.save_pretrained(A   )
		return student, e_layers_to_copy, d_layers_to_copy

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

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

	return student, e_layers_to_copy, d_layers_to_copy


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


