





import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

				from transformers import SwiftFormerForImageClassification, SwiftFormerModel
				from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  Optional[Any] ,					__lowercase    :  Dict ,					__lowercase    :  int=13 ,					__lowercase    :  int=3 ,					__lowercase    :  Optional[int]=True ,					__lowercase    :  Optional[Any]=True ,					__lowercase    :  Union[str, Any]=0.1 ,					__lowercase    :  Dict=0.1 ,					__lowercase    :  Tuple=2_24 ,					__lowercase    :  Optional[int]=10_00 ,					__lowercase    :  Union[str, Any]=[3, 3, 6, 4] ,					__lowercase    :  Any=[48, 56, 1_12, 2_20] ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     parent
						UpperCAmelCase_								=     batch_size
						UpperCAmelCase_								=     num_channels
						UpperCAmelCase_								=     is_training
						UpperCAmelCase_								=     use_labels
						UpperCAmelCase_								=     hidden_dropout_prob
						UpperCAmelCase_								=     attention_probs_dropout_prob
						UpperCAmelCase_								=     num_labels
						UpperCAmelCase_								=     image_size
						UpperCAmelCase_								=     layer_depths
						UpperCAmelCase_								=     embed_dims





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						UpperCAmelCase_								=     None
						if self.use_labels:
							UpperCAmelCase_								=     ids_tensor([self.batch_size] ,					self.num_labels		)

						UpperCAmelCase_								=     self.get_config()

						return config, pixel_values, labels





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						return SwiftFormerConfig(
						    depths=self.layer_depths ,					embed_dims=self.embed_dims ,					mlp_ratio=4 ,					downsamples=[True, True, True, True] ,					hidden_act="""gelu""" ,					num_labels=self.num_labels ,					down_patch_size=3 ,					down_stride=2 ,					down_pad=1 ,					drop_rate=0.0 ,					drop_path_rate=0.0 ,					use_layer_scale=__lowercase ,					layer_scale_init_value=1e-5 ,					)





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






						'''simple docstring'''







						UpperCAmelCase_								=     SwiftFormerModel(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_								=     model(__lowercase		)
						self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.embed_dims[-1], 7, 7)		)





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






						'''simple docstring'''







						UpperCAmelCase_								=     self.num_labels
						UpperCAmelCase_								=     SwiftFormerForImageClassification(__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_								=     model(__lowercase ,					labels=__lowercase		)
						self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)		)

						UpperCAmelCase_								=     SwiftFormerForImageClassification(__lowercase		)
						model.to(__lowercase		)
						model.eval()

						UpperCAmelCase_								=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
						UpperCAmelCase_								=     model(__lowercase		)
						self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						((UpperCAmelCase_)						,	(UpperCAmelCase_)						,	(UpperCAmelCase_))								=     self.prepare_config_and_inputs()
						UpperCAmelCase_								=     {"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
class   _UpperCamelCase  (       A_						,       A_						,       unittest.TestCase  ):







					'''simple docstring'''

					lowerCamelCase     :     Tuple			    =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					lowerCamelCase     :     str			    =       (
					    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowerCamelCase     :     int			    =       False
					lowerCamelCase     :     Tuple			    =       False
					lowerCamelCase     :     Union[str, Any]			    =       False
					lowerCamelCase     :     Dict			    =       False
					lowerCamelCase     :     Union[str, Any]			    =       False





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     SwiftFormerModelTester(self		)
						UpperCAmelCase_								=     ConfigTester(
						    self ,					config_class=__lowercase ,					has_text_modality=__lowercase ,					hidden_size=37 ,					num_attention_heads=12 ,					num_hidden_layers=12 ,					)





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






						'''simple docstring'''







						self.config_tester.run_common_tests()





					@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						pass





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase_								=     model_class(__lowercase		)
							UpperCAmelCase_								=     model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__lowercase ,					nn.Linear		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase_								=     ["""pixel_values"""]
							self.assertListEqual(arg_names[:1] ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_								=     SwiftFormerModel.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)





					@unittest.skip(reason="""SwiftFormer does not output attentions"""		)
					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						pass





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						def check_hidden_states_output(__lowercase    :  Any ,					__lowercase    :  Tuple ,					__lowercase    :  Optional[int]		):
							UpperCAmelCase_								=     model_class(__lowercase		)
							model.to(__lowercase		)
							model.eval()

							with torch.no_grad():
								UpperCAmelCase_								=     model(**self._prepare_for_class(__lowercase ,					__lowercase		)		)

							UpperCAmelCase_								=     outputs.hidden_states

							UpperCAmelCase_								=     8
							self.assertEqual(len(__lowercase		) ,					__lowercase		)  # TODO

							# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
							# with the width and height being successively divided by 2, after every 2 blocks
							for i in range(len(__lowercase		)		):
								self.assertEqual(
								    hidden_states[i].shape ,					torch.Size(
								        [
								            self.model_tester.batch_size,
								            self.model_tester.embed_dims[i // 2],
								            (self.model_tester.image_size // 4) // 2 ** (i // 2),
								            (self.model_tester.image_size // 4) // 2 ** (i // 2),
								        ]		) ,					)

						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase_								=     True
							check_hidden_states_output(__lowercase ,					__lowercase ,					__lowercase		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase_								=     True

							check_hidden_states_output(__lowercase ,					__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						def _config_zero_init(__lowercase    :  Optional[int]		):
							UpperCAmelCase_								=     copy.deepcopy(__lowercase		)
							for key in configs_no_init.__dict__.keys():
								if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
									setattr(__lowercase ,					__lowercase ,					1e-10		)
								if isinstance(getattr(__lowercase ,					__lowercase ,					__lowercase		) ,					__lowercase		):
									UpperCAmelCase_								=     _config_zero_init(getattr(__lowercase ,					__lowercase		)		)
									setattr(__lowercase ,					__lowercase ,					__lowercase		)
							return configs_no_init

						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_								=     _config_zero_init(__lowercase		)
						for model_class in self.all_model_classes:
							UpperCAmelCase_								=     model_class(config=__lowercase		)
							for name, param in model.named_parameters():
								if param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1e9) / 1e9).round().item() ,					[0.0, 1.0] ,					msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" ,					)





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






						'''simple docstring'''







						pass




def    A_(			):
	UpperCAmelCase_								=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
	return image







@require_torch
@require_vision
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					@cached_property
					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""		) if is_vision_available() else None





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(__lowercase		)

						UpperCAmelCase_								=     self.default_image_processor
						UpperCAmelCase_								=     prepare_img()
						UpperCAmelCase_								=     image_processor(images=__lowercase ,					return_tensors="""pt"""		).to(__lowercase		)

						# forward pass
						with torch.no_grad():
							UpperCAmelCase_								=     model(**__lowercase		)

						# verify the logits
						UpperCAmelCase_								=     torch.Size((1, 10_00)		)
						self.assertEqual(outputs.logits.shape ,					__lowercase		)

						UpperCAmelCase_								=     torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]		).to(__lowercase		)
						self.assertTrue(torch.allclose(outputs.logits[0, :3] ,					__lowercase ,					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__   : 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)


