





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a=False   ):
	snake_case_		:			List[Any]											=	[]
	for i in range(config.num_hidden_layers   ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")   )
		rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")   )
		rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")   )
		rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")   )
		rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")   )
		rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")   )
		rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")   )
		rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")   )
		rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")   )
		rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")   )

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        ('cls_token', 'deit.embeddings.cls_token'),
	        ('dist_token', 'deit.embeddings.distillation_token'),
	        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
	        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
	        ('pos_embed', 'deit.embeddings.position_embeddings'),
	    ]   )

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		        ('pre_logits.fc.weight', 'pooler.dense.weight'),
		        ('pre_logits.fc.bias', 'pooler.dense.bias'),
		    ]   )

		# if just the base model, we should remove "deit" from all keys that start with "deit"
		snake_case_		:			List[str]											=	[(pair[0], pair[1][4:]) if pair[1].startswith('deit'   ) else pair for pair in rename_keys]
	else:
		# layernorm + classification heads
		rename_keys.extend(
		    [
		        ('norm.weight', 'deit.layernorm.weight'),
		        ('norm.bias', 'deit.layernorm.bias'),
		        ('head.weight', 'cls_classifier.weight'),
		        ('head.bias', 'cls_classifier.bias'),
		        ('head_dist.weight', 'distillation_classifier.weight'),
		        ('head_dist.bias', 'distillation_classifier.bias'),
		    ]   )

	return rename_keys


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=False   ):
	for i in range(config.num_hidden_layers   ):
		if base_model:
			snake_case_		:			str											=	''
		else:
			snake_case_		:			Any											=	'deit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		snake_case_		:			Any											=	state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""   )
		snake_case_		:			List[str]											=	state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""   )
		# next, add query, keys and values (in that order) to the state dict
		snake_case_		:			Optional[Any]											=	in_proj_weight[
		    : config.hidden_size, :
		]
		snake_case_		:			List[Any]											=	in_proj_bias[: config.hidden_size]
		snake_case_		:			str											=	in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		snake_case_		:			List[Any]											=	in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		snake_case_		:			Union[str, Any]											=	in_proj_weight[
		    -config.hidden_size :, :
		]
		snake_case_		:			Union[str, Any]											=	in_proj_bias[-config.hidden_size :]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			Any											=	dct.pop(__a   )
	snake_case_		:			Optional[int]											=	val


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Tuple											=	'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case_		:			Dict											=	Image.open(requests.get(__a   , stream=__a   ).raw   )
	return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			List[str]											=	DeiTConfig()
	# all deit models have fine-tuned heads
	snake_case_		:			List[str]											=	False
	# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
	snake_case_		:			List[str]											=	10_00
	snake_case_		:			Dict											=	'huggingface/label-files'
	snake_case_		:			Tuple											=	'imagenet-1k-id2label.json'
	snake_case_		:			Union[str, Any]											=	json.load(open(hf_hub_download(__a   , __a   , repo_type='dataset'   )   , 'r'   )   )
	snake_case_		:			Optional[int]											=	{int(__a   ): v for k, v in idalabel.items()}
	snake_case_		:			Union[str, Any]											=	idalabel
	snake_case_		:			Optional[Any]											=	{v: k for k, v in idalabel.items()}
	snake_case_		:			Tuple											=	int(deit_name[-6:-4]   )
	snake_case_		:			List[Any]											=	int(deit_name[-3:]   )
	# size of the architecture
	if deit_name[9:].startswith('tiny'   ):
		snake_case_		:			Tuple											=	1_92
		snake_case_		:			List[Any]											=	7_68
		snake_case_		:			List[Any]											=	12
		snake_case_		:			int											=	3
	elif deit_name[9:].startswith('small'   ):
		snake_case_		:			Dict											=	3_84
		snake_case_		:			List[Any]											=	15_36
		snake_case_		:			List[str]											=	12
		snake_case_		:			str											=	6
	if deit_name[9:].startswith('base'   ):
		pass
	elif deit_name[4:].startswith('large'   ):
		snake_case_		:			int											=	10_24
		snake_case_		:			Tuple											=	40_96
		snake_case_		:			int											=	24
		snake_case_		:			Optional[Any]											=	16

	# load original model from timm
	snake_case_		:			Any											=	timm.create_model(__a   , pretrained=__a   )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	snake_case_		:			str											=	timm_model.state_dict()
	snake_case_		:			List[str]											=	create_rename_keys(__a   , __a   )
	for src, dest in rename_keys:
		rename_key(__a   , __a   , __a   )
	read_in_q_k_v(__a   , __a   , __a   )

	# load HuggingFace model
	snake_case_		:			Union[str, Any]											=	DeiTForImageClassificationWithTeacher(__a   ).eval()
	model.load_state_dict(__a   )

	# Check outputs on an image, prepared by DeiTImageProcessor
	snake_case_		:			List[str]											=	int(
	    (2_56 / 2_24) * config.image_size   )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
	snake_case_		:			Any											=	DeiTImageProcessor(size=__a   , crop_size=config.image_size   )
	snake_case_		:			List[Any]											=	image_processor(images=prepare_img()   , return_tensors='pt'   )
	snake_case_		:			Union[str, Any]											=	encoding['pixel_values']
	snake_case_		:			str											=	model(__a   )

	snake_case_		:			int											=	timm_model(__a   )
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(__a   , outputs.logits   , atol=1E-3   )

	Path(__a   ).mkdir(exist_ok=__a   )
	print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(__a   )
	print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
	image_processor.save_pretrained(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--deit_name""",
						    default="""vit_deit_base_distilled_patch16_224""",
						    type=str,
						    help="""Name of the DeiT timm model you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
						)

						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


_SCREAMING_SNAKE_CASE      	=		Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

_SCREAMING_SNAKE_CASE      	=		{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

_SCREAMING_SNAKE_CASE      	=		"""zero2"""
_SCREAMING_SNAKE_CASE      	=		"""zero3"""
_SCREAMING_SNAKE_CASE      	=		[ZEROa, ZEROa]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	# customize the test name generator function as we want both params to appear in the sub-test
	# name, as by default it shows only the first param
	snake_case_		:			Tuple											=	parameterized.to_safe_name('_'.join(str(__a   ) for x in param.args   )   )
	return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
_SCREAMING_SNAKE_CASE      	=		list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Any		,   _A      :      int	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[str]		,   _A      :      str	)							->    Tuple:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      int		,   _A      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Union[str, Any]	)							->    Any:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      bool = True		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	models[model]

							snake_case_		:			str											=	self.run_trainer(
							    stage=_A		,   model_name=_A		,   eval_steps=_A		,   num_train_epochs=1		,   distributed=_A		,   fpaa=_A		,   )

							self.do_checks(_A	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      int = 1		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			str											=	self.get_auto_remove_tmp_dir('./xxx'		,   after=_A	)
							snake_case_		:			Tuple											=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_A	)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

							if fpaa:
								args.extend(['--fp16']	)

							# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
							# hence the separate config files
							snake_case_		:			Any											=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
							snake_case_		:			str											=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
							snake_case_		:			int											=	self.get_launcher(_A	)

							snake_case_		:			List[str]											=	launcher + script + args + ds_args
							# keep for quick debug
							# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
							execute_subprocess_async(_A		,   env=self.get_env()	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Optional[Any]=False	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	min(2		,   get_gpu_count()	) if distributed else 1
							return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

