
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()
lowercase        =  logging.get_logger(__name__)



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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      List[str]       )  ->    Union[str, Any]:
							lowerCamelCase_							  =					dct.pop(UpperCAmelCase__       )
							lowerCamelCase_							  =					val



def      __lowerCAmelCase						(							)  ->    str:
							lowerCamelCase_							  =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_							  =					Image.open(requests.get(UpperCAmelCase__			,				stream=UpperCAmelCase__       ).raw       )
							return im



@torch.no_grad()
def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      List[Any]       )  ->    Optional[Any]:
							lowerCamelCase_							  =					DeiTConfig()
							# all deit models have fine-tuned heads
							lowerCamelCase_							  =					False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							lowerCamelCase_							  =					1_0_0_0
							lowerCamelCase_							  =					"""huggingface/label-files"""
							lowerCamelCase_							  =					"""imagenet-1k-id2label.json"""
							lowerCamelCase_							  =					json.load(open(hf_hub_download(UpperCAmelCase__			,				UpperCAmelCase__			,				repo_type="""dataset"""       )			,				"""r"""       )       )
							lowerCamelCase_							  =					{int(UpperCAmelCase__       ): v for k, v in idalabel.items()}
							lowerCamelCase_							  =					idalabel
							lowerCamelCase_							  =					{v: k for k, v in idalabel.items()}
							lowerCamelCase_							  =					int(deit_name[-6:-4]       )
							lowerCamelCase_							  =					int(deit_name[-3:]       )
							# size of the architecture
							if deit_name[9:].startswith("""tiny"""       ):
														lowerCamelCase_							  =					1_9_2
														lowerCamelCase_							  =					7_6_8
														lowerCamelCase_							  =					1_2
														lowerCamelCase_							  =					3
							elif deit_name[9:].startswith("""small"""       ):
														lowerCamelCase_							  =					3_8_4
														lowerCamelCase_							  =					1_5_3_6
														lowerCamelCase_							  =					1_2
														lowerCamelCase_							  =					6
							if deit_name[9:].startswith("""base"""       ):
														pass
							elif deit_name[4:].startswith("""large"""       ):
														lowerCamelCase_							  =					1_0_2_4
														lowerCamelCase_							  =					4_0_9_6
														lowerCamelCase_							  =					2_4
														lowerCamelCase_							  =					1_6

							# load original model from timm
							lowerCamelCase_							  =					timm.create_model(UpperCAmelCase__			,				pretrained=UpperCAmelCase__       )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							lowerCamelCase_							  =					timm_model.state_dict()
							lowerCamelCase_							  =					create_rename_keys(UpperCAmelCase__			,				UpperCAmelCase__       )
							for src, dest in rename_keys:
														rename_key(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
							read_in_q_k_v(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )

							# load HuggingFace model
							lowerCamelCase_							  =					DeiTForImageClassificationWithTeacher(UpperCAmelCase__       ).eval()
							model.load_state_dict(UpperCAmelCase__       )

							# Check outputs on an image, prepared by DeiTImageProcessor
							lowerCamelCase_							  =					int(
							    (2_5_6 / 2_2_4) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							lowerCamelCase_							  =					DeiTImageProcessor(size=UpperCAmelCase__			,				crop_size=config.image_size       )
							lowerCamelCase_							  =					image_processor(images=prepare_img()			,				return_tensors="""pt"""       )
							lowerCamelCase_							  =					encoding["""pixel_values"""]
							lowerCamelCase_							  =					model(UpperCAmelCase__       )

							lowerCamelCase_							  =					timm_model(UpperCAmelCase__       )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(UpperCAmelCase__			,				outputs.logits			,				atol=1e-3       )

							Path(UpperCAmelCase__       ).mkdir(exist_ok=UpperCAmelCase__       )
							print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''       )
							model.save_pretrained(UpperCAmelCase__       )
							print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
							image_processor.save_pretrained(UpperCAmelCase__       )


if __name__ == "__main__":
		lowercase        =  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.'''
		)

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

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class       __A:

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]=3   ,					__UpperCamelCase					:     int=3_2   ,					__UpperCamelCase					:     Any=3   ,					__UpperCamelCase					:     List[str]=1_0   ,					__UpperCamelCase					:     int=[1_0, 2_0, 3_0, 4_0]   ,					__UpperCamelCase					:     List[str]=[1, 1, 2, 1]   ,					__UpperCamelCase					:     str=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Dict="relu"   ,					__UpperCamelCase					:     int=3   ,					__UpperCamelCase					:     Dict=None   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					image_size
												lowerCamelCase_							  =					num_channels
												lowerCamelCase_							  =					embeddings_size
												lowerCamelCase_							  =					hidden_sizes
												lowerCamelCase_							  =					depths
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					num_labels
												lowerCamelCase_							  =					scope
												lowerCamelCase_							  =					len(__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

												lowerCamelCase_							  =					self.get_config()
												return config, pixel_values, labels

					def    lowercase__      (	self					:     str   ):
												return RegNetConfig(
												    num_channels=self.num_channels   ,					embeddings_size=self.embeddings_size   ,					hidden_sizes=self.hidden_sizes   ,					depths=self.depths   ,					hidden_act=self.hidden_act   ,					num_labels=self.num_labels   ,					)

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					TFRegNetModel(config=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					training=__UpperCamelCase   )
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,					)

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					self.num_labels
												lowerCamelCase_							  =					TFRegNetForImageClassification(__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					labels=__UpperCamelCase   ,					training=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )



					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_tf
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE			    =    (
					    {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					TFRegNetModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					has_text_modality=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												return

					@unittest.skip(reason="""RegNet does not use inputs_embeds"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0   ,					reason="""TF does not support backprop for grouped convolutions on CPU."""   ,					)
					@slow
					def    lowercase__      (	self					:     Tuple   ):
												super().test_keras_fit()

					@unittest.skip(reason="""RegNet does not support input and output embeddings"""   )
					def    lowercase__      (	self					:     Optional[Any]   ):
												pass

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

																			lowerCamelCase_							  =					["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ):
												def check_hidden_states_output(__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Any   ):
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )
																			lowerCamelCase_							  =					model(**self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )   ,					training=__UpperCamelCase   )

																			lowerCamelCase_							  =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			lowerCamelCase_							  =					self.model_tester.num_stages
																			self.assertEqual(len(__UpperCamelCase   )   ,					expected_num_stages + 1   )

																			# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]   )   ,					[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,					)

												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase_							  =					["""basic""", """bottleneck"""]
												for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																										lowerCamelCase_							  =					layer_type
																										lowerCamelCase_							  =					True
																										check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

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

																										check_hidden_states_output(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[Any]={}   ):
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   )
																			lowerCamelCase_							  =					model(__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					**__UpperCamelCase   ).to_tuple()

																			def recursive_check(__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
																										if isinstance(__UpperCamelCase   ,					(List, Tuple)   ):
																																	for tuple_iterable_value, dict_iterable_value in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																																								recursive_check(__UpperCamelCase   ,					__UpperCamelCase   )
																										elif tuple_object is None:
																																	return
																										else:
																																	self.assertTrue(
																																	    all(tf.equal(__UpperCamelCase   ,					__UpperCamelCase   )   )   ,					msg=(
																																	        """Tuple and dict output are not equal. Difference:"""
																																	        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}'''
																																	    )   ,					)

																										recursive_check(__UpperCamelCase   ,					__UpperCamelCase   )

												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )
																			check_equivalence(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					{"""output_hidden_states""": True}   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase   )



					@slow
					def    lowercase__      (	self					:     List[Any]   ):
												for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					TFRegNetModel.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )



def      __lowerCAmelCase						(							)  ->    Optional[int]:
							lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image





@require_tf
@require_vision
class       __A(				unittest.TestCase       ):

					@cached_property
					def    lowercase__      (	self					:     Union[str, Any]   ):
												return (
												    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
												    if is_vision_available()
												    else None
												)



					@slow
					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

												lowerCamelCase_							  =					self.default_image_processor
												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""tf"""   )

												# forward pass
												lowerCamelCase_							  =					model(**__UpperCamelCase   ,					training=__UpperCamelCase   )

												# verify the logits
												lowerCamelCase_							  =					tf.TensorShape((1, 1_0_0_0)   )
												self.assertEqual(outputs.logits.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					tf.constant([-0.4180, -1.5051, -3.4836]   )

												tf.debugging.assert_near(outputs.logits[0, :3]   ,					__UpperCamelCase   ,					atol=1E-4   )





