import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:
			class   _lowerCAmelCase		:







										@staticmethod
										def 				snake_case  (				*__snake_case     :    str	,					**__snake_case     :    str		):
													pass
@is_pipeline_test
@require_vision
class   _lowerCAmelCase		(     unittest.TestCase				):







							@require_torch
							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Optional[int]						  =     pipeline(
										    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''	,					)
										lowerCamelCase						:List[str]						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Dict						  =     image_classifier(__snake_case	,					candidate_labels=['''a''', '''b''', '''c''']		)

										# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
										# python and torch versions.
										self.assertIn(
										    nested_simplify(__snake_case		)	,					[
										        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}],
										        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}],
										    ]	,					)

										lowerCamelCase						:Tuple						  =     image_classifier([image] * 5	,					candidate_labels=['''A''', '''B''', '''C''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										    ]	,					)







							@require_tf
							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Tuple						  =     pipeline(
										    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''	,					framework='''tf'''		)
										lowerCamelCase						:List[str]						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Optional[Any]						  =     image_classifier(__snake_case	,					candidate_labels=['''a''', '''b''', '''c''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}]	,					)

										lowerCamelCase						:int						  =     image_classifier([image] * 5	,					candidate_labels=['''A''', '''B''', '''C''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										    ]	,					)







							@slow
							@require_torch
							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:str						  =     pipeline(
										    task='''zero-shot-image-classification'''	,					model='''openai/clip-vit-base-patch32'''	,					)
										# This is an image of 2 cats with remotes and no planes
										lowerCamelCase						:List[str]						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Optional[Any]						  =     image_classifier(__snake_case	,					candidate_labels=['''cat''', '''plane''', '''remote''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        {'''score''': 0.5_1_1, '''label''': '''remote'''},
										        {'''score''': 0.4_8_5, '''label''': '''cat'''},
										        {'''score''': 0.0_0_4, '''label''': '''plane'''},
										    ]	,					)

										lowerCamelCase						:Any						  =     image_classifier([image] * 5	,					candidate_labels=['''cat''', '''plane''', '''remote''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.5_1_1, '''label''': '''remote'''},
										            {'''score''': 0.4_8_5, '''label''': '''cat'''},
										            {'''score''': 0.0_0_4, '''label''': '''plane'''},
										        ],
										    ]
										    * 5	,					)







							@slow
							@require_tf
							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Union[str, Any]						  =     pipeline(
										    task='''zero-shot-image-classification'''	,					model='''openai/clip-vit-base-patch32'''	,					framework='''tf'''		)
										# This is an image of 2 cats with remotes and no planes
										lowerCamelCase						:Dict						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Dict						  =     image_classifier(__snake_case	,					candidate_labels=['''cat''', '''plane''', '''remote''']		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        {'''score''': 0.5_1_1, '''label''': '''remote'''},
										        {'''score''': 0.4_8_5, '''label''': '''cat'''},
										        {'''score''': 0.0_0_4, '''label''': '''plane'''},
										    ]	,					)

										lowerCamelCase						:Any						  =     image_classifier([image] * 5	,					candidate_labels=['''cat''', '''plane''', '''remote''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.5_1_1, '''label''': '''remote'''},
										            {'''score''': 0.4_8_5, '''label''': '''cat'''},
										            {'''score''': 0.0_0_4, '''label''': '''plane'''},
										        ],
										    ]
										    * 5	,					)




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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def  _lowerCamelCase					(   a_				: str						,			a_				: str=False):
			lowerCamelCase						:Optional[int]						  =     []

			# fmt: off
			# stem:
			rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token'''))
			rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings'''))

			rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''))
			rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''))

			# backbone
			rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight'''))
			rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight'''))
			rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias'''))

			for stage_idx in range(len(config.backbone_config.depths)):
						for layer_idx in range(config.backbone_config.depths[stage_idx]):
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias"))

						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight"))
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight"))
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias"))

			# transformer encoder
			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"vit.encoder.layer.{i}.layernorm_before.weight"))
						rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias"))
						rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight"))
						rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias"))
						rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight"))
						rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias"))
						rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight"))
						rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias"))
						rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight"))
						rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias"))

			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 "vit" from all keys that start with "vit"
						lowerCamelCase						:List[Any]						  =     [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''') else pair for pair in rename_keys]
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ('''norm.weight''', '''vit.layernorm.weight'''),
						        ('''norm.bias''', '''vit.layernorm.bias'''),
						        ('''head.weight''', '''classifier.weight'''),
						        ('''head.bias''', '''classifier.bias'''),
						    ])
			# fmt: on

			return rename_keys





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





def  _lowerCamelCase					(   a_				: int):
			lowerCamelCase						:Any						  =     ['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(a_						,			a_)





def  _lowerCamelCase					(   a_				: int						,			a_				: Any						,			a_				: Tuple):
			lowerCamelCase						:Optional[Any]						  =     dct.pop(a_)
			lowerCamelCase						:str						  =     val





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Tuple						  =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase						:Tuple						  =     Image.open(requests.get(a_						,			stream=a_).raw)
			return im





@torch.no_grad()
def  _lowerCamelCase					(   a_				: Optional[Any]						,			a_				: Optional[Any]						,			a_				: Optional[Any]=False):
			lowerCamelCase						:Optional[int]						  =     BitConfig(
			    global_padding='''same'''						,			layer_type='''bottleneck'''						,			depths=(3, 4, 9)						,			out_features=['''stage3''']						,			embedding_dynamic_padding=a_						,			)
			lowerCamelCase						:Optional[int]						  =     ViTHybridConfig(backbone_config=a_						,			image_size=3_84						,			num_labels=10_00)
			lowerCamelCase						:List[Any]						  =     False

			# load original model from timm
			lowerCamelCase						:List[str]						  =     timm.create_model(a_						,			pretrained=a_)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowerCamelCase						:List[str]						  =     timm_model.state_dict()
			if base_model:
						remove_classification_head_(a_)
			lowerCamelCase						:Tuple						  =     create_rename_keys(a_						,			a_)
			for src, dest in rename_keys:
						rename_key(a_						,			a_						,			a_)
			read_in_q_k_v(a_						,			a_						,			a_)

			lowerCamelCase						:List[str]						  =     '''huggingface/label-files'''
			lowerCamelCase						:Any						  =     '''imagenet-1k-id2label.json'''
			lowerCamelCase						:List[Any]						  =     json.load(open(hf_hub_download(a_						,			a_						,			repo_type='''dataset''')						,			'''r'''))
			lowerCamelCase						:Optional[Any]						  =     {int(a_): v for k, v in idalabel.items()}
			lowerCamelCase						:Optional[int]						  =     idalabel
			lowerCamelCase						:Union[str, Any]						  =     {v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						lowerCamelCase						:Optional[Any]						  =     ViTHybridModel(a_).eval()
			else:
						lowerCamelCase						:Dict						  =     ViTHybridForImageClassification(a_).eval()
			model.load_state_dict(a_)

			# create image processor
			lowerCamelCase						:Dict						  =     create_transform(**resolve_data_config({}						,			model=a_))
			lowerCamelCase						:str						  =     transform.transforms

			lowerCamelCase						:int						  =     {
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			lowerCamelCase						:Any						  =     ViTHybridImageProcessor(
			    do_resize=a_						,			size={'''shortest_edge''': timm_transforms[0].size}						,			resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,			do_center_crop=a_						,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}						,			do_normalize=a_						,			image_mean=timm_transforms[-1].mean.tolist()						,			image_std=timm_transforms[-1].std.tolist()						,			)

			lowerCamelCase						:Dict						  =     prepare_img()
			lowerCamelCase						:str						  =     transform(a_).unsqueeze(0)
			lowerCamelCase						:str						  =     processor(a_						,			return_tensors='''pt''').pixel_values

			# verify pixel values
			assert torch.allclose(a_						,			a_)

			# verify logits
			with torch.no_grad():
						lowerCamelCase						:Optional[int]						  =     model(a_)
						lowerCamelCase						:Union[str, Any]						  =     outputs.logits

			print('''Predicted class:'''						,			logits.argmax(-1).item())
			if base_model:
						lowerCamelCase						:Union[str, Any]						  =     timm_model.forward_features(a_)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(a_						,			outputs.pooler_output						,			atol=1e-3)
			else:
						lowerCamelCase						:List[str]						  =     timm_model(a_)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(a_						,			outputs.logits						,			atol=1e-3)
			print('''Looks ok!''')

			if pytorch_dump_folder_path is not None:
						Path(a_).mkdir(exist_ok=a_)
						print(F"Saving model {vit_name} to {pytorch_dump_folder_path}")
						model.save_pretrained(a_)
						print(F"Saving processor to {pytorch_dump_folder_path}")
						processor.save_pretrained(a_)

			if push_to_hub:
						print(F"Pushing model and processor to the hub {vit_name}")
						model.push_to_hub(F"ybelkada/{vit_name}")
						processor.push_to_hub(F"ybelkada/{vit_name}")


if __name__ == "__main__":
			A__           =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--vit_name""",
			    default="""vit_base_r50_s16_384""",
			    type=str,
			    help="""Name of the hybrid ViT 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."""
			)
			parser.add_argument(
			    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
			)

			A__           =							parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



