

import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import BridgeTowerImageProcessor





class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Dict[str, int] = None     , __lowerCamelCase					:			int = 32     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Union[int, float] = 1 / 255     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]     , __lowerCamelCase					:			Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Optional[int]=7     , __lowerCamelCase					:			str=30     , __lowerCamelCase					:			Any=400     , __lowerCamelCase					:			Tuple=3     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   do_resize
       SCREAMING_SNAKE_CASE										=   size if size is not None else {"shortest_edge": 288}
       SCREAMING_SNAKE_CASE										=   size_divisor
       SCREAMING_SNAKE_CASE										=   do_rescale
       SCREAMING_SNAKE_CASE										=   rescale_factor
       SCREAMING_SNAKE_CASE										=   do_normalize
       SCREAMING_SNAKE_CASE										=   do_center_crop
       SCREAMING_SNAKE_CASE										=   image_mean
       SCREAMING_SNAKE_CASE										=   image_std
       SCREAMING_SNAKE_CASE										=   do_pad
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   min_resolution
       SCREAMING_SNAKE_CASE										=   max_resolution





   def      _snake_case		(       self					:			Tuple			):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "size_divisor": self.size_divisor,
       }





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			List[str]=False			):
       if not batched:
           SCREAMING_SNAKE_CASE										=   self.size["shortest_edge"]
           SCREAMING_SNAKE_CASE										=   image_inputs[0]
           if isinstance(__lowerCamelCase     , Image.Image			):
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   image.size
           else:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   image.shape[1], image.shape[2]
           SCREAMING_SNAKE_CASE										=   size / min(__lowerCamelCase     , __lowerCamelCase			)
           if h < w:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   size, scale * w
           else:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   scale * h, size

           SCREAMING_SNAKE_CASE										=   int((1333 / 800) * size			)
           if max(__lowerCamelCase     , __lowerCamelCase			) > max_size:
               SCREAMING_SNAKE_CASE										=   max_size / max(__lowerCamelCase     , __lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   newh * scale
               SCREAMING_SNAKE_CASE										=   neww * scale

           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   int(newh + 0.5			), int(neww + 0.5			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   (
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

       else:
           SCREAMING_SNAKE_CASE										=   []
           for image in image_inputs:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.get_expected_values([image]			)
               expected_values.append((expected_height, expected_width)			)
           SCREAMING_SNAKE_CASE										=   max(__lowerCamelCase     , key=lambda __lowerCamelCase			: item[0]			)[0]
           SCREAMING_SNAKE_CASE										=   max(__lowerCamelCase     , key=lambda __lowerCamelCase			: item[1]			)[1]

       return expected_height, expected_width





@require_torch
@require_vision
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      BridgeTowerImageProcessor if is_vision_available() else None





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   BridgeTowerImageProcessingTester(self			)





   @property
   def      _snake_case		(       self					:			Union[str, Any]			):
       return self.image_processor_tester.prepare_image_processor_dict()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(__lowerCamelCase     , "image_mean"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "image_std"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "do_normalize"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "do_resize"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "size"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "size_divisor"			)			)





   def      _snake_case		(       self					:			Tuple			):
       pass





   def      _snake_case		(       self					:			int			):
       # Initialize image processor
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , Image.Image			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (1, self.image_processor_tester.num_channels, expected_height, expected_width)     , )

       # Test batched
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase     , batched=__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     , )





   def      _snake_case		(       self					:			Any			):
       # Initialize image processor
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase     , numpify=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , np.ndarray			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (1, self.image_processor_tester.num_channels, expected_height, expected_width)     , )

       # Test batched
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase     , batched=__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     , )





   def      _snake_case		(       self					:			Optional[int]			):
       # Initialize image processor
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase     , torchify=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , torch.Tensor			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (1, self.image_processor_tester.num_channels, expected_height, expected_width)     , )

       # Test batched
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase     , batched=__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     , )





'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					if "img_encoder.pos_embed" in name:
										snake_case_		     =							name.replace('''img_encoder.pos_embed''', '''vision_model.embeddings.position_embeddings'''      )
					if "img_encoder.patch_embed.proj" in name:
										snake_case_		     =							name.replace('''img_encoder.patch_embed.proj''', '''vision_model.embeddings.patch_embeddings.projection'''      )
					if "img_encoder.patch_embed.norm" in name:
										snake_case_		     =							name.replace('''img_encoder.patch_embed.norm''', '''vision_model.embeddings.layernorm'''      )
					if "img_encoder.layers" in name:
										snake_case_		     =							name.replace('''img_encoder.layers''', '''vision_model.encoder.stages'''      )
					if "blocks" in name and "res" not in name:
										snake_case_		     =							name.replace('''blocks''', '''layers'''      )
					if "attn" in name and "pre_assign" not in name:
										snake_case_		     =							name.replace('''attn''', '''self_attn'''      )
					if "proj" in name and "self_attn" in name and "text" not in name:
										snake_case_		     =							name.replace('''proj''', '''out_proj'''      )
					if "pre_assign_attn.attn.proj" in name:
										snake_case_		     =							name.replace('''pre_assign_attn.attn.proj''', '''pre_assign_attn.attn.out_proj'''      )
					if "norm1" in name:
										snake_case_		     =							name.replace('''norm1''', '''layer_norm1'''      )
					if "norm2" in name and "pre_assign" not in name:
										snake_case_		     =							name.replace('''norm2''', '''layer_norm2'''      )
					if "img_encoder.norm" in name:
										snake_case_		     =							name.replace('''img_encoder.norm''', '''vision_model.layernorm'''      )
					# text encoder
					if "text_encoder.token_embedding" in name:
										snake_case_		     =							name.replace('''text_encoder.token_embedding''', '''text_model.embeddings.token_embedding'''      )
					if "text_encoder.positional_embedding" in name:
										snake_case_		     =							name.replace('''text_encoder.positional_embedding''', '''text_model.embeddings.position_embedding.weight'''      )
					if "text_encoder.transformer.resblocks." in name:
										snake_case_		     =							name.replace('''text_encoder.transformer.resblocks.''', '''text_model.encoder.layers.'''      )
					if "ln_1" in name:
										snake_case_		     =							name.replace('''ln_1''', '''layer_norm1'''      )
					if "ln_2" in name:
										snake_case_		     =							name.replace('''ln_2''', '''layer_norm2'''      )
					if "c_fc" in name:
										snake_case_		     =							name.replace('''c_fc''', '''fc1'''      )
					if "c_proj" in name:
										snake_case_		     =							name.replace('''c_proj''', '''fc2'''      )
					if "text_encoder" in name:
										snake_case_		     =							name.replace('''text_encoder''', '''text_model'''      )
					if "ln_final" in name:
										snake_case_		     =							name.replace('''ln_final''', '''final_layer_norm'''      )
					# projection layers
					if "img_projector.linear_hidden." in name:
										snake_case_		     =							name.replace('''img_projector.linear_hidden.''', '''visual_projection.'''      )
					if "img_projector.linear_out." in name:
										snake_case_		     =							name.replace('''img_projector.linear_out.''', '''visual_projection.3.'''      )
					if "text_projector.linear_hidden" in name:
										snake_case_		     =							name.replace('''text_projector.linear_hidden''', '''text_projection'''      )
					if "text_projector.linear_out" in name:
										snake_case_		     =							name.replace('''text_projector.linear_out''', '''text_projection.3'''      )

					return name


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					for key in orig_state_dict.copy().keys():
										snake_case_		     =							orig_state_dict.pop(__UpperCAmelCase      )

										if "qkv" in key:
															# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															snake_case_		     =							key.split('''.'''      )
															snake_case_     ,snake_case_		     =							int(key_split[2]      ), int(key_split[4]      )
															snake_case_		     =							config.vision_config.hidden_size
															if "weight" in key:
																				snake_case_		     =							val[:dim, :]
																				snake_case_		     =							val[dim : dim * 2, :]
																				snake_case_		     =							val[-dim:, :]
															else:
																				snake_case_		     =							val[:dim]
																				snake_case_		     =							val[dim : dim * 2]
																				snake_case_		     =							val[-dim:]
										elif "in_proj" in key:
															# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															snake_case_		     =							key.split('''.'''      )
															snake_case_		     =							int(key_split[3]      )
															snake_case_		     =							config.text_config.hidden_size
															if "weight" in key:
																				snake_case_		     =							val[:dim, :]
																				snake_case_		     =							val[
																				    dim : dim * 2, :
																				]
																				snake_case_		     =							val[-dim:, :]
															else:
																				snake_case_		     =							val[:dim]
																				snake_case_		     =							val[dim : dim * 2]
																				snake_case_		     =							val[-dim:]
										else:
															snake_case_		     =							rename_key(__UpperCAmelCase      )
															# squeeze if necessary
															if (
															    "text_projection.0" in new_name
															    or "text_projection.3" in new_name
															    or "visual_projection.0" in new_name
															    or "visual_projection.3" in new_name
															):
																				snake_case_		     =							val.squeeze_()
															else:
																				snake_case_		     =							val

					return orig_state_dict


def 						__magic_name__       (      )						->   Union[str, Any]:

					'''simple docstring'''

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


@torch.no_grad()
def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase="groupvit-gcc-yfcc", __UpperCAmelCase=False      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							GroupViTConfig()
					snake_case_		     =							GroupViTModel(__UpperCAmelCase      ).eval()

					snake_case_		     =							torch.load(__UpperCAmelCase, map_location='''cpu'''      )['''model''']
					snake_case_		     =							convert_state_dict(__UpperCAmelCase, __UpperCAmelCase      )
					snake_case_     ,snake_case_		     =							model.load_state_dict(__UpperCAmelCase, strict=__UpperCAmelCase      )
					assert missing_keys == ["text_model.embeddings.position_ids"]
					assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCAmelCase      ) == 0)

					# verify result
					snake_case_		     =							CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''      )
					snake_case_		     =							prepare_img()
					snake_case_		     =							processor(text=['''a photo of a cat''', '''a photo of a dog'''], images=__UpperCAmelCase, padding=__UpperCAmelCase, return_tensors='''pt'''      )

					with torch.no_grad():
										snake_case_		     =							model(**__UpperCAmelCase      )

					if model_name == "groupvit-gcc-yfcc":
										snake_case_		     =							torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]      )
					elif model_name == "groupvit-gcc-redcaps":
										snake_case_		     =							torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]      )
					else:
										raise ValueError(F"Model name {model_name} not supported."      )
					assert torch.allclose(outputs.logits_per_image, __UpperCAmelCase, atol=1e-3      )

					processor.save_pretrained(__UpperCAmelCase      )
					model.save_pretrained(__UpperCAmelCase      )
					print('''Successfully saved processor and model to''', __UpperCAmelCase      )

					if push_to_hub:
										print('''Pushing to the hub...'''      )
										processor.push_to_hub(__UpperCAmelCase, organization='''nielsr'''      )
										model.push_to_hub(__UpperCAmelCase, organization='''nielsr'''      )


if __name__ == "__main__":
							a    :							Optional[int]										=    argparse.ArgumentParser()
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
							)
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
							parser.add_argument(
							    '--model_name',
							    default='groupvit-gccy-fcc',
							    type=str,
							    help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
							)
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							    help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
							)
							a    :							str										=    parser.parse_args()

							convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

