



'''simple docstring'''






import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A_				:					str							=      logging.get_logger(__name__)




def        snake_case_   (	lowerCAmelCase_						)->						Tuple:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					tf.image.resize(lowerCAmelCase_			,  (346, 346)						)
       _UpperCAmelCase    :		str     	=					tf.image.crop_to_bounding_box(lowerCAmelCase_			,  (346 - 289) // 2			,  (346 - 289) // 2			,  289			,  289						)
       return image




def        snake_case_   (	)->						Tuple:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[int]     	=					EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""						)
       _UpperCAmelCase    :		int     	=					289
       _UpperCAmelCase    :		Any     	=					640
       _UpperCAmelCase    :		Any     	=					{"""0""": """LABEL_0""", """1""": """LABEL_1"""}
       _UpperCAmelCase    :		List[str]     	=					{"""LABEL_0""": 0, """LABEL_1""": 1}
       _UpperCAmelCase    :		Optional[Any]     	=					[]

       _UpperCAmelCase    :		int     	=					BertConfig()
       _UpperCAmelCase    :		Union[str, Any]     	=					AlignConfig.from_text_vision_configs(
           text_config=lowerCAmelCase_			,  vision_config=lowerCAmelCase_			,  projection_dim=640						)
       return config




def        snake_case_   (	)->						int:





       '''simple docstring'''


       _UpperCAmelCase    :		Dict     	=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase    :		int     	=					Image.open(requests.get(lowerCAmelCase_			,  stream=lowerCAmelCase_						).raw						)
       return im




def        snake_case_   (	)->						Dict:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[Any]     	=					EfficientNetImageProcessor(
           do_center_crop=lowerCAmelCase_			,  rescale_factor=1 / 1_2_7.5			,  rescale_offset=lowerCAmelCase_			,  do_normalize=lowerCAmelCase_			,  include_top=lowerCAmelCase_			,  resample=Image.BILINEAR			,  )
       _UpperCAmelCase    :		Union[str, Any]     	=					BertTokenizer.from_pretrained("""bert-base-uncased"""						)
       _UpperCAmelCase    :		Union[str, Any]     	=					64
       _UpperCAmelCase    :		int     	=					AlignProcessor(image_processor=lowerCAmelCase_			,  tokenizer=lowerCAmelCase_						)
       return processor




def        snake_case_   (	lowerCAmelCase_						)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		Union[str, Any]     	=					[v.split("""_"""						)[0].split("""block"""						)[1] for v in original_param_names if v.startswith("""block"""						)]
       _UpperCAmelCase    :		Optional[Any]     	=					list(set(lowerCAmelCase_						)						)
       _UpperCAmelCase    :		List[str]     	=					sorted(lowerCAmelCase_						)
       _UpperCAmelCase    :		Optional[Any]     	=					len(lowerCAmelCase_						)
       _UpperCAmelCase    :		Tuple     	=					{b: str(lowerCAmelCase_						) for b, i in zip(lowerCAmelCase_			,  range(lowerCAmelCase_						)						)}

       _UpperCAmelCase    :		List[Any]     	=					[]
       rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""")						)
       rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""")						)
       rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""")						)
       rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""")						)
       rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""")						)

       for b in block_names:
              _UpperCAmelCase    :		List[Any]     	=					block_name_mapping[b]
              rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')						)
              rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')						)
              rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')						)
              rename_keys.append(
                  (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')						)
              rename_keys.append(
                  (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')						)
              rename_keys.append(
                  (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')						)
              rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')						)
              rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')						)
              rename_keys.append(
                  (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')						)
              rename_keys.append(
                  (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')						)

              rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')						)
              rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')						)
              rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')						)
              rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')						)
              rename_keys.append(
                  (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')						)
              rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')						)
              rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')						)
              rename_keys.append(
                  (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')						)
              rename_keys.append(
                  (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')						)

       _UpperCAmelCase    :		List[str]     	=					{}
       for item in rename_keys:
              if item[0] in original_param_names:
                     _UpperCAmelCase    :		Any     	=					"""vision_model.""" + item[1]

    # BERT text encoder
       _UpperCAmelCase    :		Union[str, Any]     	=					[]
       _UpperCAmelCase    :		Tuple     	=					"""tf_bert_model/bert"""
       _UpperCAmelCase    :		List[Any]     	=					"""text_model"""
       for i in range(12						):
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/self/query/kernel:0''',
                      F'''{new}.encoder.layer.{i}.attention.self.query.weight''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/self/query/bias:0''',
                      F'''{new}.encoder.layer.{i}.attention.self.query.bias''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/self/key/kernel:0''',
                      F'''{new}.encoder.layer.{i}.attention.self.key.weight''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/self/key/bias:0''',
                      F'''{new}.encoder.layer.{i}.attention.self.key.bias''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/self/value/kernel:0''',
                      F'''{new}.encoder.layer.{i}.attention.self.value.weight''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/self/value/bias:0''',
                      F'''{new}.encoder.layer.{i}.attention.self.value.bias''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/output/dense/kernel:0''',
                      F'''{new}.encoder.layer.{i}.attention.output.dense.weight''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/output/dense/bias:0''',
                      F'''{new}.encoder.layer.{i}.attention.output.dense.bias''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0''',
                      F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.weight''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0''',
                      F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.bias''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/intermediate/dense/kernel:0''',
                      F'''{new}.encoder.layer.{i}.intermediate.dense.weight''',
                  )						)
              rename_keys.append(
                  (
                      F'''{old}/encoder/layer_._{i}/intermediate/dense/bias:0''',
                      F'''{new}.encoder.layer.{i}.intermediate.dense.bias''',
                  )						)
              rename_keys.append(
                  (F'''{old}/encoder/layer_._{i}/output/dense/kernel:0''', F'''{new}.encoder.layer.{i}.output.dense.weight''')						)
              rename_keys.append(
                  (F'''{old}/encoder/layer_._{i}/output/dense/bias:0''', F'''{new}.encoder.layer.{i}.output.dense.bias''')						)
              rename_keys.append(
                  (F'''{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.weight''')						)
              rename_keys.append(
                  (F'''{old}/encoder/layer_._{i}/output/LayerNorm/beta:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.bias''')						)

       rename_keys.append((F'''{old}/embeddings/word_embeddings/weight:0''', F'''{new}.embeddings.word_embeddings.weight''')						)
       rename_keys.append(
           (F'''{old}/embeddings/position_embeddings/embeddings:0''', F'''{new}.embeddings.position_embeddings.weight''')						)
       rename_keys.append(
           (F'''{old}/embeddings/token_type_embeddings/embeddings:0''', F'''{new}.embeddings.token_type_embeddings.weight''')						)
       rename_keys.append((F'''{old}/embeddings/LayerNorm/gamma:0''', F'''{new}.embeddings.LayerNorm.weight''')						)
       rename_keys.append((F'''{old}/embeddings/LayerNorm/beta:0''', F'''{new}.embeddings.LayerNorm.bias''')						)

       rename_keys.append((F'''{old}/pooler/dense/kernel:0''', F'''{new}.pooler.dense.weight''')						)
       rename_keys.append((F'''{old}/pooler/dense/bias:0''', F'''{new}.pooler.dense.bias''')						)
       rename_keys.append(("""dense/kernel:0""", """text_projection.weight""")						)
       rename_keys.append(("""dense/bias:0""", """text_projection.bias""")						)
       rename_keys.append(("""dense/bias:0""", """text_projection.bias""")						)
       rename_keys.append(("""temperature:0""", """temperature""")						)

       for item in rename_keys:
              if item[0] in original_param_names:
                     _UpperCAmelCase    :		Optional[Any]     	=					item[1]
       return key_mapping




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       list(hf_params.keys()						)

       for key, value in tf_params.items():
              if key not in key_mapping:
                     continue

              _UpperCAmelCase    :		Optional[int]     	=					key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     _UpperCAmelCase    :		Optional[int]     	=					torch.from_numpy(lowerCAmelCase_						).permute(3			,  2			,  0			,  1						)
              elif "embeddings" in key:
                     _UpperCAmelCase    :		Dict     	=					torch.from_numpy(lowerCAmelCase_						)
              elif "depthwise_kernel" in key:
                     _UpperCAmelCase    :		str     	=					torch.from_numpy(lowerCAmelCase_						).permute(2			,  3			,  0			,  1						)
              elif "kernel" in key:
                     _UpperCAmelCase    :		str     	=					torch.from_numpy(np.transpose(lowerCAmelCase_						)						)
              elif "temperature" in key:
                     _UpperCAmelCase    :		Union[str, Any]     	=					value
              elif "bn/gamma" or "bn/beta" in key:
                     _UpperCAmelCase    :		Optional[Any]     	=					torch.from_numpy(np.transpose(lowerCAmelCase_						)						).squeeze()
              else:
                     _UpperCAmelCase    :		int     	=					torch.from_numpy(lowerCAmelCase_						)

              # Replace HF parameters with original TF model parameters
              hf_params[hf_key].copy_(lowerCAmelCase_						)




@torch.no_grad()
def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[int]     	=					64
       _UpperCAmelCase    :		Optional[int]     	=					Tokenizer(lowerCAmelCase_						)
       _UpperCAmelCase    :		List[Any]     	=					align.Align("""efficientnet-b7"""			,  """bert-base"""			,  640			,  lowerCAmelCase_			,  tok.get_vocab_size()						)
       original_model.compile()
       original_model.load_weights(lowerCAmelCase_						)

       _UpperCAmelCase    :		Optional[int]     	=					original_model.trainable_variables
       _UpperCAmelCase    :		int     	=					original_model.non_trainable_variables
       _UpperCAmelCase    :		Dict     	=					{param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              _UpperCAmelCase    :		Dict     	=					param.numpy()
       _UpperCAmelCase    :		Union[str, Any]     	=					list(tf_params.keys()						)

       # Load HuggingFace model
       _UpperCAmelCase    :		Any     	=					get_align_config()
       _UpperCAmelCase    :		List[str]     	=					AlignModel(lowerCAmelCase_						).eval()
       _UpperCAmelCase    :		List[str]     	=					hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print("""Converting parameters..."""						)
       _UpperCAmelCase    :		List[str]     	=					rename_keys(lowerCAmelCase_						)
       replace_params(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)

       # Initialize processor
       _UpperCAmelCase    :		Union[str, Any]     	=					get_processor()
       _UpperCAmelCase    :		List[str]     	=					processor(
           images=prepare_img()			,  text="""A picture of a cat"""			,  padding="""max_length"""			,  max_length=64			,  return_tensors="""pt"""						)

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              _UpperCAmelCase    :		Union[str, Any]     	=					hf_model(**lowerCAmelCase_						)

       _UpperCAmelCase    :		List[Any]     	=					outputs.image_embeds.detach().numpy()
       _UpperCAmelCase    :		Any     	=					outputs.text_embeds.detach().numpy()

       # Original model inference
       _UpperCAmelCase    :		Dict     	=					False
       _UpperCAmelCase    :		Any     	=					EfficientNetImageProcessor(
           do_center_crop=lowerCAmelCase_			,  do_rescale=lowerCAmelCase_			,  do_normalize=lowerCAmelCase_			,  include_top=lowerCAmelCase_			,  resample=Image.BILINEAR			,  )
       _UpperCAmelCase    :		int     	=					tf_image_processor(images=prepare_img()			,  return_tensors="""tf"""			,  data_format="""channels_last"""						)["""pixel_values"""]
       _UpperCAmelCase    :		Optional[int]     	=					tok(tf.constant(["""A picture of a cat"""]						)						)

       _UpperCAmelCase    :		Union[str, Any]     	=					original_model.image_encoder(lowerCAmelCase_			,  training=lowerCAmelCase_						)
       _UpperCAmelCase    :		Tuple     	=					original_model.text_encoder(lowerCAmelCase_			,  training=lowerCAmelCase_						)

       _UpperCAmelCase    :		str     	=					tf.nn.la_normalize(lowerCAmelCase_			,  axis=-1						)
       _UpperCAmelCase    :		Any     	=					tf.nn.la_normalize(lowerCAmelCase_			,  axis=-1						)

       # Check whether original and HF model outputs match  -> np.allclose
       if not np.allclose(lowerCAmelCase_			,  lowerCAmelCase_			,  atol=1e-3						):
              raise ValueError("""The predicted image features are not the same."""						)
       if not np.allclose(lowerCAmelCase_			,  lowerCAmelCase_			,  atol=1e-3						):
              raise ValueError("""The predicted text features are not the same."""						)
       print("""Model outputs match!"""						)

       if save_model:
              # Create folder to save model
              if not os.path.isdir(lowerCAmelCase_						):
                     os.mkdir(lowerCAmelCase_						)
              # Save converted model and image processor
              hf_model.save_pretrained(lowerCAmelCase_						)
              processor.save_pretrained(lowerCAmelCase_						)

       if push_to_hub:
              # Push model and image processor to hub
              print("""Pushing converted ALIGN to the hub..."""						)
              processor.push_to_hub("""align-base"""						)
              hf_model.push_to_hub("""align-base"""						)


if __name__ == "__main__":
   A_				:					Union[str, Any]							=      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_path""",
       default="""./weights/model-weights""",
       type=str,
       help="""Path to the pretrained TF ALIGN checkpoint.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default="""hf_model""",
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
   parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

   A_				:					Any							=      parser.parse_args()
   convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)











import unittest

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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BlipImageProcessor


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=None							, _UpperCAmelCase=True							, _UpperCAmelCase=False							, _UpperCAmelCase=[0.48145466, 0.4578275, 0.40821073]							, _UpperCAmelCase=[0.26862954, 0.26130258, 0.27577711]							, _UpperCAmelCase=True							, )		->				List[str]:
        lowerCamelCase									=size if size is not None else {"""height""": 20, """width""": 20}
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_resize
        lowerCamelCase									=size
        lowerCamelCase									=do_normalize
        lowerCamelCase									=image_mean
        lowerCamelCase									=image_std
        lowerCamelCase									=do_pad
        lowerCamelCase									=do_convert_rgb





     def 						lowerCamelCase							(	self  )		->				str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				Optional[int]:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    str 	=     BlipImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=BlipImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_resize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """size"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_normalize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_mean"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_std"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_convert_rgb"""  )  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        pass





     def 						lowerCamelCase							(	self  )		->				int:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Any 	=     BlipImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        lowerCamelCase									=BlipImageProcessingTester(self							, num_channels=4  )
        lowerCamelCase									=3





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				List[str]:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_resize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """size"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_normalize"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_mean"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """image_std"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_convert_rgb"""  )  )





     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        pass





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )

        # Test batched
        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape							, (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )							, )