
'''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()
UpperCAmelCase            = logging.get_logger(__name__)

def   A			(							A_				:       Union[str, Any]							):
						snake_case							:  Optional[Any]			=     tf.image.resize(A_     ,				(346, 346)							)
						snake_case							:  Optional[Any]			=     tf.image.crop_to_bounding_box(A_     ,				(346 - 289) // 2     ,				(346 - 289) // 2     ,				289     ,				289							)
						return image

def   A			(							):
						snake_case							:  Tuple			=     EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''							)
						snake_case							:  Tuple			=     289
						snake_case							:  int			=     640
						snake_case							:  Optional[Any]			=     {'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
						snake_case							:  Tuple			=     {'''LABEL_0''': 0, '''LABEL_1''': 1}
						snake_case							:  Optional[Any]			=     []

						snake_case							:  Any			=     BertConfig()
						snake_case							:  List[str]			=     AlignConfig.from_text_vision_configs(
						    text_config=A_     ,				vision_config=A_     ,				projection_dim=640							)
						return config

def   A			(							):
						snake_case							:  Tuple			=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case							:  Optional[Any]			=     Image.open(requests.get(A_     ,				stream=A_							).raw							)
						return im

def   A			(							):
						snake_case							:  List[str]			=     EfficientNetImageProcessor(
						    do_center_crop=A_     ,				rescale_factor=1 / 127.5     ,				rescale_offset=A_     ,				do_normalize=A_     ,				include_top=A_     ,				resample=Image.BILINEAR     ,				)
						snake_case							:  Tuple			=     BertTokenizer.from_pretrained('''bert-base-uncased'''							)
						snake_case							:  List[str]			=     64
						snake_case							:  List[str]			=     AlignProcessor(image_processor=A_     ,				tokenizer=A_							)
						return processor

def   A			(							A_				:       Dict							):
						# EfficientNet image encoder
						snake_case							:  Dict			=     [v.split('''_'''							)[0].split('''block'''							)[1] for v in original_param_names if v.startswith('''block'''							)]
						snake_case							:  str			=     list(set(A_							)							)
						snake_case							:  Union[str, Any]			=     sorted(A_							)
						snake_case							:  int			=     len(A_							)
						snake_case							:  Dict			=     {b: str(A_							) for b, i in zip(A_     ,				range(A_							)							)}

						snake_case							:  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:
												snake_case							:  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""")							)

						snake_case							:  Optional[int]			=     {}
						for item in rename_keys:
												if item[0] in original_param_names:
																		snake_case							:  int			=     '''vision_model.''' + item[1]

    # BERT text encoder
						snake_case							:  Union[str, Any]			=     []
						snake_case							:  Optional[Any]			=     '''tf_bert_model/bert'''
						snake_case							:  Optional[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:
																		snake_case							:  Tuple			=     item[1]
						return key_mapping

def   A			(							A_				:       Union[str, Any]     ,				A_				:       List[str]     ,				A_				:       Any							):
						list(hf_params.keys()							)

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

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

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

@torch.no_grad()
def   A			(							A_				:       Union[str, Any]     ,				A_				:       Tuple     ,				A_				:       Optional[int]     ,				A_				:       List[str]							):
						snake_case							:  Dict			=     64
						snake_case							:  int			=     Tokenizer(A_							)
						snake_case							:  Dict			=     align.Align('''efficientnet-b7'''     ,				'''bert-base'''     ,				640     ,				A_     ,				tok.get_vocab_size()							)
						original_model.compile()
						original_model.load_weights(A_							)

						snake_case							:  List[Any]			=     original_model.trainable_variables
						snake_case							:  Optional[Any]			=     original_model.non_trainable_variables
						snake_case							:  List[Any]			=     {param.name: param.numpy() for param in tf_params}
						for param in tf_non_train_params:
												snake_case							:  Any			=     param.numpy()
						snake_case							:  List[str]			=     list(tf_params.keys()							)

						# Load HuggingFace model
						snake_case							:  int			=     get_align_config()
						snake_case							:  Any			=     AlignModel(A_							).eval()
						snake_case							:  Any			=     hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print('''Converting parameters...'''							)
						snake_case							:  Tuple			=     rename_keys(A_							)
						replace_params(A_     ,				A_     ,				A_							)

						# Initialize processor
						snake_case							:  int			=     get_processor()
						snake_case							:  Optional[Any]			=     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():
												snake_case							:  Optional[int]			=     hf_model(**A_							)

						snake_case							:  List[str]			=     outputs.image_embeds.detach().numpy()
						snake_case							:  int			=     outputs.text_embeds.detach().numpy()

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

						snake_case							:  int			=     original_model.image_encoder(A_     ,				training=A_							)
						snake_case							:  Any			=     original_model.text_encoder(A_     ,				training=A_							)

						snake_case							:  Any			=     tf.nn.la_normalize(A_     ,				axis=-1							)
						snake_case							:  List[str]			=     tf.nn.la_normalize(A_     ,				axis=-1							)

						# Check whether original and HF model outputs match  -> np.allclose
						if not np.allclose(A_     ,				A_     ,				atol=1e-3							):
												raise ValueError('''The predicted image features are not the same.'''							)
						if not np.allclose(A_     ,				A_     ,				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(A_							):
																		os.mkdir(A_							)
												# Save converted model and image processor
												hf_model.save_pretrained(A_							)
												processor.save_pretrained(A_							)

						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__":
						UpperCAmelCase            = 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")

						UpperCAmelCase            = 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 datasets import load_dataset

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 SegformerImageProcessor







class __snake_case     (     unittest.TestCase						):







   def __init__( self						:		List[Any]   ,		_snake_case						:		List[str]   ,		_snake_case						:		Any=7   ,		_snake_case						:		Any=3   ,		_snake_case						:		str=30   ,		_snake_case						:		str=400   ,		_snake_case						:		Any=True   ,		_snake_case						:		Dict=None   ,		_snake_case						:		List[str]=True   ,		_snake_case						:		Union[str, Any]=[0.5, 0.5, 0.5]   ,		_snake_case						:		Tuple=[0.5, 0.5, 0.5]   ,		_snake_case						:		str=False   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				size if size is not None else {'''height''': 30, '''width''': 30}
        UpperCAmelCase_		=				parent
        UpperCAmelCase_		=				batch_size
        UpperCAmelCase_		=				num_channels
        UpperCAmelCase_		=				min_resolution
        UpperCAmelCase_		=				max_resolution
        UpperCAmelCase_		=				do_resize
        UpperCAmelCase_		=				size
        UpperCAmelCase_		=				do_normalize
        UpperCAmelCase_		=				image_mean
        UpperCAmelCase_		=				image_std
        UpperCAmelCase_		=				do_reduce_labels







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        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_reduce_labels": self.do_reduce_labels,
        }







def   A		()  -> List[str]:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)

     return image, map


def   A		()  -> str:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[2]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[3]['''file''']			)

     return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       int		=    SegformerImageProcessor if is_vision_available() else None







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				SegformerImageProcessingTester(self)







   @property
   def lowerCamelCase		( self						:		int):

        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()







   def lowerCamelCase		( self						:		List[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(_snake_case   ,		'''do_resize'''))
        self.assertTrue(hasattr(_snake_case   ,		'''size'''))
        self.assertTrue(hasattr(_snake_case   ,		'''do_normalize'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_mean'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_std'''))
        self.assertTrue(hasattr(_snake_case   ,		'''do_reduce_labels'''))







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size   ,		{'''height''': 30, '''width''': 30})
        self.assertEqual(image_processor.do_reduce_labels   ,		_snake_case)

        UpperCAmelCase_		=				self.image_processing_class.from_dict(self.image_processor_dict   ,		size=42   ,		reduce_labels=_snake_case)
        self.assertEqual(image_processor.size   ,		{'''height''': 42, '''width''': 42})
        self.assertEqual(image_processor.do_reduce_labels   ,		_snake_case)







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        pass







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		Image.Image)

        # Test not batched input
        UpperCAmelCase_		=				image_processing(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
        UpperCAmelCase_		=				image_processing(_snake_case   ,		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[int]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		numpify=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		np.ndarray)

        # Test not batched input
        UpperCAmelCase_		=				image_processing(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
        UpperCAmelCase_		=				image_processing(_snake_case   ,		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]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		torchify=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		torch.Tensor)

        # Test not batched input
        UpperCAmelCase_		=				image_processing(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
        UpperCAmelCase_		=				image_processing(_snake_case   ,		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						:		str):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		torchify=_snake_case)
        UpperCAmelCase_		=				[]
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		torch.Tensor)
             maps.append(torch.zeros(image.shape[-2:]).long())

        # Test not batched input
        UpperCAmelCase_		=				image_processing(image_inputs[0]   ,		maps[0]   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test batched
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].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'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test not batched input (PIL images)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test batched input (PIL images)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_batch_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                2,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 150)

        UpperCAmelCase_		=				True
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)






