

'''simple docstring'''
import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowerCamelCase      					=     2
class 					_snake_case		:






			def __init__(  self			,*,  # begin keyword-only arguments
			    _snake_case="<s>"			,_snake_case="<pad>"			,_snake_case="</s>"			,_snake_case="<unk>"			,_snake_case=None			,):
				UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  bos, unk, pad, eos
				UpperCAmelCase_     :      Dict						=  []
				UpperCAmelCase_     :      Dict						=  []
				UpperCAmelCase_     :      Dict						=  {}
				UpperCAmelCase_     :      Optional[int]						=  self.add_symbol(_snake_case     )
				UpperCAmelCase_     :      Tuple						=  self.add_symbol(_snake_case     )
				UpperCAmelCase_     :      Dict						=  self.add_symbol(_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  self.add_symbol(_snake_case     )
				if extra_special_symbols:
					for s in extra_special_symbols:
						self.add_symbol(_snake_case     )
				UpperCAmelCase_     :      Tuple						=  len(self.symbols     )






			def __eq__(  self			,_snake_case     ):
				return self.indices == other.indices






			def __getitem__(  self			,_snake_case     ):
				if idx < len(self.symbols     ):
					return self.symbols[idx]
				return self.unk_word






			def __len__(  self     ):
				return len(self.symbols     )






			def __contains__(  self			,_snake_case     ):
				return sym in self.indices






			@classmethod
			def 		UpperCamelCase__							(  cls			,_snake_case     ):
				UpperCAmelCase_     :      Dict						=  cls()
				d.add_from_file(_snake_case     )
				return d






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=1			,_snake_case=False     ):
				if word in self.indices and not overwrite:
					UpperCAmelCase_     :      Union[str, Any]						=  self.indices[word]
					UpperCAmelCase_     :      Optional[int]						=  self.count[idx] + n
					return idx
				else:
					UpperCAmelCase_     :      List[Any]						=  len(self.symbols     )
					UpperCAmelCase_     :      int						=  idx
					self.symbols.append(_snake_case     )
					self.count.append(_snake_case     )
					return idx






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				return 0






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				if isinstance(_snake_case			,_snake_case     ):
					try:
						with open(_snake_case			,"r"			,encoding="utf-8"     ) as fd:
							self.add_from_file(_snake_case     )
					except FileNotFoundError as fnfe:
						raise fnfe
					except UnicodeError:
						raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(_snake_case     )     )
					return

				UpperCAmelCase_     :      Optional[int]						=  f.readlines()
				UpperCAmelCase_     :      Dict						=  self._load_meta(_snake_case     )

				for line in lines[indices_start_line:]:
					try:
						UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  line.rstrip().rsplit(" "			,1     )
						if field == "#fairseq:overwrite":
							UpperCAmelCase_     :      List[Any]						=  True
							UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  line.rsplit(" "			,1     )
						else:
							UpperCAmelCase_     :      Any						=  False
						UpperCAmelCase_     :      Optional[Any]						=  int(_snake_case     )
						UpperCAmelCase_     :      Any						=  line
						if word in self and not overwrite:
							raise RuntimeError(
							    "Duplicate word found when loading Dictionary: '{}'. "
							    "Duplicate words can overwrite earlier ones by adding the "
							    "#fairseq:overwrite flag at the end of the corresponding row "
							    "in the dictionary file. If using the Camembert model, please "
							    "download an updated copy of the model file.".format(_snake_case     )     )
						self.add_symbol(_snake_case			,n=_snake_case			,overwrite=_snake_case     )
					except ValueError:
						raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"     )




def  a__       (    _SCREAMING_SNAKE_CASE   :       List[Any]				)     ->					Any:


	"""simple docstring"""





	UpperCAmelCase_     :      Union[str, Any]						=  dict((re.sub(r"@@$"	,				""	,				_SCREAMING_SNAKE_CASE				), v) if k.endswith("@@"				) else (re.sub(r"$"	,				"</w>"	,				_SCREAMING_SNAKE_CASE				), v) for k, v in d.items()				)
	UpperCAmelCase_     :      Any						=  "<s> <pad> </s> <unk>".split()
	# restore the special tokens
	for k in keep_keys:
		del da[F'''{k}</w>''']
		UpperCAmelCase_     :      int						=  d[k]  # restore
	return da


def  a__       (    _SCREAMING_SNAKE_CASE   :       List[str]	,				_SCREAMING_SNAKE_CASE   :       Union[str, Any]				)     ->					Union[str, Any]:


	"""simple docstring"""





	if not os.path.exists(_SCREAMING_SNAKE_CASE				):
		raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''				)
	os.makedirs(_SCREAMING_SNAKE_CASE	,				exist_ok=_SCREAMING_SNAKE_CASE				)
	print(F'''Writing results to {pytorch_dump_folder_path}'''				)

	# handle various types of models

	UpperCAmelCase_     :      str						=  os.path.join(_SCREAMING_SNAKE_CASE	,				"checkpoint.pt"				)
	if not os.path.isfile(_SCREAMING_SNAKE_CASE				):
		raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''				)
	UpperCAmelCase_     :      Tuple						=  torch.load(_SCREAMING_SNAKE_CASE	,				map_location="cpu"				)

	UpperCAmelCase_     :      Optional[Any]						=  chkpt["cfg"]["model"]

	# dicts
	UpperCAmelCase_     :      Optional[Any]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				"dict.txt"				)
	if not os.path.isfile(_SCREAMING_SNAKE_CASE				):
		raise ValueError(F'''path to the file {dict_file} does not exist!'''				)
	UpperCAmelCase_     :      Optional[int]						=  Dictionary.load(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Tuple						=  rewrite_dict_keys(src_dict.indices				)
	UpperCAmelCase_     :      List[Any]						=  len(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Optional[int]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				VOCAB_FILES_NAMES["vocab_file"]				)
	print(F'''Generating {src_vocab_file} of {src_vocab_size} records'''				)
	with open(_SCREAMING_SNAKE_CASE	,				"w"	,				encoding="utf-8"				) as f:
		f.write(json.dumps(_SCREAMING_SNAKE_CASE	,				ensure_ascii=_SCREAMING_SNAKE_CASE	,				indent=_SCREAMING_SNAKE_CASE				)				)

	# merges_file (bpecodes)
	UpperCAmelCase_     :      Optional[Any]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				"bpecodes"				)
	if not os.path.isfile(_SCREAMING_SNAKE_CASE				):
		raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''				)

	UpperCAmelCase_     :      Optional[int]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				VOCAB_FILES_NAMES["merges_file"]				)
	shutil.copyfile(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	# model config
	UpperCAmelCase_     :      str						=  os.path.join(_SCREAMING_SNAKE_CASE	,				"config.json"				)

	UpperCAmelCase_     :      List[str]						=  {
	    "activation_dropout": args["activation_dropout"],
	    "architectures": ["BioGptForCausalLM"],
	    "attention_probs_dropout_prob": args["attention_dropout"],
	    "bos_token_id": 0,
	    "eos_token_id": 2,
	    "hidden_act": args["activation_fn"],
	    "hidden_dropout_prob": args["dropout"],
	    "hidden_size": args["decoder_embed_dim"],
	    "initializer_range": 0.02,
	    "intermediate_size": args["decoder_ffn_embed_dim"],
	    "layer_norm_eps": 1E-12,
	    "layerdrop": args["decoder_layerdrop"],
	    "max_position_embeddings": args["max_target_positions"],
	    "model_type": "biogpt",
	    "num_attention_heads": args["decoder_attention_heads"],
	    "num_hidden_layers": args["decoder_layers"],
	    "pad_token_id": 1,
	    "scale_embedding": not args["no_scale_embedding"],
	    "tie_word_embeddings": args["share_decoder_input_output_embed"],
	    "vocab_size": src_vocab_size,
	}

	# good hparam defaults to start with

	print(F'''Generating {biogpt_model_config_file}'''				)
	with open(_SCREAMING_SNAKE_CASE	,				"w"	,				encoding="utf-8"				) as f:
		f.write(json.dumps(_SCREAMING_SNAKE_CASE	,				ensure_ascii=_SCREAMING_SNAKE_CASE	,				indent=_SCREAMING_SNAKE_CASE				)				)

	# tokenizer config
	UpperCAmelCase_     :      Optional[int]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Any						=  {
	    "bos_token": "<s>",
	    "eos_token": "</s>",
	    "model_max_length": 10_24,
	    "pad_token": "<pad>",
	    "special_tokens_map_file": None,
	    "tokenizer_class": "BioGptTokenizer",
	    "unk_token": "<unk>",
	}

	print(F'''Generating {biogpt_tokenizer_config_file}'''				)
	with open(_SCREAMING_SNAKE_CASE	,				"w"	,				encoding="utf-8"				) as f:
		f.write(json.dumps(_SCREAMING_SNAKE_CASE	,				ensure_ascii=_SCREAMING_SNAKE_CASE	,				indent=_SCREAMING_SNAKE_CASE				)				)

	# model
	UpperCAmelCase_     :      Union[str, Any]						=  chkpt["model"]

	# remove unneeded keys
	UpperCAmelCase_     :      Dict						=  [
	    "decoder.version",
	]
	for k in ignore_keys:
		model_state_dict.pop(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Tuple						=  list(model_state_dict.keys()				)
	for layer_name in layer_names:
		if layer_name.endswith("output_projection.weight"				):
			UpperCAmelCase_     :      Dict						=  model_state_dict.pop(_SCREAMING_SNAKE_CASE				)
		else:
			UpperCAmelCase_     :      str						=  model_state_dict.pop(_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Tuple						=  BioGptConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Any						=  BioGptForCausalLM(_SCREAMING_SNAKE_CASE				)

	# check that it loads ok
	model_new.load_state_dict(_SCREAMING_SNAKE_CASE				)

	# save
	UpperCAmelCase_     :      int						=  os.path.join(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	print(F'''Generating {pytorch_weights_dump_path}'''				)
	torch.save(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	print("Conversion is done!"				)


if __name__ == "__main__":
					_lowerCamelCase      					=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--biogpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
					        """ bpecodes, etc."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					_lowerCamelCase      					=     parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""

import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
    from transformers.models.mobilevitva.modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        make_divisible,
    )


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.config_class(**self.inputs_dict   )
        self.parent.assertTrue(hasattr(snake_case			,					"width_multiplier"   )   )






class 			__a					:





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=13			,					snake_case=64			,					snake_case=2			,					snake_case=3			,					snake_case="swish"			,					snake_case=3			,					snake_case=32			,					snake_case=0.1			,					snake_case=0.02			,					snake_case=True			,					snake_case=True			,					snake_case=10			,					snake_case=None			,					snake_case=0.25			,					snake_case=0.0			,					snake_case=0.0			,					):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				parent
        lowerCAmelCase__   :				Optional[Any] 			=				batch_size
        lowerCAmelCase__   :				Optional[Any] 			=				image_size
        lowerCAmelCase__   :				Tuple 			=				patch_size
        lowerCAmelCase__   :				Any 			=				num_channels
        lowerCAmelCase__   :				Tuple 			=				make_divisible(512 * width_multiplier			,					divisor=8   )
        lowerCAmelCase__   :				str 			=				hidden_act
        lowerCAmelCase__   :				Union[str, Any] 			=				conv_kernel_size
        lowerCAmelCase__   :				List[str] 			=				output_stride
        lowerCAmelCase__   :				List[Any] 			=				classifier_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				use_labels
        lowerCAmelCase__   :				Tuple 			=				is_training
        lowerCAmelCase__   :				Optional[int] 			=				num_labels
        lowerCAmelCase__   :				int 			=				initializer_range
        lowerCAmelCase__   :				str 			=				scope
        lowerCAmelCase__   :				Optional[Any] 			=				width_multiplier
        lowerCAmelCase__   :				Union[str, Any] 			=				ffn_dropout
        lowerCAmelCase__   :				Tuple 			=				attn_dropout






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

        lowerCAmelCase__   :				Optional[Any] 			=				None
        lowerCAmelCase__   :				List[str] 			=				None
        if self.use_labels:
         lowerCAmelCase__   :				Dict 			=				ids_tensor([self.batch_size]			,					self.num_labels   )
         lowerCAmelCase__   :				Dict 			=				ids_tensor([self.batch_size, self.image_size, self.image_size]			,					self.num_labels   )

        lowerCAmelCase__   :				int 			=				self.get_config()

        return config, pixel_values, labels, pixel_labels






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return MobileViTVaConfig(
            image_size=self.image_size			,					patch_size=self.patch_size			,					num_channels=self.num_channels			,					hidden_act=self.hidden_act			,					conv_kernel_size=self.conv_kernel_size			,					output_stride=self.output_stride			,					classifier_dropout_prob=self.classifier_dropout_prob			,					initializer_range=self.initializer_range			,					width_multiplier=self.width_multiplier			,					ffn_dropout=self.ffn_dropout_prob			,					attn_dropout=self.attn_dropout_prob			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				MobileViTVaModel(config=snake_case   )
        model.to(snake_case   )
        model.eval()
        lowerCAmelCase__   :				int 			=				model(snake_case   )
        self.parent.assertEqual(
            result.last_hidden_state.shape			,					(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.num_labels
        lowerCAmelCase__   :				Any 			=				MobileViTVaForImageClassification(snake_case   )
        model.to(snake_case   )
        model.eval()
        lowerCAmelCase__   :				int 			=				model(snake_case			,					labels=snake_case   )
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.num_labels
        lowerCAmelCase__   :				Optional[int] 			=				MobileViTVaForSemanticSegmentation(snake_case   )
        model.to(snake_case   )
        model.eval()
        lowerCAmelCase__   :				Any 			=				model(snake_case   )
        self.parent.assertEqual(
            result.logits.shape			,					(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )			,					)
        lowerCAmelCase__   :				Tuple 			=				model(snake_case			,					labels=snake_case   )
        self.parent.assertEqual(
            result.logits.shape			,					(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.prepare_config_and_inputs()
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Any 			=				config_and_inputs
        lowerCAmelCase__   :				int 			=				{"pixel_values": pixel_values}
        return config, inputs_dict






@require_torch
class 			__a					(						__magic_name__       ,	__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      List[Any]    =						(
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

       __UpperCamelCase					:      List[Any]    =						(
           {
               'feature-extraction': MobileViTVaModel,
               'image-classification': MobileViTVaForImageClassification,
               'image-segmentation': MobileViTVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       __UpperCamelCase					:      str    =						False
       __UpperCamelCase					:      int    =						False
       __UpperCamelCase					:      Tuple    =						False
       __UpperCamelCase					:      List[str]    =						False






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				MobileViTVaModelTester(self   )
        lowerCAmelCase__   :				List[str] 			=				MobileViTVaConfigTester(self			,					config_class=snake_case			,					has_text_modality=snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        self.config_tester.run_common_tests()






       @unittest.skip(reason="MobileViTV2 does not use inputs_embeds"   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @unittest.skip(reason="MobileViTV2 does not output attentions"   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @require_torch_multi_gpu
       @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__			,						lowerCAmelCase__   :				Tuple 			=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__   :				Union[str, Any] 			=				model_class(snake_case   )
         lowerCAmelCase__   :				Tuple 			=				inspect.signature(model.forward   )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCAmelCase__   :				int 			=				[*signature.parameters.keys()]

         lowerCAmelCase__   :				Dict 			=				["pixel_values"]
         self.assertListEqual(arg_names[:1]			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        def check_hidden_states_output(snake_case			,					snake_case			,					snake_case   ):
         lowerCAmelCase__   :				List[str] 			=				model_class(snake_case   )
         model.to(snake_case   )
         model.eval()

         with torch.no_grad():
          lowerCAmelCase__   :				Optional[int] 			=				model(**self._prepare_for_class(snake_case			,					snake_case   )   )

         lowerCAmelCase__   :				str 			=				outputs.hidden_states

         lowerCAmelCase__   :				List[Any] 			=				5
         self.assertEqual(len(snake_case   )			,					snake_case   )

         # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
         # with the width and height being successively divided by 2.
         lowerCAmelCase__   :				str 			=				2
         for i in range(len(snake_case   )   ):
          self.assertListEqual(
              list(hidden_states[i].shape[-2:]   )			,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			,					)
          divisor *= 2

         self.assertEqual(self.model_tester.output_stride			,					divisor // 2   )

        lowerCAmelCase__			,						lowerCAmelCase__   :				Tuple 			=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__   :				int 			=				True
         check_hidden_states_output(snake_case			,					snake_case			,					snake_case   )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCAmelCase__   :				Dict 			=				True

         check_hidden_states_output(snake_case			,					snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*snake_case   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__   :				Dict 			=				MobileViTVaModel.from_pretrained(snake_case   )
         self.assertIsNotNone(snake_case   )




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






@require_torch
@require_vision
class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       @cached_property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return (
            MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"   )
            if is_vision_available()
            else None
        )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"   ).to(
            snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				self.default_image_processor
        lowerCAmelCase__   :				Union[str, Any] 			=				prepare_img()
        lowerCAmelCase__   :				List[str] 			=				image_processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__   :				str 			=				model(**snake_case   )

        # verify the logits
        lowerCAmelCase__   :				int 			=				torch.Size((1, 1_000)   )
        self.assertEqual(outputs.logits.shape			,					snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]   ).to(snake_case   )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]			,					snake_case			,					atol=1e-4   )   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )
        lowerCAmelCase__   :				Tuple 			=				model.to(snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )

        lowerCAmelCase__   :				Union[str, Any] 			=				prepare_img()
        lowerCAmelCase__   :				str 			=				image_processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__   :				Any 			=				model(**snake_case   )
        lowerCAmelCase__   :				List[str] 			=				outputs.logits

        # verify the logits
        lowerCAmelCase__   :				Tuple 			=				torch.Size((1, 21, 32, 32)   )
        self.assertEqual(logits.shape			,					snake_case   )

        lowerCAmelCase__   :				Dict 			=				torch.tensor(
            [
                [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
                [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
                [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
            ]			,					device=snake_case			,					)

        self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,					snake_case			,					atol=1e-4   )   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )
        lowerCAmelCase__   :				List[Any] 			=				model.to(snake_case   )

        lowerCAmelCase__   :				str 			=				MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )

        lowerCAmelCase__   :				Optional[int] 			=				prepare_img()
        lowerCAmelCase__   :				List[Any] 			=				image_processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__   :				Union[str, Any] 			=				model(**snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				outputs.logits.detach().cpu()

        lowerCAmelCase__   :				Optional[int] 			=				image_processor.post_process_semantic_segmentation(outputs=snake_case			,					target_sizes=[(50, 60)]   )
        lowerCAmelCase__   :				Union[str, Any] 			=				torch.Size((50, 60)   )
        self.assertEqual(segmentation[0].shape			,					snake_case   )

        lowerCAmelCase__   :				Any 			=				image_processor.post_process_semantic_segmentation(outputs=snake_case   )
        lowerCAmelCase__   :				Optional[int] 			=				torch.Size((32, 32)   )
        self.assertEqual(segmentation[0].shape			,					snake_case   )






