






import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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 MobileNetVaForImageClassification, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor





class  __magic_name__   (							A__						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.config_class(**self.inputs_dict      )
								self.parent.assertTrue(hasattr(UpperCamelCase__				,		"tf_padding"      )      )
								self.parent.assertTrue(hasattr(UpperCamelCase__				,		"depth_multiplier"      )      )





class  __magic_name__   :






		def __init__(						self      :     List[Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     int=13				,		UpperCamelCase__      :     Optional[int]=3				,		UpperCamelCase__      :     Dict=32				,		UpperCamelCase__      :     int=0.25				,		UpperCamelCase__      :     Optional[int]=8				,		UpperCamelCase__      :     str=True				,		UpperCamelCase__      :     List[Any]=10_24				,		UpperCamelCase__      :     str=32				,		UpperCamelCase__      :     Tuple="relu6"				,		UpperCamelCase__      :     Dict=0.1				,		UpperCamelCase__      :     int=0.02				,		UpperCamelCase__      :     int=True				,		UpperCamelCase__      :     Any=True				,		UpperCamelCase__      :     int=10				,		UpperCamelCase__      :     Any=None				,		)				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     num_channels
								UpperCAmelCase         =     image_size
								UpperCAmelCase         =     depth_multiplier
								UpperCAmelCase         =     min_depth
								UpperCAmelCase         =     tf_padding
								UpperCAmelCase         =     int(last_hidden_size * depth_multiplier      )
								UpperCAmelCase         =     output_stride
								UpperCAmelCase         =     hidden_act
								UpperCAmelCase         =     classifier_dropout_prob
								UpperCAmelCase         =     use_labels
								UpperCAmelCase         =     is_training
								UpperCAmelCase         =     num_labels
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     scope






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								UpperCAmelCase         =     None
								UpperCAmelCase         =     None
								if self.use_labels:
														UpperCAmelCase         =     ids_tensor([self.batch_size]				,		self.num_labels      )
														UpperCAmelCase         =     ids_tensor([self.batch_size, self.image_size, self.image_size]				,		self.num_labels      )

								UpperCAmelCase         =     self.get_config()

								return config, pixel_values, labels, pixel_labels






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					str:
								'''simple docstring'''







								return MobileNetVaConfig(
								    num_channels=self.num_channels				,		image_size=self.image_size				,		depth_multiplier=self.depth_multiplier				,		min_depth=self.min_depth				,		tf_padding=self.tf_padding				,		hidden_act=self.hidden_act				,		classifier_dropout_prob=self.classifier_dropout_prob				,		initializer_range=self.initializer_range				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     int      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     MobileNetVaModel(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__      )
								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      :     Dict				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Union[str, Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_labels
								UpperCAmelCase         =     MobileNetVaForImageClassification(UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__				,		labels=UpperCamelCase__      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)      )







		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Union[str, Any]:
								'''simple docstring'''







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





@require_torch
class  __magic_name__   (							A__,  A__,  unittest.TestCase						):
		lowercase      :       List[str]          =(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
		lowercase      :       List[Any]          =(
		    {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowercase      :       Optional[int]          =False
		lowercase      :       Optional[int]          =False
		lowercase      :       Union[str, Any]          =False
		lowercase      :       Any          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     MobileNetVaModelTester(self      )
								UpperCAmelCase         =     MobileNetVaConfigTester(self				,		config_class=UpperCamelCase__				,		has_text_modality=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Tuple:
								'''simple docstring'''







								self.config_tester.run_common_tests()






		@unittest.skip(reason="MobileNetV1 does not use inputs_embeds"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[int]:
								'''simple docstring'''







								pass






		@unittest.skip(reason="MobileNetV1 does not support input and output embeddings"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					str:
								'''simple docstring'''







								pass






		@unittest.skip(reason="MobileNetV1 does not output attentions"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Union[str, Any]:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														UpperCAmelCase         =     model_class(UpperCamelCase__      )
														UpperCAmelCase         =     inspect.signature(model.forward      )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														UpperCAmelCase         =     [*signature.parameters.keys()]

														UpperCAmelCase         =     ["pixel_values"]
														self.assertListEqual(arg_names[:1]				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Union[str, Any]:
								'''simple docstring'''







								def check_hidden_states_output(UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Optional[int]      ):
														UpperCAmelCase         =     model_class(UpperCamelCase__      )
														model.to(UpperCamelCase__      )
														model.eval()

														with torch.no_grad():
																				UpperCAmelCase         =     model(**self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__      )      )

														UpperCAmelCase         =     outputs.hidden_states

														UpperCAmelCase         =     26
														self.assertEqual(len(UpperCamelCase__      )				,		UpperCamelCase__      )

								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														UpperCAmelCase         =     True
														check_hidden_states_output(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														UpperCAmelCase         =     True

														check_hidden_states_output(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__      )







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







								for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCAmelCase         =     MobileNetVaModel.from_pretrained(UpperCamelCase__      )
														self.assertIsNotNone(UpperCamelCase__      )

def    lowerCamelCase_() ->       Any:
						UpperCAmelCase         =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image





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






		@cached_property
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					List[Any]:
								'''simple docstring'''







								return (
								    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"      ) if is_vision_available() else None
								)







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"      ).to(UpperCamelCase__      )

								UpperCAmelCase         =     self.default_image_processor
								UpperCAmelCase         =     prepare_img()
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="pt"      ).to(UpperCamelCase__      )

								# forward pass
								with torch.no_grad():
														UpperCAmelCase         =     model(**UpperCamelCase__      )

								# verify the logits
								UpperCAmelCase         =     torch.Size((1, 10_01)      )
								self.assertEqual(outputs.logits.shape				,		UpperCamelCase__      )

								UpperCAmelCase         =     torch.tensor([-4.17_39, -1.12_33, 3.12_05]      ).to(UpperCamelCase__      )

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








import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_ta import TaTokenizer
else:
		__lowerCamelCase     :							Optional[int]  							=				None


__lowerCamelCase     :							int  							=				logging.get_logger(__name__)

__lowerCamelCase     :							Any  							=				{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__lowerCamelCase     :							List[str]  							=				{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__lowerCamelCase     :							List[str]  							=				{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}





class  __magic_name__   (							A__						):
		lowercase      :       List[str]          =VOCAB_FILES_NAMES
		lowercase      :       int          =PRETRAINED_VOCAB_FILES_MAP
		lowercase      :       Union[str, Any]          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase      :       Union[str, Any]          =['''input_ids''', '''attention_mask''']
		lowercase      :       Any          =TaTokenizer

		lowercase      :       List[int]          =[]






		def __init__(						self      :     Optional[Any]				,		UpperCamelCase__      :     List[str]=None				,		UpperCamelCase__      :     List[str]=None				,		UpperCamelCase__      :     Any="</s>"				,		UpperCamelCase__      :     Dict="<unk>"				,		UpperCamelCase__      :     Tuple="<pad>"				,		UpperCamelCase__      :     Tuple=1_00				,		UpperCamelCase__      :     List[Any]=None				,		**UpperCamelCase__      :     int				,		)				->					Optional[Any]:
								'''simple docstring'''







								if extra_ids > 0 and additional_special_tokens is None:
														UpperCAmelCase         =     [F'<extra_id_{i}>' for i in range(UpperCamelCase__      )]
								elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra special tokens
														UpperCAmelCase         =     len(set(filter(lambda UpperCamelCase__      : bool("extra_id_" in str(UpperCamelCase__      )      )				,		UpperCamelCase__      )      )      )
														if extra_tokens != extra_ids:
																				raise ValueError(
																				    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
																				    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
																				    " tokens"      )

								super().__init__(
								    UpperCamelCase__				,		tokenizer_file=UpperCamelCase__				,		eos_token=UpperCamelCase__				,		unk_token=UpperCamelCase__				,		pad_token=UpperCamelCase__				,		extra_ids=UpperCamelCase__				,		additional_special_tokens=UpperCamelCase__				,		**UpperCamelCase__				,		)

								UpperCAmelCase         =     vocab_file
								UpperCAmelCase         =     False if not self.vocab_file else True
								UpperCAmelCase         =     extra_ids






		@staticmethod
		def SCREAMING_SNAKE_CASE_			(						UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														UpperCAmelCase         =     TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																				return init_max_model_length
														elif init_max_model_length is None:
																				warnings.warn(
																				    "This tokenizer was incorrectly instantiated with a model max length of"
																				    F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
																				    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
																				    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
																				    F' {pretrained_model_name_or_path} automatically truncating your input to'
																				    F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
																				    F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
																				    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
																				    " instantiate this tokenizer with `model_max_length` set to your preferred value."				,		UpperCamelCase__				,		)

								return max_model_length






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[str] = None      )				->					Tuple[str]:
								'''simple docstring'''







								if not self.can_save_slow_tokenizer:
														raise ValueError(
														    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
														    "tokenizer."      )

								if not os.path.isdir(UpperCamelCase__      ):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
														return
								UpperCAmelCase         =     os.path.join(
								    UpperCamelCase__				,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

								if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase__      ):
														copyfile(self.vocab_file				,		UpperCamelCase__      )
														logger.info(F'Copy vocab file to {out_vocab_file}'      )

								return (out_vocab_file,)






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None      )				->					List[int]:
								'''simple docstring'''







								UpperCAmelCase         =     token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
								else:
														UpperCAmelCase         =     token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     Optional[List[int]] = None      )				->					List[int]:
								'''simple docstring'''







								UpperCAmelCase         =     [self.eos_token_id]

								if token_ids_a is None:
														return len(token_ids_a + eos      ) * [0]
								return len(token_ids_a + eos + token_ids_a + eos      ) * [0]






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								return list(
								    set(filter(lambda UpperCamelCase__      : bool(re.search(R"<extra_id_\d+>"				,		UpperCamelCase__      )      ) is not None				,		self.additional_special_tokens      )      )      )







		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







								return [self.convert_tokens_to_ids(UpperCamelCase__      ) for token in self.get_sentinel_tokens()]



