






"""simple docstring"""



import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__A		:       List[str]  				=		logging.get_logger(__name__)


__A		:       Optional[Any]  				=		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A		:       Optional[Any]  				=		{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

__A		:       int  				=		{
    '''allenai/led-base-16384''': 16_384,
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   LEDTokenizer
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   ["input_ids", "attention_mask"]




					def __init__(				self     :						int    ,						A     :						Any=None    ,						A     :						Optional[Any]=None    ,						A     :						List[Any]=None    ,						A     :						Optional[int]="replace"    ,						A     :						Tuple="<s>"    ,						A     :						List[str]="</s>"    ,						A     :						Optional[int]="</s>"    ,						A     :						List[Any]="<s>"    ,						A     :						Optional[Any]="<unk>"    ,						A     :						Optional[int]="<pad>"    ,						A     :						Optional[Any]="<mask>"    ,						A     :						Any=False    ,						A     :						Union[str, Any]=True    ,						**A     :						Union[str, Any]    ,						)  ->  int:
							super().__init__(
							    A    ,						A    ,						tokenizer_file=A    ,						errors=A    ,						bos_token=A    ,						eos_token=A    ,						sep_token=A    ,						cls_token=A    ,						unk_token=A    ,						pad_token=A    ,						mask_token=A    ,						add_prefix_space=A    ,						trim_offsets=A    ,						**A    ,						)

							lowercase_ : List[Any]							       =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
							if pre_tok_state.get('''add_prefix_space'''    ,						A   ) != add_prefix_space:
									lowercase_ : List[str]							       =   getattr(A    ,						pre_tok_state.pop('''type'''   )   )
									lowercase_ : int							       =   add_prefix_space
									lowercase_ : Tuple							       =   pre_tok_class(**A   )

							lowercase_ : int							       =   add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							lowercase_ : List[str]							       =   '''post_processor'''
							lowercase_ : List[Any]							       =   getattr(self.backend_tokenizer    ,						A    ,						A   )
							if tokenizer_component_instance:
									lowercase_ : Tuple							       =   json.loads(tokenizer_component_instance.__getstate__()   )

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
											lowercase_ : Dict							       =   tuple(state['''sep''']   )
									if "cls" in state:
											lowercase_ : Union[str, Any]							       =   tuple(state['''cls''']   )

									lowercase_ : Dict							       =   False

									if state.get('''add_prefix_space'''    ,						A   ) != add_prefix_space:
											lowercase_ : Union[str, Any]							       =   add_prefix_space
											lowercase_ : Union[str, Any]							       =   True

									if state.get('''trim_offsets'''    ,						A   ) != trim_offsets:
											lowercase_ : Union[str, Any]							       =   trim_offsets
											lowercase_ : str							       =   True

									if changes_to_apply:
											lowercase_ : int							       =   getattr(A    ,						state.pop('''type'''   )   )
											lowercase_ : Tuple							       =   component_class(**A   )
											setattr(self.backend_tokenizer    ,						A    ,						A   )




					@property
					# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
					def   A  (				self     :						Optional[Any]   )  ->  str:
							if self._mask_token is None:
									if self.verbose:
											logger.error('''Using mask_token, but it is not set yet.'''   )
									return None
							return str(self._mask_token   )




					@mask_token.setter
					def   A  (				self     :						Optional[int]    ,						A     :						int   )  ->  str:
							lowercase_ : str							       =   AddedToken(A    ,						lstrip=A    ,						rstrip=A   ) if isinstance(A    ,						A   ) else value
							lowercase_ : Optional[Any]							       =   value




					def   A  (				self     :						Optional[Any]    ,						*A     :						str    ,						**A     :						Optional[Any]   )  ->  BatchEncoding:
							lowercase_ : int							       =   kwargs.get('''is_split_into_words'''    ,						A   )

							if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
									    '''to use it with pretokenized inputs.'''   )

							return super()._batch_encode_plus(*A    ,						**A   )




					def   A  (				self     :						List[Any]    ,						*A     :						int    ,						**A     :						List[str]   )  ->  BatchEncoding:
							lowercase_ : str							       =   kwargs.get('''is_split_into_words'''    ,						A   )

							if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
									    '''to use it with pretokenized inputs.'''   )

							return super()._encode_plus(*A    ,						**A   )




					def   A  (				self     :						Any    ,						A     :						str    ,						A     :						Optional[str] = None   )  ->  Tuple[str]:
							lowercase_ : str							       =   self._tokenizer.model.save(A    ,						name=A   )
							return tuple(A   )




					def   A  (				self     :						Any    ,						A     :						str    ,						A     :						Union[str, Any]=None   )  ->  Any:
							lowercase_ : Any							       =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
									return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




					def   A  (				self     :						Union[str, Any]    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None   )  ->  List[int]:
							lowercase_ : Optional[Any]							       =   [self.sep_token_id]
							lowercase_ : Optional[int]							       =   [self.cls_token_id]

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






					def   A  (				self     :						Optional[Any]    ,						A     :						Union[Dict[str, EncodedInput], BatchEncoding]    ,						A     :						Optional[int] = None    ,						A     :						PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,						A     :						Optional[int] = None    ,						A     :						Optional[bool] = None    ,						)  ->  dict:
							lowercase_ : Any							       =   super()._pad(
							    encoded_inputs=A    ,						max_length=A    ,						padding_strategy=A    ,						pad_to_multiple_of=A    ,						return_attention_mask=A    ,						)

							# Load from model defaults
							if return_attention_mask is None:
									lowercase_ : Optional[Any]							       =   '''attention_mask''' in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
									lowercase_ : Optional[int]							       =   encoded_inputs[self.model_input_names[0]]
									# `global_attention_mask` need to have the same length as other (sequential) inputs.
									lowercase_ : Tuple							       =   len(encoded_inputs['''global_attention_mask''']   ) != len(A   )

									if needs_to_be_padded:
											lowercase_ : Union[str, Any]							       =   len(A   ) - len(encoded_inputs['''global_attention_mask''']   )

											if self.padding_side == "right":
													# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
													lowercase_ : Dict							       =   (
													    encoded_inputs['''global_attention_mask'''] + [-1] * difference
													)
											elif self.padding_side == "left":
													lowercase_ : str							       =   [-1] * difference + encoded_inputs[
													    '''global_attention_mask'''
													]
											else:
													raise ValueError('''Invalid padding strategy:''' + str(self.padding_side   )   )

							return encoded_inputs











"""simple docstring"""



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFResNetForImageClassification, TFResNetModel
						from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor





class 					_UpperCAmelCase       :




					def __init__(				self     :						int    ,						A     :						Tuple    ,						A     :						int=3    ,						A     :						List[str]=32    ,						A     :						Dict=3    ,						A     :						Any=10    ,						A     :						Dict=[10, 20, 30, 40]    ,						A     :						Optional[Any]=[1, 1, 2, 1]    ,						A     :						Union[str, Any]=True    ,						A     :						Optional[Any]=True    ,						A     :						Any="relu"    ,						A     :						Optional[Any]=3    ,						A     :						Tuple=None    ,						)  ->  Dict:
							lowercase_ : str							       =   parent
							lowercase_ : List[Any]							       =   batch_size
							lowercase_ : Optional[int]							       =   image_size
							lowercase_ : int							       =   num_channels
							lowercase_ : int							       =   embeddings_size
							lowercase_ : str							       =   hidden_sizes
							lowercase_ : List[str]							       =   depths
							lowercase_ : Dict							       =   is_training
							lowercase_ : int							       =   use_labels
							lowercase_ : Any							       =   hidden_act
							lowercase_ : List[Any]							       =   num_labels
							lowercase_ : Tuple							       =   scope
							lowercase_ : Optional[Any]							       =   len(A   )




					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_ : Optional[int]							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							lowercase_ : Union[str, Any]							       =   None
							if self.use_labels:
									lowercase_ : List[str]							       =   ids_tensor([self.batch_size]    ,						self.num_labels   )

							lowercase_ : Optional[int]							       =   self.get_config()

							return config, pixel_values, labels




					def   A  (				self     :						Dict   )  ->  int:
							return ResNetConfig(
							    num_channels=self.num_channels    ,						embeddings_size=self.embeddings_size    ,						hidden_sizes=self.hidden_sizes    ,						depths=self.depths    ,						hidden_act=self.hidden_act    ,						num_labels=self.num_labels    ,						image_size=self.image_size    ,						)




					def   A  (				self     :						str    ,						A     :						Tuple    ,						A     :						str    ,						A     :						str   )  ->  str:
							lowercase_ : str							       =   TFResNetModel(config=A   )
							lowercase_ : Union[str, Any]							       =   model(A   )
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape    ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,						)




					def   A  (				self     :						Any    ,						A     :						int    ,						A     :						List[Any]    ,						A     :						Optional[Any]   )  ->  Optional[Any]:
							lowercase_ : Tuple							       =   self.num_labels
							lowercase_ : Union[str, Any]							       =   TFResNetForImageClassification(A   )
							lowercase_ : Tuple							       =   model(A    ,						labels=A   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )






					def   A  (				self     :						Union[str, Any]   )  ->  Tuple:
							lowercase_ : Tuple							       =   self.prepare_config_and_inputs()
							lowercase_       ,					lowercase_       ,					lowercase_ : Dict							       =   config_and_inputs
							lowercase_ : int							       =   {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_tf
class 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   (
					    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   False
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   False
					SCREAMING_SNAKE_CASE_		:				str							    =   False
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   False
					SCREAMING_SNAKE_CASE_		:				Any							    =   False




					def   A  (				self     :						Union[str, Any]   )  ->  List[Any]:
							lowercase_ : int							       =   TFResNetModelTester(self   )
							lowercase_ : str							       =   ConfigTester(self    ,						config_class=A    ,						has_text_modality=A   )




					def   A  (				self     :						Dict   )  ->  Optional[Any]:
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()




					def   A  (				self     :						Dict   )  ->  List[Any]:
							return




					@unittest.skip(reason='''ResNet does not use inputs_embeds'''   )
					def   A  (				self     :						Any   )  ->  Any:
							pass




					@unittest.skip(reason='''ResNet does not support input and output embeddings'''   )
					def   A  (				self     :						List[str]   )  ->  Optional[Any]:
							pass




					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_       ,					lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase_ : int							       =   model_class(A   )
									lowercase_ : str							       =   inspect.signature(model.call   )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowercase_ : str							       =   [*signature.parameters.keys()]

									lowercase_ : str							       =   ['''pixel_values''']
									self.assertListEqual(arg_names[:1]    ,						A   )




					def   A  (				self     :						List[str]   )  ->  Tuple:
							lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A   )




					def   A  (				self     :						List[Any]   )  ->  List[str]:
							def check_hidden_states_output(A     :						Union[str, Any]    ,						A     :						int    ,						A     :						List[Any]   ):
									lowercase_ : int							       =   model_class(A   )
									lowercase_ : Optional[Any]							       =   model(**self._prepare_for_class(A    ,						A   )   )

									lowercase_ : Union[str, Any]							       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									lowercase_ : Any							       =   self.model_tester.num_stages
									self.assertEqual(len(A   )    ,						expected_num_stages + 1   )

									# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]   )    ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,						)

							lowercase_       ,					lowercase_ : List[Any]							       =   self.model_tester.prepare_config_and_inputs_for_common()
							lowercase_ : Union[str, Any]							       =   ['''basic''', '''bottleneck''']
							for model_class in self.all_model_classes:
									for layer_type in layers_type:
											lowercase_ : List[str]							       =   layer_type
											lowercase_ : Tuple							       =   True
											check_hidden_states_output(A    ,						A    ,						A   )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowercase_ : Optional[Any]							       =   True

											check_hidden_states_output(A    ,						A    ,						A   )




					def   A  (				self     :						Optional[int]   )  ->  Tuple:
							lowercase_ : Optional[int]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A   )






					@slow
					def   A  (				self     :						List[str]   )  ->  Optional[int]:
							for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Tuple							       =   TFResNetModel.from_pretrained(A   )
									self.assertIsNotNone(A   )



def 						lowercase    (				):
		lowercase_ : List[str]							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image






@require_tf
@require_vision
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@cached_property
					def   A  (				self     :						Any   )  ->  Optional[int]:
							return (
							    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
							    if is_vision_available()
							    else None
							)






					@slow
					def   A  (				self     :						Any   )  ->  Optional[int]:
							lowercase_ : Optional[int]							       =   TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

							lowercase_ : List[Any]							       =   self.default_image_processor
							lowercase_ : Dict							       =   prepare_img()
							lowercase_ : List[str]							       =   image_processor(images=A    ,						return_tensors='''tf'''   )

							# forward pass
							lowercase_ : Tuple							       =   model(**A   )

							# verify the logits
							lowercase_ : Optional[int]							       =   tf.TensorShape((1, 10_00)   )
							self.assertEqual(outputs.logits.shape    ,						A   )

							lowercase_ : Optional[Any]							       =   tf.constant([-11.1069, -9.7877, -8.3777]   )

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



