"""simple docstring"""




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase    :							Any													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Union[str, Any]													=						"▁"

__lowerCAmelCase    :							List[str]													=						{"vocab_file": "prophetnet.tokenizer"}

__lowerCAmelCase    :							int													=						{
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__lowerCAmelCase    :							Optional[int]													=						{
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__lowerCAmelCase    :							int													=						{
    "microsoft/xprophetnet-large-wiki100-cased": 5_12,
}







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   collections.OrderedDict()
		with open(UpperCamelCase__			,  """r"""			,  encoding="""utf-8"""     ) as reader:
				lowerCAmelCase__        =   reader.readlines()
		for index, token in enumerate(UpperCamelCase__     ):
				lowerCAmelCase__        =   token.rstrip("""\n"""     )
				lowerCAmelCase__        =   index
		return vocab






class       a_      (     UpperCAmelCase_			):
					UpperCamelCase_ :	List[str]			=    VOCAB_FILES_NAMES
					UpperCamelCase_ :	Union[str, Any]			=    PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase_ :	List[Any]			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase_ :	Optional[int]			=    ["input_ids", "attention_mask"]




					def __init__(	self						:     List[str]  ,						snake_case__						:     Dict  ,						snake_case__						:     Union[str, Any]="[SEP]"  ,						snake_case__						:     Dict="[SEP]"  ,						snake_case__						:     int="[SEP]"  ,						snake_case__						:     List[Any]="[UNK]"  ,						snake_case__						:     List[Any]="[PAD]"  ,						snake_case__						:     Optional[Any]="[CLS]"  ,						snake_case__						:     Union[str, Any]="[MASK]"  ,						snake_case__						:     Optional[Dict[str, Any]] = None  ,						**snake_case__						:     List[Any]  ,						):
							lowerCAmelCase__        =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=_lowercase  ,						eos_token=_lowercase  ,						sep_token=_lowercase  ,						unk_token=_lowercase  ,						pad_token=_lowercase  ,						cls_token=_lowercase  ,						mask_token=_lowercase  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**_lowercase  ,						)

							try:
									import sentencepiece as spm
							except ImportError:
									logger.warning(
									    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
									    """ pip install sentencepiece"""     )
									raise

							lowerCAmelCase__        =   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
							self.sp_model.Load(str(_lowercase     )     )
							lowerCAmelCase__        =   vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# put special tokens and [unused] tokens into the vocab
							lowerCAmelCase__        =   {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

							for i in range(10     ):
									lowerCAmelCase__        =   F"""[unused{i}]"""
									lowerCAmelCase__        =   5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							lowerCAmelCase__        =   12
							lowerCAmelCase__        =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							for k in self.fairseq_tokens_to_ids.keys():
									self.unique_no_split_tokens.append(_lowercase     )




					def __getstate__(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.__dict__.copy()
							lowerCAmelCase__        =   None
							return state




					def __setstate__(	self						:     Optional[int]  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   d
							try:
									import sentencepiece as spm
							except ImportError:
									logger.warning(
									    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
									    """ pip install sentencepiece"""     )
									raise

							# for backward compatibility
							if not hasattr(self  ,						"""sp_model_kwargs"""     ):
									lowerCAmelCase__        =   {}

							lowerCAmelCase__        =   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
							self.sp_model.Load(self.vocab_file     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     List[int]  ,						snake_case__						:     Optional[List[int]] = None  ,						snake_case__						:     bool = False     ):

							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_lowercase  ,						token_ids_a=_lowercase  ,						already_has_special_tokens=_lowercase     )

							if token_ids_a is None:
									return ([0] * len(_lowercase     )) + [1]
							return ([0] * len(_lowercase     )) + [1] + ([0] * len(_lowercase     )) + [1]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     List[int]  ,						snake_case__						:     Optional[List[int]] = None     ):
							lowerCAmelCase__        =   [self.sep_token_id]

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




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							return len(self.sp_model     ) + self.fairseq_offset




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   {self.convert_ids_to_tokens(_lowercase     ): i for i in range(self.vocab_size     )}
							vocab.update(self.added_tokens_encoder     )
							return vocab




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     str     ):
							return self.sp_model.encode(_lowercase  ,						out_type=_lowercase     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[str]     ):
							if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
							lowerCAmelCase__        =   self.sp_model.PieceToId(_lowercase     )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     str     ):
							if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   """""".join(_lowercase     ).replace(_lowercase  ,						""" """     ).strip()
							return out_string




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     str  ,						snake_case__						:     Optional[str] = None     ):
							if not os.path.isdir(_lowercase     ):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
									return
							lowerCAmelCase__        =   os.path.join(
							    _lowercase  ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

							if os.path.abspath(self.vocab_file     ) != os.path.abspath(_lowercase     ) and os.path.isfile(self.vocab_file     ):
									copyfile(self.vocab_file  ,						_lowercase     )
							elif not os.path.isfile(self.vocab_file     ):
									with open(_lowercase  ,						"""wb"""     ) as fi:
											lowerCAmelCase__        =   self.sp_model.serialized_model_proto()
											fi.write(_lowercase     )

							return (out_vocab_file,)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     List[int]  ,						snake_case__						:     Optional[List[int]] = None     ):

							if token_ids_a is None:
									return token_ids_a + [self.sep_token_id]
							lowerCAmelCase__        =   [self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep





"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
			    TFDeiTForImageClassification,
			    TFDeiTForImageClassificationWithTeacher,
			    TFDeiTForMaskedImageModeling,
			    TFDeiTModel,
			)
			from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DeiTImageProcessor

class       a_      :




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Any=13  ,						snake_case__						:     int=30  ,						snake_case__						:     int=2  ,						snake_case__						:     Union[str, Any]=3  ,						snake_case__						:     Dict=True  ,						snake_case__						:     Optional[int]=True  ,						snake_case__						:     List[Any]=32  ,						snake_case__						:     List[str]=2  ,						snake_case__						:     Optional[Any]=4  ,						snake_case__						:     Optional[int]=37  ,						snake_case__						:     Tuple="gelu"  ,						snake_case__						:     str=0.1  ,						snake_case__						:     Any=0.1  ,						snake_case__						:     int=10  ,						snake_case__						:     Dict=0.02  ,						snake_case__						:     Union[str, Any]=3  ,						snake_case__						:     str=None  ,						snake_case__						:     List[Any]=2  ,						):
							lowerCAmelCase__        =   parent
							lowerCAmelCase__        =   batch_size
							lowerCAmelCase__        =   image_size
							lowerCAmelCase__        =   patch_size
							lowerCAmelCase__        =   num_channels
							lowerCAmelCase__        =   is_training
							lowerCAmelCase__        =   use_labels
							lowerCAmelCase__        =   hidden_size
							lowerCAmelCase__        =   num_hidden_layers
							lowerCAmelCase__        =   num_attention_heads
							lowerCAmelCase__        =   intermediate_size
							lowerCAmelCase__        =   hidden_act
							lowerCAmelCase__        =   hidden_dropout_prob
							lowerCAmelCase__        =   attention_probs_dropout_prob
							lowerCAmelCase__        =   type_sequence_label_size
							lowerCAmelCase__        =   initializer_range
							lowerCAmelCase__        =   scope
							lowerCAmelCase__        =   encoder_stride

							# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
							lowerCAmelCase__        =   (image_size // patch_size) ** 2
							lowerCAmelCase__        =   num_patches + 2




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

							lowerCAmelCase__        =   None
							if self.use_labels:
									lowerCAmelCase__        =   ids_tensor([self.batch_size]  ,						self.type_sequence_label_size     )

							lowerCAmelCase__        =   self.get_config()

							return config, pixel_values, labels




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							return DeiTConfig(
							    image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						intermediate_size=self.intermediate_size  ,						hidden_act=self.hidden_act  ,						hidden_dropout_prob=self.hidden_dropout_prob  ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,						is_decoder=snake_case__  ,						initializer_range=self.initializer_range  ,						encoder_stride=self.encoder_stride  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     int  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     List[str]     ):
							lowerCAmelCase__        =   TFDeiTModel(config=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   TFDeiTForMaskedImageModeling(config=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(
							    result.reconstruction.shape  ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

							# test greyscale images
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   TFDeiTForMaskedImageModeling(snake_case__     )

							lowerCAmelCase__        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.reconstruction.shape  ,						(self.batch_size, 1, self.image_size, self.image_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   self.type_sequence_label_size
							lowerCAmelCase__        =   TFDeiTForImageClassification(snake_case__     )
							lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)     )

							# test greyscale images
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   TFDeiTForImageClassification(snake_case__     )

							lowerCAmelCase__        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   config_and_inputs
							lowerCAmelCase__        =   {"""pixel_values""": pixel_values}
							return config, inputs_dict

@require_tf
class       a_      (     __UpperCamelCase ,		__UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Optional[Any]			=    (
					    (
					        TFDeiTModel,
					        TFDeiTForImageClassification,
					        TFDeiTForImageClassificationWithTeacher,
					        TFDeiTForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCamelCase_ :	Any			=    (
					    {
					        "feature-extraction": TFDeiTModel,
					        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
					    }
					    if is_tf_available()
					    else {}
					)

					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[int]			=    False
					UpperCamelCase_ :	int			=    False




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   TFDeiTModelTester(self     )
							lowerCAmelCase__        =   ConfigTester(self  ,						config_class=snake_case__  ,						has_text_modality=snake_case__  ,						hidden_size=37     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							self.config_tester.run_common_tests()




					@unittest.skip(reason="""DeiT does not use inputs_embeds"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )
									self.assertIsInstance(model.get_input_embeddings()  ,						(tf.keras.layers.Layer)     )
									lowerCAmelCase__        =   model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case__  ,						tf.keras.layers.Dense     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     int  ,						snake_case__						:     Union[str, Any]=False     ):
							lowerCAmelCase__        =   super()._prepare_for_class(snake_case__  ,						snake_case__  ,						return_labels=snake_case__     )

							if return_labels:
									if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call     ).parameters:
											del inputs_dict["labels"]

							return inputs_dict




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase__        =   TFDeiTModel.from_pretrained(snake_case__     )
									self.assertIsNotNone(snake_case__     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
		return image






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




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							return (
							    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     )
							    if is_vision_available()
							    else None
							)




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     )

							lowerCAmelCase__        =   self.default_image_processor
							lowerCAmelCase__        =   prepare_img()
							lowerCAmelCase__        =   image_processor(images=snake_case__  ,						return_tensors="""tf"""     )

							# forward pass
							lowerCAmelCase__        =   model(**snake_case__     )

							# verify the logits
							lowerCAmelCase__        =   tf.TensorShape((1, 1000)     )
							self.assertEqual(outputs.logits.shape  ,						snake_case__     )

							lowerCAmelCase__        =   tf.constant([-1.0266, 0.1912, -1.2861]     )

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




