
import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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 torch import nn

					from transformers import (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    DeiTForImageClassification,
					    DeiTForImageClassificationWithTeacher,
					    DeiTForMaskedImageModeling,
					    DeiTModel,
					)
					from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor


class        lowercase      :



		def __init__(				self       ,   A_       ,   A_=13       ,   A_=30       ,   A_=2       ,   A_=3       ,   A_=True       ,   A_=True       ,   A_=32       ,   A_=5       ,   A_=4       ,   A_=37       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=10       ,   A_=0.02       ,   A_=3       ,   A_=None       ,   A_=2       ,   )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  image_size
				UpperCamelCase						=  patch_size
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  is_training
				UpperCamelCase						=  use_labels
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  intermediate_size
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  hidden_dropout_prob
				UpperCamelCase						=  attention_probs_dropout_prob
				UpperCamelCase						=  type_sequence_label_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  scope
				UpperCamelCase						=  encoder_stride

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



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

				UpperCamelCase						=  self.get_config()

				return config, pixel_values, labels



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				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=lowercase_       ,   initializer_range=self.initializer_range       ,   encoder_stride=self.encoder_stride       ,   )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  DeiTModel(config=lowercase_ )
				model.to(lowercase_ )
				model.eval()
				UpperCamelCase						=  model(lowercase_ )
				self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  DeiTForMaskedImageModeling(config=lowercase_ )
				model.to(lowercase_ )
				model.eval()
				UpperCamelCase						=  model(lowercase_ )
				self.parent.assertEqual(
				    result.reconstruction.shape       ,   (self.batch_size, self.num_channels, self.image_size, self.image_size) )

				# test greyscale images
				UpperCamelCase						=  1
				UpperCamelCase						=  DeiTForMaskedImageModeling(lowercase_ )
				model.to(lowercase_ )
				model.eval()

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



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.type_sequence_label_size
				UpperCamelCase						=  DeiTForImageClassification(lowercase_ )
				model.to(lowercase_ )
				model.eval()
				UpperCamelCase						=  model(lowercase_       ,   labels=lowercase_ )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.type_sequence_label_size) )

				# test greyscale images
				UpperCamelCase						=  1
				UpperCamelCase						=  DeiTForImageClassification(lowercase_ )
				model.to(lowercase_ )
				model.eval()

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



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				(
				    UpperCamelCase 
				)						=  config_and_inputs
				UpperCamelCase						=  {"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class        lowercase      (	snake_case__						,     snake_case__						,     unittest.TestCase     ):
		__lowercase : Dict			  =       (
		    (
		        DeiTModel,
		        DeiTForImageClassification,
		        DeiTForImageClassificationWithTeacher,
		        DeiTForMaskedImageModeling,
		    )
		    if is_torch_available()
		    else ()
		)
		__lowercase : Any			  =       (
		    {
		        "feature-extraction": DeiTModel,
		        "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
		    }
		    if is_torch_available()
		    else {}
		)

		__lowercase : Optional[Any]			  =       False
		__lowercase : Optional[int]			  =       False
		__lowercase : List[str]			  =       False



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  DeiTModelTester(self )
				UpperCamelCase						=  ConfigTester(self       ,   config_class=lowercase_       ,   has_text_modality=lowercase_       ,   hidden_size=37 )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		@unittest.skip(reason='DeiT does not use inputs_embeds' )
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase						=  model_class(lowercase_ )
						self.assertIsInstance(model.get_input_embeddings()       ,   (nn.Module) )
						UpperCamelCase						=  model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(lowercase_       ,   nn.Linear ) )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase						=  model_class(lowercase_ )
						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]       ,   lowercase_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_=False )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  super()._prepare_for_class(lowercase_       ,   lowercase_       ,   return_labels=lowercase_ )

				if return_labels:
						if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
								del inputs_dict["labels"]

				return inputs_dict



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				if not self.model_tester.is_training:
						return

				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase						=  True

				for model_class in self.all_model_classes:
						# DeiTForImageClassificationWithTeacher supports inference-only
						if (
						    model_class in get_values(lowercase_ )
						    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
						):
								continue
						UpperCamelCase						=  model_class(lowercase_ )
						model.to(lowercase_ )
						model.train()
						UpperCamelCase						=  self._prepare_for_class(lowercase_       ,   lowercase_       ,   return_labels=lowercase_ )
						UpperCamelCase						=  model(**lowercase_ ).loss
						loss.backward()



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
						return

				UpperCamelCase						=  False
				UpperCamelCase						=  True

				for model_class in self.all_model_classes:
						if model_class in get_values(lowercase_ ) or not model_class.supports_gradient_checkpointing:
								continue
						# DeiTForImageClassificationWithTeacher supports inference-only
						if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
								continue
						UpperCamelCase						=  model_class(lowercase_ )
						model.gradient_checkpointing_enable()
						model.to(lowercase_ )
						model.train()
						UpperCamelCase						=  self._prepare_for_class(lowercase_       ,   lowercase_       ,   return_labels=lowercase_ )
						UpperCamelCase						=  model(**lowercase_ ).loss
						loss.backward()



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				UpperCamelCase						=  [
				    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
				    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
				    {"title": "regression", "num_labels": 1, "dtype": torch.float},
				]

				for model_class in self.all_model_classes:
						if (
						    model_class
						    not in [
						        *get_values(lowercase_ ),
						        *get_values(lowercase_ ),
						    ]
						    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
						):
								continue

						for problem_type in problem_types:
								with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}''' ):
										UpperCamelCase						=  problem_type["title"]
										UpperCamelCase						=  problem_type["num_labels"]

										UpperCamelCase						=  model_class(lowercase_ )
										model.to(lowercase_ )
										model.train()

										UpperCamelCase						=  self._prepare_for_class(lowercase_       ,   lowercase_       ,   return_labels=lowercase_ )

										if problem_type["num_labels"] > 1:
												UpperCamelCase						=  inputs["labels"].unsqueeze(1 ).repeat(1       ,   problem_type['num_labels'] )

										UpperCamelCase						=  inputs["labels"].to(problem_type['dtype'] )

										# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
										# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
										# they have the same size." which is a symptom something in wrong for the regression problem.
										# See https://github.com/huggingface/transformers/issues/11780
										with warnings.catch_warnings(record=lowercase_ ) as warning_list:
												UpperCamelCase						=  model(**lowercase_ ).loss
										for w in warning_list:
												if "Using a target size that is different to the input size" in str(w.message ):
														raise ValueError(
														    F'''Something is going wrong in the regression problem: intercepted {w.message}''' )

										loss.backward()



		@slow
		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase						=  DeiTModel.from_pretrained(lowercase_ )
						self.assertIsNotNone(lowercase_ )






def    A (      )     ->						Any:
		'''simple docstring'''

		UpperCamelCase						=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
		return image


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



		@cached_property
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				return (
				    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
				    if is_vision_available()
				    else None
				)



		@slow
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' ).to(
				    lowercase_ )

				UpperCamelCase						=  self.default_image_processor
				UpperCamelCase						=  prepare_img()
				UpperCamelCase						=  image_processor(images=lowercase_       ,   return_tensors='pt' ).to(lowercase_ )

				# forward pass
				with torch.no_grad():
						UpperCamelCase						=  model(**lowercase_ )

				# verify the logits
				UpperCamelCase						=  torch.Size((1, 1_000) )
				self.assertEqual(outputs.logits.shape       ,   lowercase_ )

				UpperCamelCase						=  torch.tensor([-1.0266, 0.1912, -1.2861] ).to(lowercase_ )

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



		@slow
		@require_accelerate
		@require_torch_gpu
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  DeiTModel.from_pretrained(
				    'facebook/deit-base-distilled-patch16-224'       ,   torch_dtype=torch.floataa       ,   device_map='auto' )
				UpperCamelCase						=  self.default_image_processor

				UpperCamelCase						=  prepare_img()
				UpperCamelCase						=  image_processor(images=lowercase_       ,   return_tensors='pt' )
				UpperCamelCase						=  inputs.pixel_values.to(lowercase_ )

				# forward pass to make sure inference works in fp16
				with torch.no_grad():
						UpperCamelCase						=  model(lowercase_ )





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

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


if is_sentencepiece_available():
					from .tokenization_camembert import CamembertTokenizer
else:
					_UpperCAmelCase      :				List[str]          = None


_UpperCAmelCase      :				Any          = logging.get_logger(__name__)

_UpperCAmelCase      :				Tuple          = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_UpperCAmelCase      :				List[str]          = {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

_UpperCAmelCase      :				Optional[int]          = {
    "camembert-base": 512,
}

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


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : str			  =       VOCAB_FILES_NAMES
		__lowercase : Optional[Any]			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : List[str]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : List[str]			  =       ["input_ids", "attention_mask"]
		__lowercase : Tuple			  =       CamembertTokenizer



		def __init__(				self       ,   A_=None       ,   A_=None       ,   A_="<s>"       ,   A_="</s>"       ,   A_="</s>"       ,   A_="<s>"       ,   A_="<unk>"       ,   A_="<pad>"       ,   A_="<mask>"       ,   A_=["<s>NOTUSED", "</s>NOTUSED"]       ,   **A_       ,   )						->      List[Any]:
				"""simple docstring"""
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else mask_token

				super().__init__(
				    A_       ,   tokenizer_file=A_       ,   bos_token=A_       ,   eos_token=A_       ,   sep_token=A_       ,   cls_token=A_       ,   unk_token=A_       ,   pad_token=A_       ,   mask_token=A_       ,   additional_special_tokens=A_       ,   **A_       ,   )

				UpperCamelCase						=  vocab_file
				UpperCamelCase						=  False if not self.vocab_file else True



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""

				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase						=  [self.cls_token_id]
				UpperCamelCase						=  [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""
				UpperCamelCase						=  [self.sep_token_id]
				UpperCamelCase						=  [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 __UpperCamelCase				(				self       ,   A_       ,   A_ = 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(A_ ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
						return
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

				if os.path.abspath(self.vocab_file ) != os.path.abspath(A_ ):
						copyfile(self.vocab_file       ,   A_ )

				return (out_vocab_file,)
