


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

												# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
												a__					=					(image_size // patch_size) ** 2
												a__					=					num_patches + 2
					def 			lowerCamelCase__(				self       :List[str]				) ->   int:
												a__					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

												a__					=					self.get_config()

												return config, pixel_values, labels
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   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=A_      ,initializer_range=self.initializer_range      ,encoder_stride=self.encoder_stride      ,)
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :List[Any]      ,__snake_case       :str      ,__snake_case       :Union[str, Any]				) ->   Dict:
												a__					=					TFDeiTModel(config=A_				)
												a__					=					model(A_				)
												self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)				)
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Union[str, Any]      ,__snake_case       :str      ,__snake_case       :Optional[int]				) ->   List[Any]:
												a__					=					TFDeiTForMaskedImageModeling(config=A_				)
												a__					=					model(A_				)
												self.parent.assertEqual(
												    result.reconstruction.shape      ,(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

												# test greyscale images
												a__					=					1
												a__					=					TFDeiTForMaskedImageModeling(A_				)

												a__					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
												a__					=					model(A_				)
												self.parent.assertEqual(result.reconstruction.shape      ,(self.batch_size, 1, self.image_size, self.image_size)				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :Union[str, Any]      ,__snake_case       :Dict      ,__snake_case       :int				) ->   Any:
												a__					=					self.type_sequence_label_size
												a__					=					TFDeiTForImageClassification(A_				)
												a__					=					model(A_      ,labels=A_				)
												self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)				)

												# test greyscale images
												a__					=					1
												a__					=					TFDeiTForImageClassification(A_				)

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



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







@require_tf
class 	snake_case_       (_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Any   				=      (
					    (
					        TFDeiTModel,
					        TFDeiTForImageClassification,
					        TFDeiTForImageClassificationWithTeacher,
					        TFDeiTForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCAmelCase__ :   Dict   				=      (
					    {
					        "feature-extraction": TFDeiTModel,
					        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
					    }
					    if is_tf_available()
					    else {}
					)

					UpperCAmelCase__ :   str   				=      False
					UpperCAmelCase__ :   List[Any]   				=      False
					UpperCAmelCase__ :   Optional[int]   				=      False
					UpperCAmelCase__ :   Optional[Any]   				=      False
					def 			lowerCamelCase__(				self       :Tuple				) ->   List[str]:
												a__					=					TFDeiTModelTester(self				)
												a__					=					ConfigTester(self      ,config_class=A_      ,has_text_modality=A_      ,hidden_size=37				)
					def 			lowerCamelCase__(				self       :int				) ->   Tuple:
												self.config_tester.run_common_tests()
					@unittest.skip(reason='DeiT does not use inputs_embeds'				)
					def 			lowerCamelCase__(				self       :Dict				) ->   Any:
												pass
					def 			lowerCamelCase__(				self       :Any				) ->   str:
												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__					=					model_class(A_				)
																			self.assertIsInstance(model.get_input_embeddings()      ,(tf.keras.layers.Layer)				)
																			a__					=					model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(A_      ,tf.keras.layers.Dense				)				)
					def 			lowerCamelCase__(				self       :Dict				) ->   Dict:
												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

																			a__					=					['pixel_values']
																			self.assertListEqual(arg_names[:1]      ,A_				)
					def 			lowerCamelCase__(				self       :int				) ->   List[str]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A_				)
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Tuple:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_image_modeling(*A_				)
					def 			lowerCamelCase__(				self       :int				) ->   List[str]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*A_				)
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :Union[str, Any]      ,__snake_case       :Dict      ,__snake_case       :List[Any]=False				) ->   str:
												a__					=					super()._prepare_for_class(A_      ,A_      ,return_labels=A_				)

												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 			lowerCamelCase__(				self       :List[Any]				) ->   Tuple:
												for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__					=					TFDeiTModel.from_pretrained(A_				)
																			self.assertIsNotNone(A_				)







def 							__lowercase       (			):
							a__					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
							return image







@require_tf
@require_vision
class 	snake_case_       (unittest.TestCase				):
					@cached_property
					def 			lowerCamelCase__(				self       :Any				) ->   int:
												return (
												    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'				)
												    if is_vision_available()
												    else None
												)



					@slow
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Optional[int]:
												a__					=					TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'				)

												a__					=					self.default_image_processor
												a__					=					prepare_img()
												a__					=					image_processor(images=A_      ,return_tensors='tf'				)

												# forward pass
												a__					=					model(**A_				)

												# verify the logits
												a__					=					tf.TensorShape((1, 10_00)				)
												self.assertEqual(outputs.logits.shape      ,A_				)

												a__					=					tf.constant([-1.02_66, 0.19_12, -1.28_61]				)

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








import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      MvpTokenizer
					UpperCAmelCase__ :   List[Any]   				=      MvpTokenizerFast
					UpperCAmelCase__ :   Optional[int]   				=      True
					UpperCAmelCase__ :   Tuple   				=      filter_roberta_detectors
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Union[str, Any]:
												super().setUp()
												a__					=					[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												a__					=					dict(zip(__snake_case      ,range(len(__snake_case				)				)				)				)
												a__					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												a__					=					{'unk_token': '<unk>'}

												a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['vocab_file']				)
												a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['merges_file']				)
												with open(self.vocab_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write(json.dumps(__snake_case				) + '\n'				)
												with open(self.merges_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write('\n'.join(__snake_case				)				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Tuple				) ->   int:
												kwargs.update(self.special_tokens_map				)
												return self.tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Dict				) ->   Dict:
												kwargs.update(self.special_tokens_map				)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Union[str, Any]				) ->   List[Any]:
												return "lower newer", "lower newer"
					@cached_property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return MvpTokenizer.from_pretrained('RUCAIBox/mvp'				)
					@cached_property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'				)
					@require_torch
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												a__					=					[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,max_length=len(__snake_case				)      ,padding=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)

																			self.assertEqual((2, 9)      ,batch.input_ids.shape				)
																			self.assertEqual((2, 9)      ,batch.attention_mask.shape				)
																			a__					=					batch.input_ids.tolist()[0]
																			self.assertListEqual(__snake_case      ,__snake_case				)
																			# Test that special tokens are reset
					@require_torch
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[Any]:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,padding=__snake_case      ,return_tensors='pt'				)
																			# check if input_ids are returned and no labels
																			self.assertIn('input_ids'      ,__snake_case				)
																			self.assertIn('attention_mask'      ,__snake_case				)
																			self.assertNotIn('labels'      ,__snake_case				)
																			self.assertNotIn('decoder_attention_mask'      ,__snake_case				)
					@require_torch
					def 			lowerCamelCase__(				self       :Any				) ->   int:
												a__					=					[
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(text_target=__snake_case      ,max_length=32      ,padding='max_length'      ,return_tensors='pt'				)
																			self.assertEqual(32      ,targets['input_ids'].shape[1]				)
					@require_torch
					def 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(
																			    ['I am a small frog' * 10_24, 'I am a small frog']      ,padding=__snake_case      ,truncation=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)
																			self.assertEqual(batch.input_ids.shape      ,(2, 10_24)				)
					@require_torch
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Any:
												a__					=					['A long paragraph for summarization.']
												a__					=					[
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,text_target=__snake_case      ,return_tensors='pt'				)
																			a__					=					inputs['input_ids']
																			a__					=					inputs['labels']
																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Tuple:
												pass



					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[Any]:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																										a__					=					self.rust_tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					self.tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					'A, <mask> AllenNLP sentence.'
																										a__					=					tokenizer_r.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)
																										a__					=					tokenizer_p.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)

																										# token_type_ids should put 0 everywhere
																										self.assertEqual(sum(tokens_r['token_type_ids']				)      ,sum(tokens_p['token_type_ids']				)				)

																										# attention_mask should put 1 everywhere, so sum over length should be 1
																										self.assertEqual(
																										    sum(tokens_r['attention_mask']				) / len(tokens_r['attention_mask']				)      ,sum(tokens_p['attention_mask']				) / len(tokens_p['attention_mask']				)      ,)

																										a__					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
																										a__					=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']				)

																										# Rust correctly handles the space before the mask while python doesnt
																										self.assertSequenceEqual(tokens_p['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)
																										self.assertSequenceEqual(tokens_r['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)

																										self.assertSequenceEqual(
																										    __snake_case      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)
																										self.assertSequenceEqual(
																										    __snake_case      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)




