

'''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 	lowerCAmelCase  :







       def __init__(				self      :			Any    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			List[Any]=13    ,   __lowercase      :			List[str]=30    ,   __lowercase      :			Union[str, Any]=2    ,   __lowercase      :			Optional[Any]=3    ,   __lowercase      :			Any=True    ,   __lowercase      :			int=True    ,   __lowercase      :			int=32    ,   __lowercase      :			List[str]=2    ,   __lowercase      :			str=4    ,   __lowercase      :			Any=37    ,   __lowercase      :			Union[str, Any]="gelu"    ,   __lowercase      :			Dict=0.1    ,   __lowercase      :			Any=0.1    ,   __lowercase      :			Optional[int]=10    ,   __lowercase      :			Optional[int]=0.0_2    ,   __lowercase      :			int=3    ,   __lowercase      :			str=None    ,   __lowercase      :			Optional[Any]=2    ,   ):







            """simple docstring"""







            __lowercase      		=parent
            __lowercase      		=batch_size
            __lowercase      		=image_size
            __lowercase      		=patch_size
            __lowercase      		=num_channels
            __lowercase      		=is_training
            __lowercase      		=use_labels
            __lowercase      		=hidden_size
            __lowercase      		=num_hidden_layers
            __lowercase      		=num_attention_heads
            __lowercase      		=intermediate_size
            __lowercase      		=hidden_act
            __lowercase      		=hidden_dropout_prob
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=type_sequence_label_size
            __lowercase      		=initializer_range
            __lowercase      		=scope
            __lowercase      		=encoder_stride

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







       def       snake_case (				self      :			str   ):







            """simple docstring"""







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

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

            __lowercase      		=self.get_config()

            return config, pixel_values, labels







       def       snake_case (				self      :			str   ):







            """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=SCREAMING_SNAKE_CASE__    ,   initializer_range=self.initializer_range    ,   encoder_stride=self.encoder_stride    ,   )







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			str    ,   __lowercase      :			Any   ):







            """simple docstring"""







            __lowercase      		=TFDeiTModel(config=SCREAMING_SNAKE_CASE__   )
            __lowercase      		=model(SCREAMING_SNAKE_CASE__   )
            self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)   )







       def       snake_case (				self      :			Dict    ,   __lowercase      :			List[str]    ,   __lowercase      :			Any    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__   )
            __lowercase      		=model(SCREAMING_SNAKE_CASE__   )
            self.parent.assertEqual(
                result.reconstruction.shape    ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

            # test greyscale images
            __lowercase      		=1
            __lowercase      		=TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE__   )

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







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=self.type_sequence_label_size
            __lowercase      		=TFDeiTForImageClassification(SCREAMING_SNAKE_CASE__   )
            __lowercase      		=model(SCREAMING_SNAKE_CASE__    ,   labels=SCREAMING_SNAKE_CASE__   )
            self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)   )

            # test greyscale images
            __lowercase      		=1
            __lowercase      		=TFDeiTForImageClassification(SCREAMING_SNAKE_CASE__   )

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







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=self.prepare_config_and_inputs()
            __lowercase		,  __lowercase		,  __lowercase      		=config_and_inputs
            __lowercase      		={'pixel_values': pixel_values}
            return config, inputs_dict






@require_tf
class 	lowerCAmelCase  (   _lowerCAmelCase ,      _lowerCAmelCase ,      unittest.TestCase ):
       lowerCAmelCase_												=			(
           (
               TFDeiTModel,
               TFDeiTForImageClassification,
               TFDeiTForImageClassificationWithTeacher,
               TFDeiTForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       lowerCAmelCase_												=			(
           {
               "feature-extraction": TFDeiTModel,
               "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
           }
           if is_tf_available()
           else {}
       )

       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			False







       def       snake_case (				self      :			List[str]   ):







            """simple docstring"""







            __lowercase      		=TFDeiTModelTester(self   )
            __lowercase      		=ConfigTester(self    ,   config_class=SCREAMING_SNAKE_CASE__    ,   has_text_modality=SCREAMING_SNAKE_CASE__    ,   hidden_size=37   )







       def       snake_case (				self      :			List[str]   ):







            """simple docstring"""







            self.config_tester.run_common_tests()







       @unittest.skip(reason='DeiT does not use inputs_embeds'   )
       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            pass







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase		,  __lowercase      		=self.model_tester.prepare_config_and_inputs_for_common()

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







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            __lowercase		,  __lowercase      		=self.model_tester.prepare_config_and_inputs_for_common()

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

                 __lowercase      		=['pixel_values']
                 self.assertListEqual(arg_names[:1]    ,   SCREAMING_SNAKE_CASE__   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__   )







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__   )







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			Dict    ,   __lowercase      :			List[str]    ,   __lowercase      :			Tuple=False   ):







            """simple docstring"""







            __lowercase      		=super()._prepare_for_class(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   return_labels=SCREAMING_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       snake_case (				self      :			str   ):







            """simple docstring"""







            for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase      		=TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )






def 					__UpperCamelCase				(						):






     '''simple docstring'''





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







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







       @cached_property
       def       snake_case (				self      :			Any   ):







            """simple docstring"""







            return (
                DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'   )
                if is_vision_available()
                else None
            )







       @slow
       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'   )

            __lowercase      		=self.default_image_processor
            __lowercase      		=prepare_img()
            __lowercase      		=image_processor(images=SCREAMING_SNAKE_CASE__    ,   return_tensors='tf'   )

            # forward pass
            __lowercase      		=model(**SCREAMING_SNAKE_CASE__   )

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

            __lowercase      		=tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]   )

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







"""simple docstring"""







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

				A__											=					BlipImageProcessor()
				A__											=					GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"		)

				A__											=					BlipaProcessor(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				processor.save_pretrained(self.tmpdirname		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).tokenizer


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).image_processor


			def   snake_case__	(      self		)      ->	str:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]

				A__											=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1		)		) for x in image_inputs]

				return image_inputs


			def   snake_case__	(      self		)      ->	str:
				A__											=					BlipaProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

				A__											=					self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"		)
				A__											=					self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				A__											=					BlipaProcessor.from_pretrained(
				    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()		)
				self.assertIsInstance(processor.tokenizer						,	SCREAMING_SNAKE_CASE__		)

				self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string()		)
				self.assertIsInstance(processor.image_processor						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)

				for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2		)


			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	return_token_type_ids=SCREAMING_SNAKE_CASE__		)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]		)


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(list(inputs.keys()		)						,	["pixel_values", "input_ids", "attention_mask"]		)

				# test if it raises when no input is passed
				with pytest.raises(SCREAMING_SNAKE_CASE__		):
					processor()


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
				self.assertListEqual(list(inputs.keys()		)						,	["pixel_values", "input_ids", "attention_mask"]		)




