




'''simple docstring'''


from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
				import tensorflow as tf

				from transformers import TFBlipTextModel
				from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class      A       :







							def __init__(    self			:    Any				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[Any]=12				,     lowerCAmelCase_			:    str=7				,     lowerCAmelCase_			:    Tuple=True				,     lowerCAmelCase_			:    List[str]=True				,     lowerCAmelCase_			:    Tuple=True				,     lowerCAmelCase_			:    Dict=99				,     lowerCAmelCase_			:    Tuple=32				,     lowerCAmelCase_			:    Dict=32				,     lowerCAmelCase_			:    int=2				,     lowerCAmelCase_			:    str=4				,     lowerCAmelCase_			:    List[str]=37				,     lowerCAmelCase_			:    List[str]=0.1				,     lowerCAmelCase_			:    Union[str, Any]=0.1				,     lowerCAmelCase_			:    List[Any]=5_12				,     lowerCAmelCase_			:    Dict=0.0_2				,     lowerCAmelCase_			:    str=0				,     lowerCAmelCase_			:    Dict=None				,     )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							parent
														_a      	=							batch_size
														_a      	=							seq_length
														_a      	=							is_training
														_a      	=							use_input_mask
														_a      	=							use_labels
														_a      	=							vocab_size
														_a      	=							hidden_size
														_a      	=							projection_dim
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							intermediate_size
														_a      	=							dropout
														_a      	=							attention_dropout
														_a      	=							max_position_embeddings
														_a      	=							initializer_range
														_a      	=							scope
														_a      	=							bos_token_id







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size   )

														_a      	=							None
														if self.use_input_mask:
																					_a      	=							random_attention_mask([self.batch_size, self.seq_length]   )

														if input_mask is not None:
																					_a      	=							input_mask.numpy()
																					_a	,				_a      	=							input_mask.shape
																					_a      	=							np.random.randint(1				,     seq_length - 1				,     size=(batch_size,)   )
																					for batch_idx, start_index in enumerate(lowerCAmelCase_   ):
																												_a      	=							1
																												_a      	=							0

														_a      	=							self.get_config()

														return config, input_ids, tf.convert_to_tensor(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


														return BlipTextConfig(
														    vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     projection_dim=self.projection_dim				,     num_hidden_layers=self.num_hidden_layers				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     dropout=self.dropout				,     attention_dropout=self.attention_dropout				,     max_position_embeddings=self.max_position_embeddings				,     initializer_range=self.initializer_range				,     bos_token_id=self.bos_token_id				,     )







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							TFBlipTextModel(config=lowerCAmelCase_   )
														_a      	=							model(lowerCAmelCase_				,     attention_mask=lowerCAmelCase_				,     training=lowerCAmelCase_   )
														_a      	=							model(lowerCAmelCase_				,     training=lowerCAmelCase_   )
														self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)   )
														self.parent.assertEqual(result.pooler_output.shape				,     (self.batch_size, self.hidden_size)   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							self.prepare_config_and_inputs()
														_a	,				_a	,				_a      	=							config_and_inputs
														_a      	=							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
														return config, inputs_dict



@require_tf
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							(TFBlipTextModel,) if is_tf_available() else ()
							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							False







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														_a      	=							BlipTextModelTester(self   )
														_a      	=							ConfigTester(self				,     config_class=lowerCAmelCase_				,     hidden_size=37   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				str:


														"""simple docstring"""


														self.config_tester.run_common_tests()







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Dict:


														"""simple docstring"""


														pass







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				List[str]:


														"""simple docstring"""


														pass







							@unittest.skip(reason='''Blip does not use inputs_embeds'''   )
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Any:


														"""simple docstring"""


														pass







							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''   )
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Optional[Any]:


														"""simple docstring"""


														pass







							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''   )
							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														pass







							@slow
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Dict:


														"""simple docstring"""


														for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_a      	=							TFBlipTextModel.from_pretrained(lowerCAmelCase_   )
																					self.assertIsNotNone(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Union[str, Any]=True   )     ->				str:


														"""simple docstring"""


														super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCAmelCase_   )







'''simple docstring'''


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor

class      A       (						unittest.TestCase       ):







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[str]=7				,     lowerCAmelCase_			:    Dict=3				,     lowerCAmelCase_			:    List[Any]=18				,     lowerCAmelCase_			:    Any=30				,     lowerCAmelCase_			:    Optional[int]=4_00				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    List[str]=True				,     )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							size if size is not None else {'''height''': 18, '''width''': 18}
														_a      	=							parent
														_a      	=							batch_size
														_a      	=							num_channels
														_a      	=							image_size
														_a      	=							min_resolution
														_a      	=							max_resolution
														_a      	=							do_resize
														_a      	=							size
														_a      	=							do_normalize







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
														            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
														        ]   ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}



@require_torch
@require_vision
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							ImageGPTImageProcessor if is_vision_available() else None







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessingTester(self   )







							@property
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''clusters'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_resize'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''size'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_normalize'''   )   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict   )
														self.assertEqual(image_processor.size				,     {'''height''': 18, '''width''': 18}   )

														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42   )
														self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														_a      	=							json.loads(image_processor.to_json_string()   )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     obj[key]   )   )
																					else:
																												self.assertEqual(obj[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							os.path.join(lowerCAmelCase_				,     '''image_processor.json'''   )
																					image_processor_first.to_json_file(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_json_file(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_pretrained(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							@unittest.skip('''ImageGPT requires clusters at initialization'''   )
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														pass



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							load_dataset('''hf-internal-testing/fixtures_image_utils'''					,		split='''test''' )

							_a      	=							Image.open(dataset[4]['''file'''] )
							_a      	=							Image.open(dataset[5]['''file'''] )

							_a      	=							[imagea, imagea]

							return images

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







							@slow
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''   )

														_a      	=							prepare_images()

														# test non-batched
														_a      	=							image_processing(images[0]				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (1, 10_24)   )

														_a      	=							[3_06, 1_91, 1_91]
														self.assertEqual(encoding.input_ids[0, :3].tolist()				,     lowerCAmelCase_   )

														# test batched
														_a      	=							image_processing(lowerCAmelCase_				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (2, 10_24)   )

														_a      	=							[3_03, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()				,     lowerCAmelCase_   )

