

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						,					A_						,					A_=12						,					A_=7						,					A_=True						,					A_=True						,					A_=True						,					A_=99						,					A_=32						,					A_=32						,					A_=2						,					A_=4						,					A_=37						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=0.02						,					A_=0						,					A_=None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    parent
										_UpperCAmelCase       :		int 		=    batch_size
										_UpperCAmelCase       :		List[str] 		=    seq_length
										_UpperCAmelCase       :		Union[str, Any] 		=    is_training
										_UpperCAmelCase       :		List[str] 		=    use_input_mask
										_UpperCAmelCase       :		Optional[Any] 		=    use_labels
										_UpperCAmelCase       :		Dict 		=    vocab_size
										_UpperCAmelCase       :		Optional[int] 		=    hidden_size
										_UpperCAmelCase       :		int 		=    projection_dim
										_UpperCAmelCase       :		str 		=    num_hidden_layers
										_UpperCAmelCase       :		str 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    intermediate_size
										_UpperCAmelCase       :		Dict 		=    dropout
										_UpperCAmelCase       :		int 		=    attention_dropout
										_UpperCAmelCase       :		Any 		=    max_position_embeddings
										_UpperCAmelCase       :		List[Any] 		=    initializer_range
										_UpperCAmelCase       :		List[str] 		=    scope
										_UpperCAmelCase       :		List[Any] 		=    bos_token_id


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size	)

										_UpperCAmelCase       :		Union[str, Any] 		=    None
										if self.use_input_mask:
														_UpperCAmelCase       :		Dict 		=    random_attention_mask([self.batch_size, self.seq_length]	)

										if input_mask is not None:
														_UpperCAmelCase       :		List[str] 		=    input_mask.numpy()
														_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    input_mask.shape
														_UpperCAmelCase       :		List[str] 		=    np.random.randint(1						,					seq_length - 1						,					size=(batch_size,)	)
														for batch_idx, start_index in enumerate(snake_case__	):
																		_UpperCAmelCase       :		Dict 		=    1
																		_UpperCAmelCase       :		Tuple 		=    0

										_UpperCAmelCase       :		Union[str, Any] 		=    self.get_config()

										return config, input_ids, tf.convert_to_tensor(snake_case__	)


						def        _UpperCAmelCase				(       self	):







										'''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        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    TFBlipTextModel(config=snake_case__	)
										_UpperCAmelCase       :		Optional[int] 		=    model(snake_case__						,					attention_mask=snake_case__						,					training=snake_case__	)
										_UpperCAmelCase       :		Optional[int] 		=    model(snake_case__						,					training=snake_case__	)
										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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    self.prepare_config_and_inputs()
										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    config_and_inputs
										_UpperCAmelCase       :		Tuple 		=    {"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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    BlipTextModelTester(self	)
										_UpperCAmelCase       :		Any 		=    ConfigTester(self						,					config_class=snake_case__						,					hidden_size=37	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						@unittest.skip(reason="Blip does not use inputs_embeds"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase       :		int 		=    TFBlipTextModel.from_pretrained(snake_case__	)
														self.assertIsNotNone(snake_case__	)





						def        _UpperCAmelCase				(       self						,					A_=True	):







										'''simple docstring'''


										super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__	)





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Union[str, Any] )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Dict 		=    b.T
				_UpperCAmelCase       :		Dict 		=    np.sum(np.square(lowerCAmelCase )  ,      axis=1 )
				_UpperCAmelCase       :		Optional[Any] 		=    np.sum(np.square(lowerCAmelCase )  ,      axis=0 )
				_UpperCAmelCase       :		str 		=    np.matmul(lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    aa[:, None] - 2 * ab + ba[None, :]
				return d

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Dict )   ->       int:
				_UpperCAmelCase       :		Any 		=    x.reshape(-1  ,      3 )
				_UpperCAmelCase       :		List[str] 		=    squared_euclidean_distance(lowerCAmelCase  ,      lowerCAmelCase )
				return np.argmin(lowerCAmelCase  ,      axis=1 )





class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["pixel_values"]


						def __init__(       self						,					A_ = None						,					A_ = True						,					A_ = None						,					A_ = PILImageResampling.BILINEAR						,					A_ = True						,					A_ = True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    size if size is not None else {"height": 256, "width": 256}
										_UpperCAmelCase       :		Optional[int] 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    np.array(A_	) if clusters is not None else None
										_UpperCAmelCase       :		int 		=    do_resize
										_UpperCAmelCase       :		Union[str, Any] 		=    size
										_UpperCAmelCase       :		Optional[Any] 		=    resample
										_UpperCAmelCase       :		str 		=    do_normalize
										_UpperCAmelCase       :		List[str] 		=    do_color_quantize


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = PILImageResampling.BILINEAR						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    get_size_dict(A_	)
										if "height" not in size or "width" not in size:
														raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'	)
										return resize(
										    A_						,					size=(size["height"], size["width"])						,					resample=A_						,					data_format=A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    rescale(image=A_						,					scale=1 / 1_27.5						,					data_format=A_	)
										_UpperCAmelCase       :		List[Any] 		=    image - 1
										return image





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = ChannelDimension.FIRST						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase       :		Any 		=    size if size is not None else self.size
										_UpperCAmelCase       :		Dict 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		List[Any] 		=    resample if resample is not None else self.resample
										_UpperCAmelCase       :		Any 		=    do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase       :		Optional[Any] 		=    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										_UpperCAmelCase       :		Any 		=    clusters if clusters is not None else self.clusters
										_UpperCAmelCase       :		Optional[int] 		=    np.array(A_	)

										_UpperCAmelCase       :		List[str] 		=    make_list_of_images(A_	)

										if not valid_images(A_	):
														raise ValueError(
														    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
														    "torch.Tensor, tf.Tensor or jax.ndarray."	)

										if do_resize and size is None or resample is None:
														raise ValueError("Size and resample must be specified if do_resize is True."	)

										if do_color_quantize and clusters is None:
														raise ValueError("Clusters must be specified if do_color_quantize is True."	)

										# All transformations expect numpy arrays.
										_UpperCAmelCase       :		List[str] 		=    [to_numpy_array(A_	) for image in images]

										if do_resize:
														_UpperCAmelCase       :		int 		=    [self.resize(image=A_						,					size=A_						,					resample=A_	) for image in images]

										if do_normalize:
														_UpperCAmelCase       :		List[str] 		=    [self.normalize(image=A_	) for image in images]

										if do_color_quantize:
														_UpperCAmelCase       :		Tuple 		=    [to_channel_dimension_format(A_						,					ChannelDimension.LAST	) for image in images]
														# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
														_UpperCAmelCase       :		List[str] 		=    np.array(A_	)
														_UpperCAmelCase       :		List[Any] 		=    color_quantize(A_						,					A_	).reshape(images.shape[:-1]	)

														# flatten to (batch_size, height*width)
														_UpperCAmelCase       :		Any 		=    images.shape[0]
														_UpperCAmelCase       :		List[Any] 		=    images.reshape(A_						,					-1	)

														# We need to convert back to a list of images to keep consistent behaviour across processors.
														_UpperCAmelCase       :		Union[str, Any] 		=    list(A_	)
										else:
														_UpperCAmelCase       :		Optional[Any] 		=    [to_channel_dimension_format(A_						,					A_	) for image in images]

										_UpperCAmelCase       :		List[Any] 		=    {"input_ids": images}
										return BatchFeature(data=A_						,					tensor_type=A_	)


