


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


SCREAMING_SNAKE_CASE			: int				   =					logging.get_logger(__name__)






class _lowerCamelCase(		_a  ):
       lowercase_					:      Union[str, Any]       					=     ["""pixel_values"""]
       def __init__(   self, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = PILImageResampling.BILINEAR, lowerCamelCase = True, lowerCamelCase = 1 / 2_55, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = True, **lowerCamelCase, )			->  None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase			:				List[str] 						=						size if size is not None else {'shortest_edge': 2_24}
           _lowercase			:				Dict 						=						get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
           _lowercase			:				Dict 						=						crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
           _lowercase			:				Tuple 						=						get_size_dict(lowerCamelCase, param_name='crop_size')

           _lowercase			:				Optional[int] 						=						do_resize
           _lowercase			:				List[str] 						=						size
           _lowercase			:				List[str] 						=						resample
           _lowercase			:				Dict 						=						do_rescale
           _lowercase			:				str 						=						rescale_factor
           _lowercase			:				Dict 						=						do_center_crop
           _lowercase			:				Optional[int] 						=						crop_size
           _lowercase			:				Tuple 						=						do_flip_channel_order
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = PIL.Image.BILINEAR, lowerCamelCase = None, **lowerCamelCase, )			->  np.ndarray:




           """simple docstring"""

           _lowercase			:				int 						=						get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
           if "shortest_edge" not in size:
               raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''')
           _lowercase			:				Tuple 						=						get_resize_output_image_size(lowerCamelCase, size=size['shortest_edge'], default_to_square=lowerCamelCase)
           return resize(lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, )			->  np.ndarray:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						get_size_dict(lowerCamelCase)
           if "height" not in size or "width" not in size:
               raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''')
           return center_crop(lowerCamelCase, size=(size['height'], size['width']), data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, )			->  List[Any]:




           """simple docstring"""

           return rescale(lowerCamelCase, scale=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  np.ndarray:




           """simple docstring"""

           return flip_channel_order(lowerCamelCase, data_format=lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, **lowerCamelCase, )			->  PIL.Image.Image:




           """simple docstring"""

           _lowercase			:				str 						=						do_resize if do_resize is not None else self.do_resize
           _lowercase			:				Tuple 						=						resample if resample is not None else self.resample
           _lowercase			:				Optional[int] 						=						do_rescale if do_rescale is not None else self.do_rescale
           _lowercase			:				str 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
           _lowercase			:				Tuple 						=						do_center_crop if do_center_crop is not None else self.do_center_crop
           _lowercase			:				int 						=						(
               do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
           )

           _lowercase			:				List[str] 						=						size if size is not None else self.size
           _lowercase			:				Any 						=						get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						crop_size if crop_size is not None else self.crop_size
           _lowercase			:				Optional[Any] 						=						get_size_dict(lowerCamelCase, param_name='crop_size')

           _lowercase			:				Union[str, Any] 						=						make_list_of_images(lowerCamelCase)

           if not valid_images(lowerCamelCase):
               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:
               raise ValueError('Size must be specified if do_resize is True.')

           if do_rescale and rescale_factor is None:
               raise ValueError('Rescale factor must be specified if do_rescale is True.')

           if do_center_crop and crop_size is None:
               raise ValueError('Crop size must be specified if do_center_crop is True.')

           # All transformations expect numpy arrays.
           _lowercase			:				Tuple 						=						[to_numpy_array(lowerCamelCase) for image in images]

           if do_resize:
               _lowercase			:				Optional[int] 						=						[self.resize(image=lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase) for image in images]

           if do_center_crop:
               _lowercase			:				List[Any] 						=						[self.center_crop(image=lowerCamelCase, size=lowerCamelCase) for image in images]

           if do_rescale:
               _lowercase			:				Union[str, Any] 						=						[self.rescale(image=lowerCamelCase, scale=lowerCamelCase) for image in images]

           # the pretrained checkpoints assume images are BGR, not RGB
           if do_flip_channel_order:
               _lowercase			:				List[str] 						=						[self.flip_channel_order(image=lowerCamelCase) for image in images]

           _lowercase			:				Any 						=						[to_channel_dimension_format(lowerCamelCase, lowerCamelCase) for image in images]

           _lowercase			:				Optional[Any] 						=						{'pixel_values': images}
           return BatchFeature(data=lowerCamelCase, tensor_type=lowerCamelCase)




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  Tuple:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						outputs.logits

           # Resize logits and compute semantic segmentation maps
           if target_sizes is not None:
               if len(lowerCamelCase) != len(lowerCamelCase):
                   raise ValueError(
                       'Make sure that you pass in as many target sizes as the batch dimension of the logits')

               if is_torch_tensor(lowerCamelCase):
                   _lowercase			:				List[Any] 						=						target_sizes.numpy()

               _lowercase			:				List[str] 						=						[]

               for idx in range(len(lowerCamelCase)):
                   _lowercase			:				Any 						=						torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode='bilinear', align_corners=lowerCamelCase)
                   _lowercase			:				Union[str, Any] 						=						resized_logits[0].argmax(dim=0)
                   semantic_segmentation.append(lowerCamelCase)
           else:
               _lowercase			:				Dict 						=						logits.argmax(dim=1)
               _lowercase			:				int 						=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

           return semantic_segmentation





'''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		,					UpperCamelCase__		,					UpperCamelCase__=12		,					UpperCamelCase__=7		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=99		,					UpperCamelCase__=32		,					UpperCamelCase__=32		,					UpperCamelCase__=2		,					UpperCamelCase__=4		,					UpperCamelCase__=37		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=0		,					UpperCamelCase__=None		,					)    ->   Union[str, Any]:





           '''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        snake_case_      (       self							)    ->   Dict:





           '''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(UpperCamelCase__							):
                         A_						       =   1
                         A_						       =   0

           A_						       =   self.get_config()

           return config, input_ids, tf.convert_to_tensor(UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''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        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           A_						       =   TFBlipTextModel(config=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					training=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					training=UpperCamelCase__							)
           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        snake_case_      (       self							)    ->   List[Any]:





           '''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__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(TFBlipTextModel,) if is_tf_available() else ()
    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   BlipTextModelTester(self							)
           A_						       =   ConfigTester(self		,					config_class=UpperCamelCase__		,					hidden_size=37							)



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__							)



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





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           pass



    @slow
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   TFBlipTextModel.from_pretrained(UpperCamelCase__							)
                  self.assertIsNotNone(UpperCamelCase__							)







    def        snake_case_      (       self		,					UpperCamelCase__=True							)    ->   str:





           '''simple docstring'''
           super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__							)
