import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 BlipImageProcessor
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    Any     ,       lowercase__   :    Optional[Any]=7     ,       lowercase__   :    List[str]=3     ,       lowercase__   :    Tuple=18     ,       lowercase__   :    int=30     ,       lowercase__   :    Optional[int]=400     ,       lowercase__   :    Union[str, Any]=True     ,       lowercase__   :    Tuple=None     ,       lowercase__   :    List[Any]=True     ,       lowercase__   :    int=False     ,       lowercase__   :    Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073]     ,       lowercase__   :    Optional[int]=[0.26_862_954, 0.26_130_258, 0.27_577_711]     ,       lowercase__   :    Dict=True     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   size if size is not None else {'height': 20, 'width': 20}
        lowerCAmelCase__	      =   parent
        lowerCAmelCase__	      =   batch_size
        lowerCAmelCase__	      =   num_channels
        lowerCAmelCase__	      =   image_size
        lowerCAmelCase__	      =   min_resolution
        lowerCAmelCase__	      =   max_resolution
        lowerCAmelCase__	      =   do_resize
        lowerCAmelCase__	      =   size
        lowerCAmelCase__	      =   do_normalize
        lowerCAmelCase__	      =   image_mean
        lowerCAmelCase__	      =   image_std
        lowerCAmelCase__	      =   do_pad
        lowerCAmelCase__	      =   do_convert_rgb




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    Any=False     ,       lowercase__   :    Any=False     ,       lowercase__   :    Any=False):







        '''simple docstring'''



        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               lowerCAmelCase__	      =   []
               for i in range(self.batch_size):
                      image_inputs.append(
                          np.random.randint(
                              255     ,       size=(self.num_channels, self.max_resolution, self.max_resolution)     ,       dtype=np.uinta))
        else:
               lowerCAmelCase__	      =   []
               for i in range(self.batch_size):
                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   np.random.choice(np.arange(self.min_resolution     ,       self.max_resolution)     ,       2)
                      image_inputs.append(np.random.randint(255     ,       size=(self.num_channels, width, height)     ,       dtype=np.uinta))

        if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               lowerCAmelCase__	      =   [Image.fromarray(np.moveaxis(lowercase__     ,       0     ,       -1)) for x in image_inputs]

        if torchify:
               lowerCAmelCase__	      =   [torch.from_numpy(lowercase__) for x in image_inputs]

        return image_inputs






@require_torch
@require_vision
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   BlipImageProcessor if is_vision_available() else None




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   BlipImageProcessingTester(self)




 @property
 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase__     ,       'do_resize'))
        self.assertTrue(hasattr(lowercase__     ,       'size'))
        self.assertTrue(hasattr(lowercase__     ,       'do_normalize'))
        self.assertTrue(hasattr(lowercase__     ,       'image_mean'))
        self.assertTrue(hasattr(lowercase__     ,       'image_std'))
        self.assertTrue(hasattr(lowercase__     ,       'do_convert_rgb'))




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        lowerCAmelCase__	      =   self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__     ,       Image.Image)

        # Test not batched input
        lowerCAmelCase__	      =   image_processor(image_inputs[0]     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )

        # Test batched
        lowerCAmelCase__	      =   image_processor(lowercase__     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        lowerCAmelCase__	      =   self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,       numpify=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__     ,       np.ndarray)

        # Test not batched input
        lowerCAmelCase__	      =   image_processor(image_inputs[0]     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )

        # Test batched
        lowerCAmelCase__	      =   image_processor(lowercase__     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        lowerCAmelCase__	      =   self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,       torchify=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__     ,       torch.Tensor)

        # Test not batched input
        lowerCAmelCase__	      =   image_processor(image_inputs[0]     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )

        # Test batched
        lowerCAmelCase__	      =   image_processor(lowercase__     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )






@require_torch
@require_vision
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   BlipImageProcessor if is_vision_available() else None




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   BlipImageProcessingTester(self     ,       num_channels=4)
        lowerCAmelCase__	      =   3




 @property
 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase__     ,       'do_resize'))
        self.assertTrue(hasattr(lowercase__     ,       'size'))
        self.assertTrue(hasattr(lowercase__     ,       'do_normalize'))
        self.assertTrue(hasattr(lowercase__     ,       'image_mean'))
        self.assertTrue(hasattr(lowercase__     ,       'image_std'))
        self.assertTrue(hasattr(lowercase__     ,       'do_convert_rgb'))




 def 						__snake_case		(     self   :    Union[str, Any]):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        lowerCAmelCase__	      =   self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__     ,       Image.Image)

        # Test not batched input
        lowerCAmelCase__	      =   image_processor(image_inputs[0]     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )

        # Test batched
        lowerCAmelCase__	      =   image_processor(lowercase__     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__      		=					logging.get_logger(__name__)

lowerCAmelCase__      		=					{
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_      					=   'longt5'
 UpperCAmelCase_      					=   ['past_key_values']
 UpperCAmelCase_      					=   {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}




 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    Union[str, Any]=32_128     ,       lowercase__   :    Union[str, Any]=512     ,       lowercase__   :    List[Any]=64     ,       lowercase__   :    Tuple=2_048     ,       lowercase__   :    List[Any]=6     ,       lowercase__   :    Any=None     ,       lowercase__   :    List[str]=8     ,       lowercase__   :    str=127     ,       lowercase__   :    Any=16     ,       lowercase__   :    str=32     ,       lowercase__   :    str=128     ,       lowercase__   :    Union[str, Any]=0.1     ,       lowercase__   :    List[Any]=1e-6     ,       lowercase__   :    Optional[Any]=1.0     ,       lowercase__   :    str="relu"     ,       lowercase__   :    List[str]=True     ,       lowercase__   :    Any="local"     ,       lowercase__   :    str=True     ,       lowercase__   :    Union[str, Any]=0     ,       lowercase__   :    Optional[int]=1     ,       **lowercase__   :    str     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   vocab_size
        lowerCAmelCase__	      =   d_model
        lowerCAmelCase__	      =   d_kv
        lowerCAmelCase__	      =   d_ff
        lowerCAmelCase__	      =   num_layers
        # default = symmetry
        lowerCAmelCase__	      =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
        lowerCAmelCase__	      =   num_heads
        lowerCAmelCase__	      =   local_radius
        lowerCAmelCase__	      =   global_block_size
        lowerCAmelCase__	      =   relative_attention_num_buckets
        lowerCAmelCase__	      =   relative_attention_max_distance
        lowerCAmelCase__	      =   dropout_rate
        lowerCAmelCase__	      =   layer_norm_epsilon
        lowerCAmelCase__	      =   initializer_factor
        lowerCAmelCase__	      =   feed_forward_proj
        lowerCAmelCase__	      =   encoder_attention_type
        lowerCAmelCase__	      =   use_cache

        lowerCAmelCase__	      =   self.feed_forward_proj.split('-')
        lowerCAmelCase__	      =   act_info[-1]
        lowerCAmelCase__	      =   act_info[0] == 'gated'

        if len(lowercase__) > 1 and act_info[0] != "gated" or len(lowercase__) > 2:
               raise ValueError(
                   F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                   'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                   '\'gated-gelu\' or \'relu\'')

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
               lowerCAmelCase__	      =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase__     ,       eos_token_id=lowercase__     ,       is_encoder_decoder=lowercase__     ,       **lowercase__     ,       )






class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 @property
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
               lowerCAmelCase__	      =   'past_encoder_sequence + sequence'
               lowerCAmelCase__	      =   {0: 'batch'}
               lowerCAmelCase__	      =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
               lowerCAmelCase__	      =   {0: 'batch', 1: 'decoder_sequence'}
               lowerCAmelCase__	      =   {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
               self.fill_with_past_key_values_(lowercase__     ,       direction='inputs')

        return common_inputs




 @property
 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        return 13
