
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, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ViltImageProcessor







class  _lowerCamelCase			(unittest.TestCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=7							,       SCREAMING_SNAKE_CASE_=3							,       SCREAMING_SNAKE_CASE_=18							,       SCREAMING_SNAKE_CASE_=30							,       SCREAMING_SNAKE_CASE_=400							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=2							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]							,       SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]							,       ):
            __snake_case						       =   size if size is not None else {'shortest_edge': 30}
            __snake_case						       =   parent
            __snake_case						       =   batch_size
            __snake_case						       =   num_channels
            __snake_case						       =   image_size
            __snake_case						       =   min_resolution
            __snake_case						       =   max_resolution
            __snake_case						       =   do_resize
            __snake_case						       =   size
            __snake_case						       =   size_divisor
            __snake_case						       =   do_normalize
            __snake_case						       =   image_mean
            __snake_case						       =   image_std

     def 						__lowerCamelCase				(  self    ):
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "size_divisor": self.size_divisor,
            }

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=False    ):
            if not batched:
                   __snake_case						       =   self.size['shortest_edge']
                   __snake_case						       =   image_inputs[0]
                   if isinstance(SCREAMING_SNAKE_CASE_							,       Image.Image    ):
                          __snake_case       ,       __snake_case						       =   image.size
                   else:
                          __snake_case       ,       __snake_case						       =   image.shape[1], image.shape[2]
                   __snake_case						       =   size / min(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   if h < w:
                          __snake_case       ,       __snake_case						       =   size, scale * w
                   else:
                          __snake_case       ,       __snake_case						       =   scale * h, size

                   __snake_case						       =   int((1_333 / 800) * size    )
                   if max(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ) > max_size:
                          __snake_case						       =   max_size / max(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                          __snake_case						       =   newh * scale
                          __snake_case						       =   neww * scale

                   __snake_case       ,       __snake_case						       =   int(newh + 0.5    ), int(neww + 0.5    )
                   __snake_case       ,       __snake_case						       =   (
                       newh // self.size_divisor * self.size_divisor,
                       neww // self.size_divisor * self.size_divisor,
                   )

            else:
                   __snake_case						       =   []
                   for image in image_inputs:
                          __snake_case       ,       __snake_case						       =   self.get_expected_values([image]    )
                          expected_values.append((expected_height, expected_width)    )
                   __snake_case						       =   max(SCREAMING_SNAKE_CASE_							,       key=lambda SCREAMING_SNAKE_CASE_    : item[0]    )[0]
                   __snake_case						       =   max(SCREAMING_SNAKE_CASE_							,       key=lambda SCREAMING_SNAKE_CASE_    : item[1]    )[1]

            return expected_height, expected_width







@require_torch
@require_vision
class  _lowerCamelCase			(lowerCamelCase							,					unittest.TestCase					):
     lowercase__				  =			ViltImageProcessor if is_vision_available() else None

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   ViltImageProcessingTester(self    )

     @property
     def 						__lowerCamelCase				(  self    ):
            return self.image_processor_tester.prepare_image_processor_dict()

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'image_mean'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'image_std'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'do_normalize'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'do_resize'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'size'    )    )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,       'size_divisor'    )    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.image_processing_class.from_dict(self.image_processor_dict    )
            self.assertEqual(image_processor.size							,       {'shortest_edge': 30}    )

            __snake_case						       =   self.image_processing_class.from_dict(self.image_processor_dict							,       size=42    )
            self.assertEqual(image_processor.size							,       {'shortest_edge': 42}    )

     def 						__lowerCamelCase				(  self    ):
            pass

     def 						__lowerCamelCase				(  self    ):
            # Initialize image_processing
            __snake_case						       =   self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            __snake_case						       =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE_    )
            for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_							,       Image.Image    )

            # Test not batched input
            __snake_case						       =   image_processing(image_inputs[0]							,       return_tensors='pt'    ).pixel_values

            __snake_case       ,       __snake_case						       =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_    )
            self.assertEqual(
                encoded_images.shape							,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,       )

            # Test batched
            __snake_case						       =   image_processing(SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    ).pixel_values

            __snake_case       ,       __snake_case						       =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_							,       batched=SCREAMING_SNAKE_CASE_    )
            self.assertEqual(
                encoded_images.shape							,       (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )							,       )

     def 						__lowerCamelCase				(  self    ):
            # Initialize image_processing
            __snake_case						       =   self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            __snake_case						       =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE_							,       numpify=SCREAMING_SNAKE_CASE_    )
            for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_							,       np.ndarray    )

            # Test not batched input
            __snake_case						       =   image_processing(image_inputs[0]							,       return_tensors='pt'    ).pixel_values

            __snake_case       ,       __snake_case						       =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_    )
            self.assertEqual(
                encoded_images.shape							,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,       )

            # Test batched
            __snake_case						       =   image_processing(SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    ).pixel_values

            __snake_case       ,       __snake_case						       =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_							,       batched=SCREAMING_SNAKE_CASE_    )
            self.assertEqual(
                encoded_images.shape							,       (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )							,       )

     def 						__lowerCamelCase				(  self    ):
            # Initialize image_processing
            __snake_case						       =   self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            __snake_case						       =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE_							,       torchify=SCREAMING_SNAKE_CASE_    )
            for image in image_inputs:
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    )

            # Test not batched input
            __snake_case						       =   image_processing(image_inputs[0]							,       return_tensors='pt'    ).pixel_values

            __snake_case       ,       __snake_case						       =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_    )
            self.assertEqual(
                encoded_images.shape							,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,       )

            # Test batched
            __snake_case						       =   image_processing(SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    ).pixel_values

            __snake_case       ,       __snake_case						       =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_							,       batched=SCREAMING_SNAKE_CASE_    )
            self.assertEqual(
                encoded_images.shape							,       (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )							,       )


from typing import Mapping

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


lowerCamelCase_				:							Union[str, Any]       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "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  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""longt5"""
     lowercase__				  =			["""past_key_values"""]
     lowercase__				  =			{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=32_128							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=64							,       SCREAMING_SNAKE_CASE_=2_048							,       SCREAMING_SNAKE_CASE_=6							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=8							,       SCREAMING_SNAKE_CASE_=127							,       SCREAMING_SNAKE_CASE_=16							,       SCREAMING_SNAKE_CASE_=32							,       SCREAMING_SNAKE_CASE_=128							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=1E-6							,       SCREAMING_SNAKE_CASE_=1.0							,       SCREAMING_SNAKE_CASE_="relu"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_="local"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=1							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   vocab_size
            __snake_case						       =   d_model
            __snake_case						       =   d_kv
            __snake_case						       =   d_ff
            __snake_case						       =   num_layers
            # default = symmetry
            __snake_case						       =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
            __snake_case						       =   num_heads
            __snake_case						       =   local_radius
            __snake_case						       =   global_block_size
            __snake_case						       =   relative_attention_num_buckets
            __snake_case						       =   relative_attention_max_distance
            __snake_case						       =   dropout_rate
            __snake_case						       =   layer_norm_epsilon
            __snake_case						       =   initializer_factor
            __snake_case						       =   feed_forward_proj
            __snake_case						       =   encoder_attention_type
            __snake_case						       =   use_cache

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

            if len(SCREAMING_SNAKE_CASE_    ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_    ) > 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":
                   __snake_case						       =   'gelu_new'

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







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
            }
            if self.use_past:
                   __snake_case						       =   'past_encoder_sequence + sequence'
                   __snake_case						       =   {0: 'batch'}
                   __snake_case						       =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}

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

            return common_inputs

     @property
     def 						__lowerCamelCase				(  self    ):
            return 13
