






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch


@require_torch
class   a__	:

       @property
       def 						UpperCAmelCase(   self      :     str    ):
            return self.get_dummy_input()

       @property
       def 						UpperCAmelCase(   self      :     Dict    ):
            if self.block_type == "down":
                 return (4, 3_2, 1_6, 1_6)
            elif self.block_type == "mid":
                 return (4, 3_2, 3_2, 3_2)
            elif self.block_type == "up":
                 return (4, 3_2, 6_4, 6_4)

            raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''    )

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     Any=True      ,							lowerCamelCase_      :     str=False      ,							lowerCamelCase_      :     str=False      ,							lowerCamelCase_      :     str=False      ,							):
            a_    :     Optional[int]										=    4
            a_    :     Union[str, Any]										=    3_2
            a_    :     Dict										=    (3_2, 3_2)

            a_    :     Tuple										=    torch.manual_seed(0    )
            a_    :     str										=    torch.device(lowerCamelCase_    )
            a_    :     Dict										=    (batch_size, num_channels) + sizes
            a_    :     Tuple										=    randn_tensor(lowerCamelCase_      ,							generator=lowerCamelCase_      ,							device=lowerCamelCase_    )
            a_    :     List[str]										=    {"""hidden_states""": hidden_states}

            if include_temb:
                 a_    :     Any										=    1_2_8
                 a_    :     Any										=    randn_tensor((batch_size, temb_channels)      ,							generator=lowerCamelCase_      ,							device=lowerCamelCase_    )

            if include_res_hidden_states_tuple:
                 a_    :     List[Any]										=    torch.manual_seed(1    )
                 a_    :     Any										=    (randn_tensor(lowerCamelCase_      ,							generator=lowerCamelCase_      ,							device=lowerCamelCase_    ),)

            if include_encoder_hidden_states:
                 a_    :     str										=    floats_tensor((batch_size, 3_2, 3_2)    ).to(lowerCamelCase_    )

            if include_skip_sample:
                 a_    :     Any										=    randn_tensor(((batch_size, 3) + sizes)      ,							generator=lowerCamelCase_      ,							device=lowerCamelCase_    )

            return dummy_input

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     List[Any]										=    {
                """in_channels""": 3_2,
                """out_channels""": 3_2,
                """temb_channels""": 1_2_8,
            }
            if self.block_type == "up":
                 a_    :     List[str]										=    3_2

            if self.block_type == "mid":
                 init_dict.pop("""out_channels"""    )

            a_    :     Union[str, Any]										=    self.dummy_input
            return init_dict, inputs_dict

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     str    ):
            a_    :     Optional[int]										=    self.prepare_init_args_and_inputs_for_common()
            a_    :     List[str]										=    self.block_class(**lowerCamelCase_    )
            unet_block.to(lowerCamelCase_    )
            unet_block.eval()

            with torch.no_grad():
                 a_    :     Tuple										=    unet_block(**lowerCamelCase_    )

            if isinstance(lowerCamelCase_      ,							lowerCamelCase_    ):
                 a_    :     Optional[Any]										=    output[0]

            self.assertEqual(output.shape      ,							self.output_shape    )

            a_    :     Any										=    output[0, -1, -3:, -3:]
            a_    :     int										=    torch.tensor(lowerCamelCase_    ).to(lowerCamelCase_    )
            assert torch_all_close(output_slice.flatten()      ,							lowerCamelCase_      ,							atol=5E-3    )

       @unittest.skipIf(torch_device == """mps"""      ,							"""Training is not supported in mps"""    )
       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Tuple										=    self.prepare_init_args_and_inputs_for_common()
            a_    :     str										=    self.block_class(**lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.train()
            a_    :     List[str]										=    model(**lowerCamelCase_    )

            if isinstance(lowerCamelCase_      ,							lowerCamelCase_    ):
                 a_    :     Optional[int]										=    output[0]

            a_    :     Any										=    torch.device(lowerCamelCase_    )
            a_    :     List[str]										=    randn_tensor(output.shape      ,							device=lowerCamelCase_    )
            a_    :     Union[str, Any]										=    torch.nn.functional.mse_loss(lowerCamelCase_      ,							lowerCamelCase_    )
            loss.backward()







import numpy as np

__lowerCamelCase        =     [
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]


class   a__	:

       def __init__(   self      :     Union[str, Any]    ):
            a_    :     List[Any]										=    np.array(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str    ):
            a_			,			a_    :     Optional[int]										=    np.where(letter == self.SQUARE    )
            a_    :     Union[str, Any]										=    np.concatenate([indexa + 1, indexa + 1]    )
            return indexes

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     str										=    self.SQUARE[indexa - 1, indexa - 1]
            return letter

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     str    ):
            a_    :     List[str]										=    message.lower()
            a_    :     str										=    message.replace(""" """      ,							""""""    )
            a_    :     Union[str, Any]										=    message.replace("""j"""      ,							"""i"""    )

            a_    :     Optional[Any]										=    np.empty((2, len(lowerCamelCase_    ))    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     int										=    self.letter_to_numbers(message[letter_index]    )

                 a_    :     str										=    numbers[0]
                 a_    :     int										=    numbers[1]

            a_    :     int										=    first_step.reshape(2 * len(lowerCamelCase_    )    )
            a_    :     Optional[Any]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Optional[Any]										=    int(second_step[numbers_index * 2]    )
                 a_    :     Optional[Any]										=    int(second_step[(numbers_index * 2) + 1]    )
                 a_    :     Optional[Any]										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     List[str]										=    encoded_message + letter

            return encoded_message

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     str    ):
            a_    :     Optional[Any]										=    message.lower()
            message.replace(""" """      ,							""""""    )
            a_    :     int										=    np.empty(2 * len(lowerCamelCase_    )    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     str										=    self.letter_to_numbers(message[letter_index]    )
                 a_    :     Optional[int]										=    numbers[0]
                 a_    :     Optional[Any]										=    numbers[1]

            a_    :     Tuple										=    first_step.reshape((2, len(lowerCamelCase_    ))    )
            a_    :     Optional[int]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Dict										=    int(second_step[0, numbers_index]    )
                 a_    :     Tuple										=    int(second_step[1, numbers_index]    )
                 a_    :     Tuple										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    decoded_message + letter

            return decoded_message



