

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class      lowerCamelCase_ (			_lowerCAmelCase	,				_lowerCAmelCase	,				_lowerCAmelCase):




 @register_to_config
 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = False  ,  ):
   super().__init__()

   __UpperCAmelCase	:  List[str]		=     nn.Linear(_UpperCAmelCase  ,  _UpperCAmelCase  ,  bias=_UpperCAmelCase							)

   __UpperCAmelCase	:  int		=     nn.Embedding(_UpperCAmelCase  ,  _UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[int]		=     False

   __UpperCAmelCase	:  Optional[Any]		=     nn.Dropout(p=_UpperCAmelCase							)

   __UpperCAmelCase	:  List[Any]		=     TaConfig(
       d_model=_UpperCAmelCase  ,  num_heads=_UpperCAmelCase  ,  d_kv=_UpperCAmelCase  ,  d_ff=_UpperCAmelCase  ,  feed_forward_proj=_UpperCAmelCase  ,  dropout_rate=_UpperCAmelCase  ,  is_decoder=_UpperCAmelCase  ,  is_encoder_decoder=_UpperCAmelCase  ,  )
   __UpperCAmelCase	:  str		=     nn.ModuleList()
   for lyr_num in range(_UpperCAmelCase							):
     __UpperCAmelCase	:  Tuple		=     TaBlock(_UpperCAmelCase							)
     self.encoders.append(_UpperCAmelCase							)

   __UpperCAmelCase	:  str		=     TaLayerNorm(_UpperCAmelCase							)
   __UpperCAmelCase	:  str		=     nn.Dropout(p=_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Tuple		=     self.input_proj(_UpperCAmelCase							)

   # terminal relative positional encodings
   __UpperCAmelCase	:  int		=     encoder_inputs.shape[1]
   __UpperCAmelCase	:  Dict		=     torch.arange(_UpperCAmelCase  ,  device=encoder_inputs.device							)

   __UpperCAmelCase	:  Union[str, Any]		=     encoder_inputs_mask.sum(-1							)
   __UpperCAmelCase	:  Optional[Any]		=     torch.roll(input_positions.unsqueeze(0							)  ,  tuple(seq_lens.tolist()							)  ,  dims=0							)
   x += self.position_encoding(_UpperCAmelCase							)

   __UpperCAmelCase	:  str		=     self.dropout_pre(_UpperCAmelCase							)

   # inverted the attention mask
   __UpperCAmelCase	:  List[Any]		=     encoder_inputs.size()
   __UpperCAmelCase	:  List[str]		=     self.get_extended_attention_mask(_UpperCAmelCase  ,  _UpperCAmelCase							)

   for lyr in self.encoders:
     __UpperCAmelCase	:  Optional[int]		=     lyr(_UpperCAmelCase  ,  _UpperCAmelCase							)[0]
   __UpperCAmelCase	:  Union[str, Any]		=     self.layer_norm(_UpperCAmelCase							)

   return self.dropout_post(_UpperCAmelCase							), encoder_inputs_mask







"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       IFSuperResolutionPipeline
    __lowercase				:			Optional[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    __lowercase				:			List[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowercase				:			Optional[Any]	=       PipelineTesterMixin.required_optional_params - {"""latents"""}
    def        lowerCamelCase__						(     self  ):
      return self._get_superresolution_dummy_components()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    Tuple            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    Tuple            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    List[str]            = floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )

      _snake_case  :    List[str]            = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()    ,     reason="XFormers attention is only available with CUDA and `xformers` installed"    ,     )
    def        lowerCamelCase__						(     self  ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != "cuda"    ,     reason="float16 requires CUDA"  )
    def        lowerCamelCase__						(     self  ):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1  )
    def        lowerCamelCase__						(     self  ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_local()


    def        lowerCamelCase__						(     self  ):
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2    ,     )

