


from typing import Mapping

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


lowerCamelCase_       :       Dict      						=  logging.get_logger(__name__)

lowerCamelCase_       :       Union[str, Any]      						=  {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}






class 				__A    (	UpperCAmelCase_      ):






 """simple docstring"""




 __lowerCAmelCase			 =  """t5"""
 __lowerCAmelCase			 =  ["""past_key_values"""]
 __lowerCAmelCase			 =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


 def __init__(		self			,   __A=3_2128			,   __A=512			,   __A=64			,   __A=2048			,   __A=6			,   __A=None			,   __A=8			,   __A=32			,   __A=128			,   __A=0.1			,   __A=1E-6			,   __A=1.0			,   __A="relu"			,   __A=True			,   __A=True			,   __A=0			,   __A=1			,   **__A			,   ) ->					Dict:
     a					=vocab_size
     a					=d_model
     a					=d_kv
     a					=d_ff
     a					=num_layers
     a					=(
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     a					=num_heads
     a					=relative_attention_num_buckets
     a					=relative_attention_max_distance
     a					=dropout_rate
     a					=layer_norm_epsilon
     a					=initializer_factor
     a					=feed_forward_proj
     a					=use_cache

     a					=self.feed_forward_proj.split('''-'''						)
     a					=act_info[-1]
     a					=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":
         a					='''gelu_new'''

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






class 				__A    (	UpperCAmelCase_      ):






 """simple docstring"""




 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Mapping[str, Mapping[int, str]]:
     a					={
         '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
         '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
     }
     if self.use_past:
         a					='''past_encoder_sequence + sequence'''
         a					={0: '''batch'''}
         a					={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
     else:
         a					={0: '''batch''', 1: '''decoder_sequence'''}
         a					={0: '''batch''', 1: '''decoder_sequence'''}

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

     return common_inputs





 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     return 13



"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  IFInpaintingSuperResolutionPipeline
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}      )
 __lowerCAmelCase			 =  PipelineTesterMixin.required_optional_params - {"latents"}


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     return self._get_superresolution_dummy_components()


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0						) ->					Optional[int]:
     if str(__A						).startswith('''mps'''						):
         a					=torch.manual_seed(__A						)
     else:
         a					=torch.Generator(device=__A						).manual_seed(__A						)

     a					=floats_tensor((1, 3, 16, 16)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)

     a					={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''original_image''': original_image,
         '''mask_image''': mask_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 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_save_load_optional_components()


 @unittest.skipIf(torch_device != '''cuda'''			,   reason='''float16 requires CUDA'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     # 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 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     self._test_save_load_local()





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2			,   )