

"""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				(     _A  ,	_A  ,	unittest.TestCase		):
		SCREAMING_SNAKE_CASE              =					IFSuperResolutionPipeline
		SCREAMING_SNAKE_CASE              =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
		SCREAMING_SNAKE_CASE              =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		SCREAMING_SNAKE_CASE              =					PipelineTesterMixin.required_optional_params - {"latents"}


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							return self._get_superresolution_dummy_components()


		def       SCREAMING_SNAKE_CASE__     (self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Any=0):
							if str(__SCREAMING_SNAKE_CASE).startswith("mps"):
												A    =      torch.manual_seed(__SCREAMING_SNAKE_CASE)
							else:
												A    =      torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)

							A    =      floats_tensor((1, 3, 3_2, 3_2)  ,  rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)

							A    =      {
							    "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       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							self._test_save_load_optional_components()


		@unittest.skipIf(torch_device != "cuda"  ,  reason="float16 requires CUDA")
		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
							# 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				:	Union[str, Any]):
							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							self._test_save_load_local()


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








"""simple docstring"""




from typing import Mapping

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


__A  :					List[str]												=	logging.get_logger(__name__)





class     __UpperCamelCase				(     _A		):
		SCREAMING_SNAKE_CASE              =					"mt5"
		SCREAMING_SNAKE_CASE              =					["past_key_values"]


		def __init__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=2_5_0_1_1_2  ,  __SCREAMING_SNAKE_CASE				:	int=5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6_4  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_0_2_4  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=8  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6  ,  __SCREAMING_SNAKE_CASE				:	int=3_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1_2_8  ,  __SCREAMING_SNAKE_CASE				:	Tuple=0.1  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1E-6  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]="gated-gelu"  ,  __SCREAMING_SNAKE_CASE				:	List[str]=True  ,  __SCREAMING_SNAKE_CASE				:	str=True  ,  __SCREAMING_SNAKE_CASE				:	int="T5Tokenizer"  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=False  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=0  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  **__SCREAMING_SNAKE_CASE				:	str  ,  ):
							super().__init__(
							    is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,  tokenizer_class=__SCREAMING_SNAKE_CASE  ,  tie_word_embeddings=__SCREAMING_SNAKE_CASE  ,  pad_token_id=__SCREAMING_SNAKE_CASE  ,  eos_token_id=__SCREAMING_SNAKE_CASE  ,  decoder_start_token_id=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE  ,  )
							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(__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":
												A    =      "gelu_new"


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							return self.d_model


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							return self.num_heads


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							return self.num_layers





class     __UpperCamelCase				(     _A		):


		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def       SCREAMING_SNAKE_CASE__     (self				:	int):
							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_(__SCREAMING_SNAKE_CASE  ,  direction="inputs")

							return common_inputs


		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							return 1_3


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	int):
							return 5E-4





