






"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM







@require_tf
@require_sentencepiece
@require_tokenizers
class    lowercase__			(							unittest.TestCase   ):


			@slow
			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							Union[str, Any]									=TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"					)
										lowerCamelCase_       :							Union[str, Any]									=AutoTokenizer.from_pretrained("google/mt5-small"					)

										lowerCamelCase_       :							List[Any]									=tokenizer("Hello there"   ,       return_tensors="tf"					).input_ids
										lowerCamelCase_       :							Union[str, Any]									=tokenizer("Hi I am"   ,       return_tensors="tf"					).input_ids

										lowerCamelCase_       :							Union[str, Any]									=model(UpperCamelCase_   ,       labels=UpperCamelCase_					).loss
										lowerCamelCase_       :							Union[str, Any]									=-tf.math.reduce_mean(UpperCamelCase_					).numpy()

										lowerCamelCase_       :							Tuple									=-21.228_168
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)







'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .multicontrolnet import MultiControlNetModel
		from .pipeline_controlnet import StableDiffusionControlNetPipeline
		from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
		from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
		from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline