





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     UpperCamelCase__					(     unittest.TestCase				):






		'''simple docstring'''






		@slow
		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									lowercase_	:   Tuple  				=     TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""  )
									lowercase_	:   List[Any]  				=     AutoTokenizer.from_pretrained("""google/mt5-small"""  )

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

									lowercase_	:   int  				=     model(snake_case__,   labels=snake_case__  ).loss
									lowercase_	:   Any  				=     -tf.math.reduce_mean(snake_case__  ).numpy()

									lowercase_	:   Optional[Any]  				=     -21.228168
									self.assertTrue(abs(mtf_score - EXPECTED_SCORE  ) < 2E-4  )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_							=				{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				["""MaskFormerFeatureExtractor"""]
							UpperCAmelCase_							=				["""MaskFormerImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MaskFormerForInstanceSegmentation""",
							    """MaskFormerModel""",
							    """MaskFormerPreTrainedModel""",
							]
							UpperCAmelCase_							=				[
							    """MaskFormerSwinBackbone""",
							    """MaskFormerSwinModel""",
							    """MaskFormerSwinPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
							from .configuration_maskformer_swin import MaskFormerSwinConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_maskformer import MaskFormerFeatureExtractor
														from .image_processing_maskformer import MaskFormerImageProcessor
							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskformer import (
														    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskFormerForInstanceSegmentation,
														    MaskFormerModel,
														    MaskFormerPreTrainedModel,
														)
														from .modeling_maskformer_swin import (
														    MaskFormerSwinBackbone,
														    MaskFormerSwinModel,
														    MaskFormerSwinPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)