"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase   :						List[Any]      =    {
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase   :						List[str]      =    ["BridgeTowerImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase   :						List[str]      =    [
           "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BridgeTowerForContrastiveLearning",
           "BridgeTowerForImageAndTextRetrieval",
           "BridgeTowerForMaskedLM",
           "BridgeTowerModel",
           "BridgeTowerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bridgetower import (
           BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BridgeTowerConfig,
           BridgeTowerTextConfig,
           BridgeTowerVisionConfig,
       )
       from .processing_bridgetower import BridgeTowerProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_bridgetower import BridgeTowerImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bridgetower import (
                  BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BridgeTowerForContrastiveLearning,
                  BridgeTowerForImageAndTextRetrieval,
                  BridgeTowerForMaskedLM,
                  BridgeTowerModel,
                  BridgeTowerPreTrainedModel,
              )


else:
       import sys

       _UpperCamelCase   :						Optional[int]      =    _LazyModule(__name__, globals()["__file__"], _import_structure)


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        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





