





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 numpy as np
	import tensorflow as tf

	from transformers import TFXLMRobertaModel




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


							@slow
							def      _snake_case  (							self    :List[Any]		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""		)

									SCREAMING_SNAKE_CASE__			=      {
									    """input_ids""": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]							,				dtype=tf.intaa		),  # "My dog is cute"
									    """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]							,				dtype=tf.intaa		),
									}

									SCREAMING_SNAKE_CASE__			=      model(__A		)["""last_hidden_state"""]
									SCREAMING_SNAKE_CASE__			=      tf.TensorShape((1, 6, 768)		)
									self.assertEqual(output.shape							,				__A		)
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE__			=      tf.convert_to_tensor(
									    [
									        [
									            [0.0_6_8_1_7_6_2, 0.1_0_8_9_4_4_5_1, 0.0_6_7_7_2_5_0_4],
									            [-0.0_6_4_2_3_6_6_8, 0.0_2_3_6_6_6_1_5, 0.0_4_3_2_9_3_4_4],
									            [-0.0_6_0_5_7_2_9_5, 0.0_9_9_7_4_1_3_5, -0.0_0_0_7_0_5_8_4],
									        ]
									    ]							,				dtype=tf.floataa							,				)

									self.assertTrue(np.allclose(output[:, :3, :3].numpy()							,				expected_slice.numpy()							,				atol=1E-4		)		)









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase   				=			{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XmodForCausalLM',
	    'XmodForMaskedLM',
	    'XmodForMultipleChoice',
	    'XmodForQuestionAnswering',
	    'XmodForSequenceClassification',
	    'XmodForTokenClassification',
	    'XmodModel',
	    'XmodPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

	_lowerCamelCase   				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)