
"""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 numpy as np
				import tensorflow as tf

				from transformers import TFXLMRobertaModel






@require_tf
@require_sentencepiece
@require_tokenizers
class      _UpperCAmelCase				(					unittest.TestCase):
				@slow
				def 					lowerCamelCase__	(    self     ):
						_snake_case     :						Dict						=   TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base"     )

						_snake_case     :						Optional[Any]						=   {
						    "input_ids": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]]						,    dtype=tf.intaa     ),  # "My dog is cute"
						    "attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]						,    dtype=tf.intaa     ),
						}

						_snake_case     :						Tuple						=   model(snake_case_     )["last_hidden_state"]
						_snake_case     :						Tuple						=   tf.TensorShape((1, 6, 7_68)     )
						self.assertEqual(output.shape						,    snake_case_     )
						# compare the actual values for a slice.
						_snake_case     :						str						=   tf.convert_to_tensor(
						    [
						        [
						            [0.0681762, 0.10894451, 0.06772504],
						            [-0.06423668, 0.02366615, 0.04329344],
						            [-0.06057295, 0.09974135, -0.00070584],
						        ]
						    ]						,    dtype=tf.floataa						,    )

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



"""simple docstring"""
from typing import TYPE_CHECKING

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


_a      :			Optional[int]					=							{
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			List[Any]					=							["""Pix2StructImageProcessor"""]


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Dict					=							[
        """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """Pix2StructPreTrainedModel""",
        """Pix2StructForConditionalGeneration""",
        """Pix2StructVisionModel""",
        """Pix2StructTextModel""",
    ]

if TYPE_CHECKING:
    from .configuration_pixastruct import (
        PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        PixaStructConfig,
        PixaStructTextConfig,
        PixaStructVisionConfig,
    )
    from .processing_pixastruct import PixaStructProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_pixastruct import PixaStructImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_pixastruct import (
            PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
            PixaStructForConditionalGeneration,
            PixaStructPreTrainedModel,
            PixaStructTextModel,
            PixaStructVisionModel,
        )

else:
    import sys

    _a      :			Optional[int]					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

