2020 the huggingface team 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 orglicenseslicense2 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 test with empty parameters test with various tokenizer parameters raise value error if truncation parameter given for two places test with empty parameters test with various tokenizer parameters raise value error if truncation parameter given for two places encoderdecoder models are trickier for this pipeline do we want encoder decoder inputs to get some featues do we want encoder only features for now ignore those if we send too small input there s a bug within funnelmodel output with shape 1 4 2 1 doesn t match the broadcast shape 1 4 2 2 2020 the huggingface team 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 fmt skip fmt skip test with empty parameters fmt skip test with various tokenizer parameters raise value error if truncation parameter given for two places test with empty parameters fmt skip test with various tokenizer parameters raise value error if truncation parameter given for two places encoder_decoder models are trickier for this pipeline do we want encoder decoder inputs to get some featues do we want encoder only features for now ignore those if we send too small input there s a bug within funnelmodel output with shape 1 4 2 1 doesn t match the broadcast shape 1 4 2 2
import unittest

import numpy as np

from transformers import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_MAPPING,
    TF_MODEL_MAPPING,
    FeatureExtractionPipeline,
    LxmertConfig,
    is_tf_available,
    is_torch_available,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch


if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf


@is_pipeline_test
class FeatureExtractionPipelineTests(unittest.TestCase):
    model_mapping = MODEL_MAPPING
    tf_model_mapping = TF_MODEL_MAPPING

    @require_torch
    def test_small_model_pt(self):
        feature_extractor = pipeline(
            task="feature-extraction", model="hf-internal-testing/tiny-random-distilbert", framework="pt"
        )
        outputs = feature_extractor("This is a test")
        self.assertEqual(
            nested_simplify(outputs),
            [[[2.287, 1.234, 0.042, 1.53, 1.306, 0.879, -0.526, -1.71, -1.276, 0.756, -0.775, -1.048, -0.25, -0.595, -0.137, -0.598, 2.022, -0.812, 0.284, -0.488, -0.391, -0.403, -0.525, -0.061, -0.228, 1.086, 0.378, -0.14, 0.599, -0.087, -2.259, -0.098], [1.676, 0.232, -1.508, -0.145, 1.798, -1.388, 1.331, -0.37, -0.939, 0.043, 0.06, -0.414, -1.408, 0.24, 0.622, -0.55, -0.569, 1.873, -0.706, 1.924, -0.254, 1.927, -0.423, 0.152, -0.952, 0.509, -0.496, -0.968, 0.093, -1.049, -0.65, 0.312], [0.207, -0.775, -1.822, 0.321, -0.71, -0.201, 0.3, 1.146, -0.233, -0.753, -0.305, 1.309, -1.47, -0.21, 1.802, -1.555, -1.175, 1.323, -0.303, 0.722, -0.076, 0.103, -1.406, 1.931, 0.091, 0.237, 1.172, 1.607, 0.253, -0.9, -1.068, 0.438], [0.615, 1.077, 0.171, -0.175, 1.3, 0.901, -0.653, -0.138, 0.341, -0.654, -0.184, -0.441, -0.424, 0.356, -0.075, 0.26, -1.023, 0.814, 0.524, -0.904, -0.204, -0.623, 1.234, -1.03, 2.594, 0.56, 1.831, -0.199, -1.508, -0.492, -1.687, -2.165], [0.129, 0.008, -1.279, -0.412, -0.004, 1.663, 0.196, 0.104, 0.123, 0.119, 0.635, 1.757, 2.334, -0.799, -1.626, -1.26, 0.595, -0.316, -1.399, 0.232, 0.264, 1.386, -1.171, -0.256, -0.256, -1.944, 1.168, -0.368, -0.714, -0.51, 0.454, 1.148], [-0.32, 0.29, -1.309, -0.177, 0.453, 0.636, -0.024, 0.509, 0.931, -1.754, -1.575, 0.786, 0.046, -1.165, -1.416, 1.373, 1.293, -0.285, -1.541, -1.186, -0.106, -0.994, 2.001, 0.972, -0.02, 1.654, -0.236, 0.643, 1.02, 0.572, -0.914, -0.154], [0.7, -0.937, 0.441, 0.25, 0.78, -0.022, 0.282, -0.095, 1.558, -0.336, 1.706, 0.884, 1.28, 0.198, -0.796, 1.218, -1.769, 1.197, -0.342, -0.177, -0.645, 1.364, 0.008, -0.597, -0.484, -2.772, -0.696, -0.632, -0.34, -1.527, -0.562, 0.862], [2.504, 0.831, -1.271, -0.033, 0.298, -0.735, 1.339, 1.74, 0.233, -1.424, -0.819, -0.761, 0.291, 0.853, -0.092, -0.885, 0.164, 1.025, 0.907, 0.749, -1.515, -0.545, -1.365, 0.271, 0.034, -2.005, 0.031, 0.244, 0.621, 0.176, 0.336, -1.196], [-0.711, 0.591, -1.001, -0.946, 0.784, -1.66, 1.545, 0.799, -0.857, 1.148, 0.213, -0.285, 0.464, -0.139, 0.79, -1.663, -1.121, 0.575, -0.178, -0.508, 1.565, -0.242, -0.346, 1.024, -1.135, -0.158, -2.101, 0.275, 2.009, -0.425, 0.716, 0.981], [0.912, -1.186, -0.846, -0.421, -1.315, -0.827, 0.309, 0.533, 1.029, -2.343, 1.513, -1.238, 1.487, -0.849, 0.896, -0.927, -0.459, 0.159, 0.177, 0.873, 0.935, 1.433, -0.485, 0.737, 1.327, -0.338, 1.608, -0.47, -0.445, -1.118, -0.213, -0.446], [-0.434, -1.362, -1.098, -1.068, 1.507, 0.003, 0.413, -0.395, 0.897, -0.237, 1.405, -0.344, 1.693, 0.677, 0.097, -0.257, -0.602, 1.026, -1.229, 0.855, -0.713, 1.014, 0.443, 0.238, 0.425, -2.184, 1.933, -1.157, -1.132, -0.597, -0.785, 0.967], [0.58, -0.971, 0.789, -0.468, -0.576, 1.779, 1.747, 1.715, -1.939, 0.125, 0.656, -0.042, -1.024, -1.767, 0.107, -0.408, -0.866, -1.774, 1.248, 0.939, -0.033, 1.523, 1.168, -0.744, 0.209, -0.168, -0.316, 0.207, -0.432, 0.047, -0.646, -0.664], [-0.185, -0.613, -1.695, 1.602, -0.32, -0.277, 0.967, 0.728, -0.965, -0.234, 1.069, -0.63, -1.631, 0.711, 0.426, 1.298, -0.191, -0.467, -0.771, 0.971, -0.118, -1.577, -2.064, -0.055, -0.59, 0.642, -0.997, 1.251, 0.538, 1.367, 0.106, 1.704]]])  

    @require_tf
    def test_small_model_tf(self):
        feature_extractor = pipeline(
            task="feature-extraction", model="hf-internal-testing/tiny-random-distilbert", framework="tf"
        )
        outputs = feature_extractor("This is a test")
        self.assertEqual(
            nested_simplify(outputs),
            [[[2.287, 1.234, 0.042, 1.53, 1.306, 0.879, -0.526, -1.71, -1.276, 0.756, -0.775, -1.048, -0.25, -0.595, -0.137, -0.598, 2.022, -0.812, 0.284, -0.488, -0.391, -0.403, -0.525, -0.061, -0.228, 1.086, 0.378, -0.14, 0.599, -0.087, -2.259, -0.098], [1.676, 0.232, -1.508, -0.145, 1.798, -1.388, 1.331, -0.37, -0.939, 0.043, 0.06, -0.414, -1.408, 0.24, 0.622, -0.55, -0.569, 1.873, -0.706, 1.924, -0.254, 1.927, -0.423, 0.152, -0.952, 0.509, -0.496, -0.968, 0.093, -1.049, -0.65, 0.312], [0.207, -0.775, -1.822, 0.321, -0.71, -0.201, 0.3, 1.146, -0.233, -0.753, -0.305, 1.309, -1.47, -0.21, 1.802, -1.555, -1.175, 1.323, -0.303, 0.722, -0.076, 0.103, -1.406, 1.931, 0.091, 0.237, 1.172, 1.607, 0.253, -0.9, -1.068, 0.438], [0.615, 1.077, 0.171, -0.175, 1.3, 0.901, -0.653, -0.138, 0.341, -0.654, -0.184, -0.441, -0.424, 0.356, -0.075, 0.26, -1.023, 0.814, 0.524, -0.904, -0.204, -0.623, 1.234, -1.03, 2.594, 0.56, 1.831, -0.199, -1.508, -0.492, -1.687, -2.165], [0.129, 0.008, -1.279, -0.412, -0.004, 1.663, 0.196, 0.104, 0.123, 0.119, 0.635, 1.757, 2.334, -0.799, -1.626, -1.26, 0.595, -0.316, -1.399, 0.232, 0.264, 1.386, -1.171, -0.256, -0.256, -1.944, 1.168, -0.368, -0.714, -0.51, 0.454, 1.148], [-0.32, 0.29, -1.309, -0.177, 0.453, 0.636, -0.024, 0.509, 0.931, -1.754, -1.575, 0.786, 0.046, -1.165, -1.416, 1.373, 1.293, -0.285, -1.541, -1.186, -0.106, -0.994, 2.001, 0.972, -0.02, 1.654, -0.236, 0.643, 1.02, 0.572, -0.914, -0.154], [0.7, -0.937, 0.441, 0.25, 0.78, -0.022, 0.282, -0.095, 1.558, -0.336, 1.706, 0.884, 1.28, 0.198, -0.796, 1.218, -1.769, 1.197, -0.342, -0.177, -0.645, 1.364, 0.008, -0.597, -0.484, -2.772, -0.696, -0.632, -0.34, -1.527, -0.562, 0.862], [2.504, 0.831, -1.271, -0.033, 0.298, -0.735, 1.339, 1.74, 0.233, -1.424, -0.819, -0.761, 0.291, 0.853, -0.092, -0.885, 0.164, 1.025, 0.907, 0.749, -1.515, -0.545, -1.365, 0.271, 0.034, -2.005, 0.031, 0.244, 0.621, 0.176, 0.336, -1.196], [-0.711, 0.591, -1.001, -0.946, 0.784, -1.66, 1.545, 0.799, -0.857, 1.148, 0.213, -0.285, 0.464, -0.139, 0.79, -1.663, -1.121, 0.575, -0.178, -0.508, 1.565, -0.242, -0.346, 1.024, -1.135, -0.158, -2.101, 0.275, 2.009, -0.425, 0.716, 0.981], [0.912, -1.186, -0.846, -0.421, -1.315, -0.827, 0.309, 0.533, 1.029, -2.343, 1.513, -1.238, 1.487, -0.849, 0.896, -0.927, -0.459, 0.159, 0.177, 0.873, 0.935, 1.433, -0.485, 0.737, 1.327, -0.338, 1.608, -0.47, -0.445, -1.118, -0.213, -0.446], [-0.434, -1.362, -1.098, -1.068, 1.507, 0.003, 0.413, -0.395, 0.897, -0.237, 1.405, -0.344, 1.693, 0.677, 0.097, -0.257, -0.602, 1.026, -1.229, 0.855, -0.713, 1.014, 0.443, 0.238, 0.425, -2.184, 1.933, -1.157, -1.132, -0.597, -0.785, 0.967], [0.58, -0.971, 0.789, -0.468, -0.576, 1.779, 1.747, 1.715, -1.939, 0.125, 0.656, -0.042, -1.024, -1.767, 0.107, -0.408, -0.866, -1.774, 1.248, 0.939, -0.033, 1.523, 1.168, -0.744, 0.209, -0.168, -0.316, 0.207, -0.432, 0.047, -0.646, -0.664], [-0.185, -0.613, -1.695, 1.602, -0.32, -0.277, 0.967, 0.728, -0.965, -0.234, 1.069, -0.63, -1.631, 0.711, 0.426, 1.298, -0.191, -0.467, -0.771, 0.971, -0.118, -1.577, -2.064, -0.055, -0.59, 0.642, -0.997, 1.251, 0.538, 1.367, 0.106, 1.704]]])  

    @require_torch
    def test_tokenization_small_model_pt(self):
        feature_extractor = pipeline(
            task="feature-extraction", model="hf-internal-testing/tiny-random-distilbert", framework="pt"
        )
        
        outputs = feature_extractor("This is a test")
        self.assertEqual(
            nested_simplify(outputs),
            [[[2.287, 1.234, 0.042, 1.53, 1.306, 0.879, -0.526, -1.71, -1.276, 0.756, -0.775, -1.048, -0.25, -0.595, -0.137, -0.598, 2.022, -0.812, 0.284, -0.488, -0.391, -0.403, -0.525, -0.061, -0.228, 1.086, 0.378, -0.14, 0.599, -0.087, -2.259, -0.098], [1.676, 0.232, -1.508, -0.145, 1.798, -1.388, 1.331, -0.37, -0.939, 0.043, 0.06, -0.414, -1.408, 0.24, 0.622, -0.55, -0.569, 1.873, -0.706, 1.924, -0.254, 1.927, -0.423, 0.152, -0.952, 0.509, -0.496, -0.968, 0.093, -1.049, -0.65, 0.312], [0.207, -0.775, -1.822, 0.321, -0.71, -0.201, 0.3, 1.146, -0.233, -0.753, -0.305, 1.309, -1.47, -0.21, 1.802, -1.555, -1.175, 1.323, -0.303, 0.722, -0.076, 0.103, -1.406, 1.931, 0.091, 0.237, 1.172, 1.607, 0.253, -0.9, -1.068, 0.438], [0.615, 1.077, 0.171, -0.175, 1.3, 0.901, -0.653, -0.138, 0.341, -0.654, -0.184, -0.441, -0.424, 0.356, -0.075, 0.26, -1.023, 0.814, 0.524, -0.904, -0.204, -0.623, 1.234, -1.03, 2.594, 0.56, 1.831, -0.199, -1.508, -0.492, -1.687, -2.165], [0.129, 0.008, -1.279, -0.412, -0.004, 1.663, 0.196, 0.104, 0.123, 0.119, 0.635, 1.757, 2.334, -0.799, -1.626, -1.26, 0.595, -0.316, -1.399, 0.232, 0.264, 1.386, -1.171, -0.256, -0.256, -1.944, 1.168, -0.368, -0.714, -0.51, 0.454, 1.148], [-0.32, 0.29, -1.309, -0.177, 0.453, 0.636, -0.024, 0.509, 0.931, -1.754, -1.575, 0.786, 0.046, -1.165, -1.416, 1.373, 1.293, -0.285, -1.541, -1.186, -0.106, -0.994, 2.001, 0.972, -0.02, 1.654, -0.236, 0.643, 1.02, 0.572, -0.914, -0.154], [0.7, -0.937, 0.441, 0.25, 0.78, -0.022, 0.282, -0.095, 1.558, -0.336, 1.706, 0.884, 1.28, 0.198, -0.796, 1.218, -1.769, 1.197, -0.342, -0.177, -0.645, 1.364, 0.008, -0.597, -0.484, -2.772, -0.696, -0.632, -0.34, -1.527, -0.562, 0.862], [2.504, 0.831, -1.271, -0.033, 0.298, -0.735, 1.339, 1.74, 0.233, -1.424, -0.819, -0.761, 0.291, 0.853, -0.092, -0.885, 0.164, 1.025, 0.907, 0.749, -1.515, -0.545, -1.365, 0.271, 0.034, -2.005, 0.031, 0.244, 0.621, 0.176, 0.336, -1.196], [-0.711, 0.591, -1.001, -0.946, 0.784, -1.66, 1.545, 0.799, -0.857, 1.148, 0.213, -0.285, 0.464, -0.139, 0.79, -1.663, -1.121, 0.575, -0.178, -0.508, 1.565, -0.242, -0.346, 1.024, -1.135, -0.158, -2.101, 0.275, 2.009, -0.425, 0.716, 0.981], [0.912, -1.186, -0.846, -0.421, -1.315, -0.827, 0.309, 0.533, 1.029, -2.343, 1.513, -1.238, 1.487, -0.849, 0.896, -0.927, -0.459, 0.159, 0.177, 0.873, 0.935, 1.433, -0.485, 0.737, 1.327, -0.338, 1.608, -0.47, -0.445, -1.118, -0.213, -0.446], [-0.434, -1.362, -1.098, -1.068, 1.507, 0.003, 0.413, -0.395, 0.897, -0.237, 1.405, -0.344, 1.693, 0.677, 0.097, -0.257, -0.602, 1.026, -1.229, 0.855, -0.713, 1.014, 0.443, 0.238, 0.425, -2.184, 1.933, -1.157, -1.132, -0.597, -0.785, 0.967], [0.58, -0.971, 0.789, -0.468, -0.576, 1.779, 1.747, 1.715, -1.939, 0.125, 0.656, -0.042, -1.024, -1.767, 0.107, -0.408, -0.866, -1.774, 1.248, 0.939, -0.033, 1.523, 1.168, -0.744, 0.209, -0.168, -0.316, 0.207, -0.432, 0.047, -0.646, -0.664], [-0.185, -0.613, -1.695, 1.602, -0.32, -0.277, 0.967, 0.728, -0.965, -0.234, 1.069, -0.63, -1.631, 0.711, 0.426, 1.298, -0.191, -0.467, -0.771, 0.971, -0.118, -1.577, -2.064, -0.055, -0.59, 0.642, -0.997, 1.251, 0.538, 1.367, 0.106, 1.704]]])  

        
        tokenize_kwargs = {"max_length": 3}
        outputs = feature_extractor("This is a test", tokenize_kwargs=tokenize_kwargs)
        self.assertEqual(np.squeeze(outputs).shape, (3, 32))

        tokenize_kwargs = {"truncation": True, "padding": True, "max_length": 4}
        outputs = feature_extractor(
            ["This is a test", "This", "This is", "This is a", "This is a test test test test"],
            tokenize_kwargs=tokenize_kwargs,
        )
        self.assertEqual(np.squeeze(outputs).shape, (5, 4, 32))

        tokenize_kwargs = {"padding": True, "max_length": 4}
        outputs = feature_extractor(
            ["This is a test", "This", "This is", "This is a", "This is a test test test test"],
            truncation=True,
            tokenize_kwargs=tokenize_kwargs,
        )
        self.assertEqual(np.squeeze(outputs).shape, (5, 4, 32))

        
        tokenize_kwargs = {"truncation": True}
        with self.assertRaises(ValueError):
            _ = feature_extractor(
                ["This is a test", "This", "This is", "This is a", "This is a test test test test"],
                truncation=True,
                tokenize_kwargs=tokenize_kwargs,
            )

    @require_tf
    def test_tokenization_small_model_tf(self):
        feature_extractor = pipeline(
            task="feature-extraction", model="hf-internal-testing/tiny-random-distilbert", framework="tf"
        )
        
        outputs = feature_extractor("This is a test")
        self.assertEqual(
            nested_simplify(outputs),
            [[[2.287, 1.234, 0.042, 1.53, 1.306, 0.879, -0.526, -1.71, -1.276, 0.756, -0.775, -1.048, -0.25, -0.595, -0.137, -0.598, 2.022, -0.812, 0.284, -0.488, -0.391, -0.403, -0.525, -0.061, -0.228, 1.086, 0.378, -0.14, 0.599, -0.087, -2.259, -0.098], [1.676, 0.232, -1.508, -0.145, 1.798, -1.388, 1.331, -0.37, -0.939, 0.043, 0.06, -0.414, -1.408, 0.24, 0.622, -0.55, -0.569, 1.873, -0.706, 1.924, -0.254, 1.927, -0.423, 0.152, -0.952, 0.509, -0.496, -0.968, 0.093, -1.049, -0.65, 0.312], [0.207, -0.775, -1.822, 0.321, -0.71, -0.201, 0.3, 1.146, -0.233, -0.753, -0.305, 1.309, -1.47, -0.21, 1.802, -1.555, -1.175, 1.323, -0.303, 0.722, -0.076, 0.103, -1.406, 1.931, 0.091, 0.237, 1.172, 1.607, 0.253, -0.9, -1.068, 0.438], [0.615, 1.077, 0.171, -0.175, 1.3, 0.901, -0.653, -0.138, 0.341, -0.654, -0.184, -0.441, -0.424, 0.356, -0.075, 0.26, -1.023, 0.814, 0.524, -0.904, -0.204, -0.623, 1.234, -1.03, 2.594, 0.56, 1.831, -0.199, -1.508, -0.492, -1.687, -2.165], [0.129, 0.008, -1.279, -0.412, -0.004, 1.663, 0.196, 0.104, 0.123, 0.119, 0.635, 1.757, 2.334, -0.799, -1.626, -1.26, 0.595, -0.316, -1.399, 0.232, 0.264, 1.386, -1.171, -0.256, -0.256, -1.944, 1.168, -0.368, -0.714, -0.51, 0.454, 1.148], [-0.32, 0.29, -1.309, -0.177, 0.453, 0.636, -0.024, 0.509, 0.931, -1.754, -1.575, 0.786, 0.046, -1.165, -1.416, 1.373, 1.293, -0.285, -1.541, -1.186, -0.106, -0.994, 2.001, 0.972, -0.02, 1.654, -0.236, 0.643, 1.02, 0.572, -0.914, -0.154], [0.7, -0.937, 0.441, 0.25, 0.78, -0.022, 0.282, -0.095, 1.558, -0.336, 1.706, 0.884, 1.28, 0.198, -0.796, 1.218, -1.769, 1.197, -0.342, -0.177, -0.645, 1.364, 0.008, -0.597, -0.484, -2.772, -0.696, -0.632, -0.34, -1.527, -0.562, 0.862], [2.504, 0.831, -1.271, -0.033, 0.298, -0.735, 1.339, 1.74, 0.233, -1.424, -0.819, -0.761, 0.291, 0.853, -0.092, -0.885, 0.164, 1.025, 0.907, 0.749, -1.515, -0.545, -1.365, 0.271, 0.034, -2.005, 0.031, 0.244, 0.621, 0.176, 0.336, -1.196], [-0.711, 0.591, -1.001, -0.946, 0.784, -1.66, 1.545, 0.799, -0.857, 1.148, 0.213, -0.285, 0.464, -0.139, 0.79, -1.663, -1.121, 0.575, -0.178, -0.508, 1.565, -0.242, -0.346, 1.024, -1.135, -0.158, -2.101, 0.275, 2.009, -0.425, 0.716, 0.981], [0.912, -1.186, -0.846, -0.421, -1.315, -0.827, 0.309, 0.533, 1.029, -2.343, 1.513, -1.238, 1.487, -0.849, 0.896, -0.927, -0.459, 0.159, 0.177, 0.873, 0.935, 1.433, -0.485, 0.737, 1.327, -0.338, 1.608, -0.47, -0.445, -1.118, -0.213, -0.446], [-0.434, -1.362, -1.098, -1.068, 1.507, 0.003, 0.413, -0.395, 0.897, -0.237, 1.405, -0.344, 1.693, 0.677, 0.097, -0.257, -0.602, 1.026, -1.229, 0.855, -0.713, 1.014, 0.443, 0.238, 0.425, -2.184, 1.933, -1.157, -1.132, -0.597, -0.785, 0.967], [0.58, -0.971, 0.789, -0.468, -0.576, 1.779, 1.747, 1.715, -1.939, 0.125, 0.656, -0.042, -1.024, -1.767, 0.107, -0.408, -0.866, -1.774, 1.248, 0.939, -0.033, 1.523, 1.168, -0.744, 0.209, -0.168, -0.316, 0.207, -0.432, 0.047, -0.646, -0.664], [-0.185, -0.613, -1.695, 1.602, -0.32, -0.277, 0.967, 0.728, -0.965, -0.234, 1.069, -0.63, -1.631, 0.711, 0.426, 1.298, -0.191, -0.467, -0.771, 0.971, -0.118, -1.577, -2.064, -0.055, -0.59, 0.642, -0.997, 1.251, 0.538, 1.367, 0.106, 1.704]]])  

        
        tokenize_kwargs = {"max_length": 3}
        outputs = feature_extractor("This is a test", tokenize_kwargs=tokenize_kwargs)
        self.assertEqual(np.squeeze(outputs).shape, (3, 32))

        tokenize_kwargs = {"truncation": True, "padding": True, "max_length": 4}
        outputs = feature_extractor(
            ["This is a test", "This", "This is", "This is a", "This is a test test test test"],
            tokenize_kwargs=tokenize_kwargs,
        )
        self.assertEqual(np.squeeze(outputs).shape, (5, 4, 32))

        tokenize_kwargs = {"padding": True, "max_length": 4}
        outputs = feature_extractor(
            ["This is a test", "This", "This is", "This is a", "This is a test test test test"],
            truncation=True,
            tokenize_kwargs=tokenize_kwargs,
        )
        self.assertEqual(np.squeeze(outputs).shape, (5, 4, 32))

        
        tokenize_kwargs = {"truncation": True}
        with self.assertRaises(ValueError):
            _ = feature_extractor(
                ["This is a test", "This", "This is", "This is a", "This is a test test test test"],
                truncation=True,
                tokenize_kwargs=tokenize_kwargs,
            )

    @require_torch
    def test_return_tensors_pt(self):
        feature_extractor = pipeline(
            task="feature-extraction", model="hf-internal-testing/tiny-random-distilbert", framework="pt"
        )
        outputs = feature_extractor("This is a test", return_tensors=True)
        self.assertTrue(torch.is_tensor(outputs))

    @require_tf
    def test_return_tensors_tf(self):
        feature_extractor = pipeline(
            task="feature-extraction", model="hf-internal-testing/tiny-random-distilbert", framework="tf"
        )
        outputs = feature_extractor("This is a test", return_tensors=True)
        self.assertTrue(tf.is_tensor(outputs))

    def get_shape(self, input_, shape=None):
        if shape is None:
            shape = []
        if isinstance(input_, list):
            subshapes = [self.get_shape(in_, shape) for in_ in input_]
            if all(s == 0 for s in subshapes):
                shape.append(len(input_))
            else:
                subshape = subshapes[0]
                shape = [len(input_), *subshape]
        elif isinstance(input_, float):
            return 0
        else:
            raise ValueError("We expect lists of floats, nothing else")
        return shape

    def get_test_pipeline(self, model, tokenizer, processor):
        if tokenizer is None:
            self.skipTest("No tokenizer")
            return
        elif (
            type(model.config) in FEATURE_EXTRACTOR_MAPPING
            or isinstance(model.config, LxmertConfig)
            or type(model.config) in IMAGE_PROCESSOR_MAPPING
        ):
            self.skipTest("This is a bimodal model, we need to find a more consistent way to switch on those models.")
            return
        elif model.config.is_encoder_decoder:
            self.skipTest(
                
            )

            return
        feature_extractor = FeatureExtractionPipeline(model=model, tokenizer=tokenizer, feature_extractor=processor)
        return feature_extractor, ["This is a test", "This is another test"]

    def run_pipeline_test(self, feature_extractor, examples):
        outputs = feature_extractor("This is a test")

        shape = self.get_shape(outputs)
        self.assertEqual(shape[0], 1)

        
        
        outputs = feature_extractor(["This is a test", "Another longer test"])
        shape = self.get_shape(outputs)
        self.assertEqual(shape[0], 2)

        outputs = feature_extractor("This is a test" * 100, truncation=True)
        shape = self.get_shape(outputs)
        self.assertEqual(shape[0], 1)