text: # Copyright 2020 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.
  
  import unittest
  
  from transformers import (
      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      Text2TextGenerationPipeline,
      pipeline,
  )
  from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
  
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
  
  
  @is_pipeline_test
  class Text2TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
      model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      tf_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  
      def get_test_pipeline(self, model, tokenizer, feature_extractor):
          generator = Text2TextGenerationPipeline(model=model, tokenizer=tokenizer)
          return generator, ["Something to write", "Something else"]
  
      def run_pipeline_test(self, generator, _):
          outputs = generator("Something there")
          self.assertEqual(outputs, [{"generated_text": ANY(str)}])
          # These are encoder decoder, they don't just append to incoming string
          self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))
  
          outputs = generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
          self.assertEqual(
              outputs,
              [
                  [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
                  [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
              ],
          )
  
          outputs = generator(
              ["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
          )
          self.assertEqual(
              outputs,
              [
                  [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
                  [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
              ],
          )
  
          with self.assertRaises(ValueError):
              generator(4)
  
      @require_torch
      def test_small_model_pt(self):
          generator = pipeline("text2text-generation", model="patrickvonplaten/t5-tiny-random", framework="pt")
          # do_sample=False necessary for reproducibility
          outputs = generator("Something there", do_sample=False)
          self.assertEqual(outputs, [{"generated_text": ""}])
  
          num_return_sequences = 3
          outputs = generator(
              "Something there",
              num_return_sequences=num_return_sequences,
              num_beams=num_return_sequences,
          )
          target_outputs = [
              {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
              {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
              {"generated_text": ""},
          ]
          self.assertEqual(outputs, target_outputs)
  
      @require_tf
      def test_small_model_tf(self):
          generator = pipeline("text2text-generation", model="patrickvonplaten/t5-tiny-random", framework="tf")
          # do_sample=False necessary for reproducibility
          outputs = generator("Something there", do_sample=False)
          self.assertEqual(outputs, [{"generated_text": ""}])
  