
"""simple docstring"""




import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  _A      :	Optional[int]       		=	False


if is_vision_available():
  from PIL import Image
else:

  class 		a__						:
     @staticmethod
     def       __magic_name__      (      *_a     ,   **_a    ):
            pass

@is_pipeline_test
@require_vision
class 		a__						(				unittest.TestCase						):
   __lowerCAmelCase						=    MODEL_FOR_VISION_2_SEQ_MAPPING
   __lowerCAmelCase						=    TF_MODEL_FOR_VISION_2_SEQ_MAPPING
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a    ):
          lowercase   :    Any			=							pipeline("image-to-text"     ,   model=_a     ,   tokenizer=_a     ,   image_processor=_a    )
          lowercase   :    int			=							[
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
          return pipe, examples
   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          lowercase   :    Optional[int]			=							pipe(_a    )
          self.assertEqual(
              _a     ,   [
                  [{"generated_text": ANY(_a    )}],
                  [{"generated_text": ANY(_a    )}],
              ]     ,   )
   @require_tf
   def       __magic_name__      (      self    ):
          lowercase   :    List[str]			=							pipeline("image-to-text"     ,   model="hf-internal-testing/tiny-random-vit-gpt2"     ,   framework="tf"    )
          lowercase   :    str			=							"./tests/fixtures/tests_samples/COCO/000000039769.png"

          lowercase   :    Dict			=							pipe(_a    )
          self.assertEqual(
              _a     ,   [
                  {
                      "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                  },
              ]     ,   )

          lowercase   :    Tuple			=							pipe([image, image]    )
          self.assertEqual(
              _a     ,   [
                  [
                      {
                          "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                      }
                  ],
                  [
                      {
                          "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                      }
                  ],
              ]     ,   )

          lowercase   :    str			=							pipe(_a     ,   max_new_tokens=1    )
          self.assertEqual(
              _a     ,   [{"generated_text": "growth"}]     ,   )
   @require_torch
   def       __magic_name__      (      self    ):
          lowercase   :    Tuple			=							pipeline("image-to-text"     ,   model="hf-internal-testing/tiny-random-vit-gpt2"    )
          lowercase   :    List[str]			=							"./tests/fixtures/tests_samples/COCO/000000039769.png"

          lowercase   :    Union[str, Any]			=							pipe(_a    )
          self.assertEqual(
              _a     ,   [
                  {
                      "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                  },
              ]     ,   )

          lowercase   :    int			=							pipe([image, image]    )
          self.assertEqual(
              _a     ,   [
                  [
                      {
                          "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                      }
                  ],
                  [
                      {
                          "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                      }
                  ],
              ]     ,   )
   @require_torch
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							pipeline("image-to-text"     ,   model="hf-internal-testing/tiny-random-BlipForConditionalGeneration"    )
          lowercase   :    Optional[Any]			=							"./tests/fixtures/tests_samples/COCO/000000039769.png"
          lowercase   :    List[Any]			=							"a photo of"

          lowercase   :    Tuple			=							pipe(_a     ,   prompt=_a    )
          self.assertTrue(outputs[0]["generated_text"].startswith(_a    )    )
   @slow
   @require_torch
   def       __magic_name__      (      self    ):
          lowercase   :    Any			=							pipeline("image-to-text"     ,   model="ydshieh/vit-gpt2-coco-en"    )
          lowercase   :    Tuple			=							"./tests/fixtures/tests_samples/COCO/000000039769.png"

          lowercase   :    Tuple			=							pipe(_a    )
          self.assertEqual(_a     ,   [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]    )

          lowercase   :    Union[str, Any]			=							pipe([image, image]    )
          self.assertEqual(
              _a     ,   [
                  [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                  [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
              ]     ,   )
   @slow
   @require_torch
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							pipeline("image-to-text"     ,   model="Salesforce/blip-image-captioning-base"    )
          lowercase   :    List[Any]			=							"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
          lowercase   :    str			=							Image.open(requests.get(_a     ,   stream=_a    ).raw    )

          lowercase   :    Optional[Any]			=							pipe(_a    )
          self.assertEqual(_a     ,   [{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}]    )
   @slow
   @require_torch
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[int]			=							pipeline("image-to-text"     ,   model="microsoft/git-base-coco"    )
          lowercase   :    Optional[int]			=							"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
          lowercase   :    Tuple			=							Image.open(requests.get(_a     ,   stream=_a    ).raw    )

          lowercase   :    str			=							pipe(_a    )
          self.assertEqual(_a     ,   [{"generated_text": "a cartoon of a purple character."}]    )
   @slow
   @require_torch
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							pipeline("image-to-text"     ,   model="Salesforce/blip-image-captioning-base"    )
          lowercase   :    List[Any]			=							"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
          lowercase   :    str			=							Image.open(requests.get(_a     ,   stream=_a    ).raw    )

          lowercase   :    Dict			=							"a photography of"

          lowercase   :    Tuple			=							pipe(_a     ,   prompt=_a    )
          self.assertEqual(_a     ,   [{"generated_text": "a photography of a volcano"}]    )

          with self.assertRaises(_a    ):
                 lowercase   :    Dict			=							pipe([image, image]     ,   prompt=[prompt, prompt]    )
   @slow
   @require_torch
   def       __magic_name__      (      self    ):
          lowercase   :    Dict			=							pipeline("image-to-text"     ,   model="microsoft/git-base-coco"    )
          lowercase   :    Any			=							"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
          lowercase   :    Dict			=							Image.open(requests.get(_a     ,   stream=_a    ).raw    )

          lowercase   :    Dict			=							"a photo of a"

          lowercase   :    int			=							pipe(_a     ,   prompt=_a    )
          self.assertEqual(_a     ,   [{"generated_text": "a photo of a tent with a tent and a tent in the background."}]    )

          with self.assertRaises(_a    ):
                 lowercase   :    Tuple			=							pipe([image, image]     ,   prompt=[prompt, prompt]    )
   @unittest.skipIf(
       not is_torch_greater_or_equal_than_1_11     ,   reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."    )
   @slow
   @require_torch
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							pipeline("image-to-text"     ,   model="google/pix2struct-ai2d-base"    )
          lowercase   :    str			=							"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
          lowercase   :    Optional[int]			=							Image.open(requests.get(_a     ,   stream=_a    ).raw    )

          lowercase   :    Optional[int]			=							"What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

          lowercase   :    Dict			=							pipe(_a     ,   prompt=_a    )
          self.assertEqual(_a     ,   [{"generated_text": "ash cloud"}]    )

          with self.assertRaises(_a    ):
                 lowercase   :    Optional[int]			=							pipe([image, image]     ,   prompt=[prompt, prompt]    )

   @slow
   @require_tf
   def       __magic_name__      (      self    ):
          lowercase   :    Tuple			=							pipeline("image-to-text"     ,   model="ydshieh/vit-gpt2-coco-en"     ,   framework="tf"    )
          lowercase   :    Tuple			=							"./tests/fixtures/tests_samples/COCO/000000039769.png"

          lowercase   :    Optional[int]			=							pipe(_a    )
          self.assertEqual(_a     ,   [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]    )

          lowercase   :    List[Any]			=							pipe([image, image]    )
          self.assertEqual(
              _a     ,   [
                  [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                  [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
              ]     ,   )




"""simple docstring"""




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 		a__						(				a_,      unittest.TestCase						):
   __lowerCAmelCase						=    BertweetTokenizer
   __lowerCAmelCase						=    False
   def       __magic_name__      (      self    ):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          lowercase   :    Tuple			=							["I", "m", "V@@", "R@@", "r", "e@@"]
          lowercase   :    List[Any]			=							dict(zip(_a     ,   range(len(_a    )    )    )    )
          lowercase   :    Union[str, Any]			=							["#version: 0.2", "a m</w>"]
          lowercase   :    List[Any]			=							{"unk_token": "<unk>"}

          lowercase   :    Optional[Any]			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]    )
          lowercase   :    Any			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["merges_file"]    )
          with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 for token in vocab_tokens:
                        fp.write(f"""{token} {vocab_tokens[token]}\n"""    )
          with open(self.merges_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 fp.write("\n".join(_a    )    )
   def       __magic_name__      (      self     ,   **_a    ):
          kwargs.update(self.special_tokens_map    )
          return BertweetTokenizer.from_pretrained(self.tmpdirname     ,   **_a    )
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Union[str, Any]			=							"I am VinAI Research"
          lowercase   :    str			=							"I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
          return input_text, output_text

   def       __magic_name__      (      self    ):
          lowercase   :    List[Any]			=							BertweetTokenizer(self.vocab_file     ,   self.merges_file     ,   **self.special_tokens_map    )
          lowercase   :    int			=							"I am VinAI Research"
          lowercase   :    Dict			=							"I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
          lowercase   :    Tuple			=							tokenizer.tokenize(_a    )
          self.assertListEqual(_a     ,   _a    )

          lowercase   :    Any			=							tokens + [tokenizer.unk_token]

          lowercase   :    List[Any]			=							[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a    )     ,   _a    )


