





'''simple docstring'''







import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					GPTSanJapaneseTokenizer
     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					{'''do_clean_text''': False, '''add_prefix_space''': False}







     def     UpperCamelCase			( self    ) ->	int:

      '''simple docstring'''





      super().setUp()

      # fmt: off
      snake_case				=			['''こん''', '''こんに''', '''にちは''', '''ばんは''', '''世界,㔺界''', '''、''', '''。''', '''<BR>''', '''<SP>''', '''<TAB>''', '''<URL>''', '''<EMAIL>''', '''<TEL>''', '''<DATE>''', '''<PRICE>''', '''<BLOCK>''', '''<KIGOU>''', '''<U2000U2BFF>''', '''<|emoji1|>''', '''<unk>''', '''<|bagoftoken|>''', '''<|endoftext|>''']
      # fmt: on
      snake_case				=			{'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
      snake_case				=			{'''unk_token''': '''<unk>'''}

      snake_case				=			os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES['''vocab_file''']    )
      snake_case				=			os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES['''emoji_file''']    )
      with open(self.vocab_file    ,   '''w'''    ,   encoding='''utf-8'''    ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )
      with open(self.emoji_file    ,   '''w'''    ) as emoji_writer:
       emoji_writer.write(json.dumps(A__    )    )







     def     UpperCamelCase			( self    ,   **A__    ) ->	Union[str, Any]:

      '''simple docstring'''





      kwargs.update(self.special_tokens_map    )
      return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Any:

      '''simple docstring'''





      snake_case				=			'''こんにちは、世界。 \nこんばんは、㔺界。😀'''
      snake_case				=			'''こんにちは、世界。 \nこんばんは、世界。😀'''
      return input_text, output_text







     def     UpperCamelCase			( self    ,   A__    ) ->	str:

      '''simple docstring'''





      snake_case				,		snake_case				=			self.get_input_output_texts(A__    )
      snake_case				=			tokenizer.encode(A__    ,   add_special_tokens=A__    )
      snake_case				=			tokenizer.decode(A__    ,   clean_up_tokenization_spaces=A__    )
      return text, ids







     def     UpperCamelCase			( self    ) ->	List[Any]:

      '''simple docstring'''





      pass  # TODO add if relevant







     def     UpperCamelCase			( self    ) ->	int:

      '''simple docstring'''





      pass  # TODO add if relevant







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:

      '''simple docstring'''





      pass  # TODO add if relevant







     def     UpperCamelCase			( self    ) ->	Dict:

      '''simple docstring'''





      snake_case				=			self.get_tokenizer()

      # Testing tokenization
      snake_case				=			'''こんにちは、世界。　こんばんは、㔺界。'''
      snake_case				=			['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
      snake_case				=			tokenizer.tokenize(A__    )
      self.assertListEqual(A__    ,   A__    )

      # Testing conversion to ids without special tokens
      snake_case				=			[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
      snake_case				=			tokenizer.convert_tokens_to_ids(A__    )
      self.assertListEqual(A__    ,   A__    )

      # Testing conversion to ids with special tokens
      snake_case				=			tokens + [tokenizer.unk_token]
      snake_case				=			[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
      snake_case				=			tokenizer.convert_tokens_to_ids(A__    )
      self.assertListEqual(A__    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Dict:

      '''simple docstring'''





      snake_case				=			self.get_tokenizer()

      # Testing tokenization
      snake_case				=			'''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
      snake_case				=			'''こんにちは、、、、世界。こんばんは、、、、世界。'''
      snake_case				=			tokenizer.encode(A__    )
      snake_case				=			tokenizer.decode(A__    )
      self.assertEqual(A__    ,   A__    )







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

      '''simple docstring'''





      snake_case				=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''    )

      # Testing tokenization
      snake_case				=			'''こんにちは、世界。'''
      snake_case				=			'''こんばんは、㔺界。😀'''
      snake_case				=			'''こんにちは、世界。こんばんは、世界。😀'''
      snake_case				=			tokenizer.encode(prefix_text + input_text    )
      snake_case				=			tokenizer.encode(''''''    ,   prefix_text=prefix_text + input_text    )
      snake_case				=			tokenizer.encode(A__    ,   prefix_text=A__    )
      snake_case				=			tokenizer.decode(A__    )
      snake_case				=			tokenizer.decode(A__    )
      snake_case				=			tokenizer.decode(A__    )
      self.assertEqual(A__    ,   A__    )
      self.assertEqual(A__    ,   A__    )
      self.assertEqual(A__    ,   A__    )







     @slow
     def     UpperCamelCase			( self    ) ->	int:

      '''simple docstring'''





      snake_case				=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''    )

      # Testing tokenization
      snake_case				=			'''こんにちは、世界。'''
      snake_case				=			'''こんばんは、㔺界。😀'''

      snake_case				=			len(tokenizer.encode(A__    )    ) - 2
      snake_case				=			len(tokenizer.encode(A__    )    ) - 2

      snake_case				=			[1] + [0] * (len_prefix + len_text + 1)
      snake_case				=			[1] * (len_prefix + len_text + 1) + [0]
      snake_case				=			[1] + [1] * (len_prefix) + [0] * (len_text + 1)

      snake_case				=			tokenizer(prefix_text + input_text    ).token_type_ids
      snake_case				=			tokenizer(''''''    ,   prefix_text=prefix_text + input_text    ).token_type_ids
      snake_case				=			tokenizer(A__    ,   prefix_text=A__    ).token_type_ids
      self.assertListEqual(A__    ,   A__    )
      self.assertListEqual(A__    ,   A__    )
      self.assertListEqual(A__    ,   A__    )







     @slow
     def     UpperCamelCase			( self    ) ->	Optional[Any]:

      '''simple docstring'''





      snake_case				=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''    )

      snake_case				=			tokenizer.encode('''あンいワ'''    )
      snake_case				=			tokenizer.encode(''''''    ,   prefix_text='''あンいワ'''    )
      snake_case				=			tokenizer.encode('''いワ'''    ,   prefix_text='''あン'''    )

      self.assertEqual(tokenizer.decode(A__    )    ,   tokenizer.decode(A__    )    )
      self.assertEqual(tokenizer.decode(A__    )    ,   tokenizer.decode(A__    )    )
      self.assertNotEqual(A__    ,   A__    )
      self.assertNotEqual(A__    ,   A__    )
      self.assertEqual(x_token_a[1]    ,   x_token_a[-1]    )  # SEG token
      self.assertEqual(x_token_a[1]    ,   x_token_a[3]    )  # SEG token







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

      '''simple docstring'''





      snake_case				=			self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''    )

      snake_case				=			[['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
      snake_case				=			tokenizer(A__    ,   padding=A__    )
      snake_case				=			tokenizer.batch_encode_plus(A__    ,   padding=A__    )

      # fmt: off
      snake_case				=			[[3_59_93, 86_40, 2_59_48, 3_59_98, 3_06_47, 3_56_75, 3_59_99, 3_59_99], [3_59_93, 1_03_82, 98_68, 3_59_98, 3_06_46, 94_59, 3_06_46, 3_56_75]]
      snake_case				=			[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
      snake_case				=			[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
      # fmt: on
      self.assertListEqual(x_token.input_ids    ,   A__    )
      self.assertListEqual(x_token.token_type_ids    ,   A__    )
      self.assertListEqual(x_token.attention_mask    ,   A__    )
      self.assertListEqual(x_token_a.input_ids    ,   A__    )
      self.assertListEqual(x_token_a.token_type_ids    ,   A__    )
      self.assertListEqual(x_token_a.attention_mask    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:

      '''simple docstring'''





      pass







     def     UpperCamelCase			( self    ) ->	Optional[int]:

      '''simple docstring'''





      pass








'''simple docstring'''







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:
   class 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

      snake_case				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			depth_estimator(
          [
              Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              # RGBA
              dataset[0]['''file'''],
              # LA
              dataset[1]['''file'''],
              # L
              dataset[2]['''file'''],
          ]    )
      self.assertEqual(
          [
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
          ]    ,   A__    ,   )







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			hashimage(outputs['''depth''']    )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )    ,   2_9.3_0_4    )
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )    ,   2.6_6_2    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

