



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor



@require_vision
class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Dict						     =     tempfile.mkdtemp()

          # fmt: off
          UpperCamelCase   :  Tuple						     =     ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
          # fmt: on
          UpperCamelCase   :  List[str]						     =     dict(zip(SCREAMING_SNAKE_CASE_,      range(len(SCREAMING_SNAKE_CASE_	)	)	)	)
          UpperCamelCase   :  Dict						     =     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
          UpperCamelCase   :  Optional[int]						     =     {'unk_token': '<unk>'}

          UpperCamelCase   :  List[Any]						     =     os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES['vocab_file']	)
          UpperCamelCase   :  str						     =     os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES['merges_file']	)
          with open(self.vocab_file,      'w',      encoding='utf-8'	) as fp:
             fp.write(json.dumps(SCREAMING_SNAKE_CASE_	) + '\n'	)
          with open(self.merges_file,      'w',      encoding='utf-8'	) as fp:
             fp.write('\n'.join(SCREAMING_SNAKE_CASE_	)	)

          UpperCamelCase   :  Dict						     =     {
              'do_resize': True,
              'size': 20,
              'do_center_crop': True,
              'crop_size': 18,
              'do_normalize': True,
              'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
              'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
          }
          UpperCamelCase   :  Dict						     =     os.path.join(self.tmpdirname,      SCREAMING_SNAKE_CASE_	)
          with open(self.image_processor_file,      'w',      encoding='utf-8'	) as fp:
             json.dump(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      **SCREAMING_SNAKE_CASE_	)					->		int:
          return CLIPTokenizer.from_pretrained(self.tmpdirname,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      **SCREAMING_SNAKE_CASE_	)					->		str:
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
          return CLIPImageProcessor.from_pretrained(self.tmpdirname,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Optional[int]:
          shutil.rmtree(self.tmpdirname	)
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  List[str]						     =     [np.random.randint(255,      size=(3, 30, 400),      dtype=np.uinta	)]

          UpperCamelCase   :  List[str]						     =     [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_,      0,      -1	)	) for x in image_inputs]

          return image_inputs
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  str						     =     self.get_tokenizer()
          UpperCamelCase   :  Optional[int]						     =     self.get_rust_tokenizer()
          UpperCamelCase   :  int						     =     self.get_image_processor()

          UpperCamelCase   :  str						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          processor_slow.save_pretrained(self.tmpdirname	)
          UpperCamelCase   :  Tuple						     =     CLIPProcessor.from_pretrained(self.tmpdirname,      use_fast=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          processor_fast.save_pretrained(self.tmpdirname	)
          UpperCamelCase   :  Optional[Any]						     =     CLIPProcessor.from_pretrained(self.tmpdirname	)

          self.assertEqual(processor_slow.tokenizer.get_vocab(),      tokenizer_slow.get_vocab()	)
          self.assertEqual(processor_fast.tokenizer.get_vocab(),      tokenizer_fast.get_vocab()	)
          self.assertEqual(tokenizer_slow.get_vocab(),      tokenizer_fast.get_vocab()	)
          self.assertIsInstance(processor_slow.tokenizer,      SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(processor_fast.tokenizer,      SCREAMING_SNAKE_CASE_	)

          self.assertEqual(processor_slow.image_processor.to_json_string(),      image_processor.to_json_string()	)
          self.assertEqual(processor_fast.image_processor.to_json_string(),      image_processor.to_json_string()	)
          self.assertIsInstance(processor_slow.image_processor,      SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(processor_fast.image_processor,      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  str						     =     CLIPProcessor(tokenizer=self.get_tokenizer(),      image_processor=self.get_image_processor()	)
          processor.save_pretrained(self.tmpdirname	)

          UpperCamelCase   :  Any						     =     self.get_tokenizer(bos_token='(BOS)',      eos_token='(EOS)'	)
          UpperCamelCase   :  Optional[Any]						     =     self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_,      padding_value=1.0	)

          UpperCamelCase   :  List[str]						     =     CLIPProcessor.from_pretrained(
              self.tmpdirname,      bos_token='(BOS)',      eos_token='(EOS)',      do_normalize=SCREAMING_SNAKE_CASE_,      padding_value=1.0	)

          self.assertEqual(processor.tokenizer.get_vocab(),      tokenizer_add_kwargs.get_vocab()	)
          self.assertIsInstance(processor.tokenizer,      SCREAMING_SNAKE_CASE_	)

          self.assertEqual(processor.image_processor.to_json_string(),      image_processor_add_kwargs.to_json_string()	)
          self.assertIsInstance(processor.image_processor,      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  List[str]						     =     self.get_image_processor()
          UpperCamelCase   :  List[Any]						     =     self.get_tokenizer()

          UpperCamelCase   :  int						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     self.prepare_image_inputs()

          UpperCamelCase   :  Dict						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='np'	)
          UpperCamelCase   :  Optional[Any]						     =     processor(images=SCREAMING_SNAKE_CASE_,      return_tensors='np'	)

          for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum(),      input_processor[key].sum(),      delta=1e-2	)
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  int						     =     self.get_image_processor()
          UpperCamelCase   :  List[Any]						     =     self.get_tokenizer()

          UpperCamelCase   :  Optional[int]						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     'lower newer'

          UpperCamelCase   :  Dict						     =     processor(text=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     tokenizer(SCREAMING_SNAKE_CASE_	)

          for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key],      encoded_processor[key]	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  Tuple						     =     self.get_image_processor()
          UpperCamelCase   :  Optional[int]						     =     self.get_tokenizer()

          UpperCamelCase   :  Any						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     'lower newer'
          UpperCamelCase   :  Optional[int]						     =     self.prepare_image_inputs()

          UpperCamelCase   :  str						     =     processor(text=SCREAMING_SNAKE_CASE_,      images=SCREAMING_SNAKE_CASE_	)

          self.assertListEqual(list(inputs.keys()	),      ['input_ids', 'attention_mask', 'pixel_values']	)

          # test if it raises when no input is passed
          with pytest.raises(SCREAMING_SNAKE_CASE_	):
             processor()
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  List[str]						     =     self.get_image_processor()
          UpperCamelCase   :  int						     =     self.get_tokenizer()

          UpperCamelCase   :  Any						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCamelCase   :  int						     =     processor.batch_decode(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	)

          self.assertListEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Any						     =     self.get_image_processor()
          UpperCamelCase   :  List[str]						     =     self.get_tokenizer()

          UpperCamelCase   :  Any						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     'lower newer'
          UpperCamelCase   :  Optional[Any]						     =     self.prepare_image_inputs()

          UpperCamelCase   :  str						     =     processor(text=SCREAMING_SNAKE_CASE_,      images=SCREAMING_SNAKE_CASE_	)

          self.assertListEqual(list(inputs.keys()	),      processor.model_input_names	)




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     lowerCAmelCase_		:
           @staticmethod
           def  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : str       					=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
          self.assertEqual({'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},      SCREAMING_SNAKE_CASE_	)
          import datasets

          UpperCamelCase   :  int						     =     datasets.load_dataset('hf-internal-testing/fixtures_image_utils',      'image',      split='test'	)
          UpperCamelCase   :  Tuple						     =     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	)},
              ],      SCREAMING_SNAKE_CASE_,      )
       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF'	)
       def  snake_case_	(      self	)					->		Optional[int]:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[Any]						     =     'Intel/dpt-large'
          UpperCamelCase   :  Tuple						     =     pipeline('depth-estimation',      model=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'	)
          UpperCamelCase   :  int						     =     hashimage(outputs['depth']	)

          # This seems flaky.
          # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
          self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()	),      29.3_04	)
          self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()	),      2.6_62	)






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



