
"""simple docstring"""




import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DPTImageProcessor

class 		a__						(      unittest.TestCase						):
   def __init__(			self	,    _a	,    _a=7	,    _a=3	,    _a=18	,    _a=30	,    _a=400	,    _a=True	,    _a=None	,    _a=True	,    _a=[0.5, 0.5, 0.5]	,    _a=[0.5, 0.5, 0.5]	,    ):
          lowercase    :				int       		=							size if size is not None else {"height": 18, "width": 18}
          lowercase    :				Optional[Any]       		=							parent
          lowercase    :				Optional[Any]       		=							batch_size
          lowercase    :				Tuple       		=							num_channels
          lowercase    :				Optional[Any]       		=							image_size
          lowercase    :				Union[str, Any]       		=							min_resolution
          lowercase    :				int       		=							max_resolution
          lowercase    :				Tuple       		=							do_resize
          lowercase    :				List[str]       		=							size
          lowercase    :				List[Any]       		=							do_normalize
          lowercase    :				Union[str, Any]       		=							image_mean
          lowercase    :				Any       		=							image_std

   def 			__magic_name__			(			self				):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }


@require_torch
@require_vision
class 		a__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				DPTImageProcessor if is_vision_available() else None
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							DPTImageProcessingTester(self				)
   @property
   def 			__magic_name__			(			self				):
          return self.image_processor_tester.prepare_image_processor_dict()
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(_a	,    "image_mean"				)				)
          self.assertTrue(hasattr(_a	,    "image_std"				)				)
          self.assertTrue(hasattr(_a	,    "do_normalize"				)				)
          self.assertTrue(hasattr(_a	,    "do_resize"				)				)
          self.assertTrue(hasattr(_a	,    "size"				)				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.image_processing_class.from_dict(self.image_processor_dict				)
          self.assertEqual(image_processor.size	,    {"height": 18, "width": 18}				)

          lowercase    :				List[Any]       		=							self.image_processing_class.from_dict(self.image_processor_dict	,    size=42				)
          self.assertEqual(image_processor.size	,    {"height": 42, "width": 42}				)
   def 			__magic_name__			(			self				):
          # Initialize image_processing
          lowercase    :				int       		=							self.image_processing_class(**self.image_processor_dict				)
          # create random PIL images
          lowercase    :				Dict       		=							prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_a				)
          for image in image_inputs:
                 self.assertIsInstance(_a	,    Image.Image				)

          # Test not batched input
          lowercase    :				Dict       		=							image_processing(image_inputs[0]	,    return_tensors="pt"				).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["height"],
                  self.image_processor_tester.size["width"],
              )	,    )

          # Test batched
          lowercase    :				Any       		=							image_processing(_a	,    return_tensors="pt"				).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["height"],
                  self.image_processor_tester.size["width"],
              )	,    )
   def 			__magic_name__			(			self				):
          # Initialize image_processing
          lowercase    :				Optional[Any]       		=							self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          lowercase    :				str       		=							prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_a	,    numpify=_a				)
          for image in image_inputs:
                 self.assertIsInstance(_a	,    np.ndarray				)

          # Test not batched input
          lowercase    :				Optional[int]       		=							image_processing(image_inputs[0]	,    return_tensors="pt"				).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["height"],
                  self.image_processor_tester.size["width"],
              )	,    )

          # Test batched
          lowercase    :				Dict       		=							image_processing(_a	,    return_tensors="pt"				).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["height"],
                  self.image_processor_tester.size["width"],
              )	,    )

   def 			__magic_name__			(			self				):
          # Initialize image_processing
          lowercase    :				Optional[Any]       		=							self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          lowercase    :				Union[str, Any]       		=							prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_a	,    torchify=_a				)
          for image in image_inputs:
                 self.assertIsInstance(_a	,    torch.Tensor				)

          # Test not batched input
          lowercase    :				int       		=							image_processing(image_inputs[0]	,    return_tensors="pt"				).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["height"],
                  self.image_processor_tester.size["width"],
              )	,    )

          # Test batched
          lowercase    :				Optional[Any]       		=							image_processing(_a	,    return_tensors="pt"				).pixel_values
          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size["height"],
                  self.image_processor_tester.size["width"],
              )	,    )




"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. 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 re

from ..utils import cached_file


# docstyle-ignore
_A			:       Optional[int]   		=       """
Human: <<task>>

Assistant: """


_A			:       List[Any]   		=       """huggingface-tools/default-prompts"""
_A			:       Optional[int]   		=       {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       List[Any]     , __snake_case				:       Dict="run"  ) ->				Union[str, Any]:
       if prompt_or_repo_id is None:
              lowercase    :				List[Any]       		=							DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search("\\s"     , __snake_case  ) is not None:
              return prompt_or_repo_id

       lowercase    :				Optional[int]       		=							cached_file(
           __snake_case     , PROMPT_FILES[mode]     , repo_type="dataset"     , user_agent={"agent": agent_name}  )
       with open(__snake_case     , "r"     , encoding="utf-8"  ) as f:
              return f.read()


