
"""simple docstring"""




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor

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

   def 			__magic_name__			(			self				):
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                      [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
                  ]				),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }


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

          lowercase    :				Dict       		=							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				):
          lowercase    :				Union[str, Any]       		=							self.image_processing_class(**self.image_processor_dict				)
          lowercase    :				Optional[Any]       		=							json.loads(image_processor.to_json_string()				)
          for key, value in self.image_processor_dict.items():
                 if key == "clusters":
                        self.assertTrue(np.array_equal(_a	,    obj[key]				)				)
                 else:
                        self.assertEqual(obj[key]	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.image_processing_class(**self.image_processor_dict				)

          with tempfile.TemporaryDirectory() as tmpdirname:
                 lowercase    :				List[Any]       		=							os.path.join(_a	,    "image_processor.json"				)
                 image_processor_first.to_json_file(_a				)
                 lowercase    :				Tuple       		=							self.image_processing_class.from_json_file(_a				).to_dict()

          lowercase    :				Any       		=							image_processor_first.to_dict()
          for key, value in image_processor_first.items():
                 if key == "clusters":
                        self.assertTrue(np.array_equal(_a	,    image_processor_second[key]				)				)
                 else:
                        self.assertEqual(image_processor_first[key]	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.image_processing_class(**self.image_processor_dict				)

          with tempfile.TemporaryDirectory() as tmpdirname:
                 image_processor_first.save_pretrained(_a				)
                 lowercase    :				List[Any]       		=							self.image_processing_class.from_pretrained(_a				).to_dict()

          lowercase    :				str       		=							image_processor_first.to_dict()
          for key, value in image_processor_first.items():
                 if key == "clusters":
                        self.assertTrue(np.array_equal(_a	,    image_processor_second[key]				)				)
                 else:
                        self.assertEqual(image_processor_first[key]	,    _a				)

   @unittest.skip("ImageGPT requires clusters at initialization"				)
   def 			__magic_name__			(			self				):
          pass


def   __magic_name__	(      ) ->				Any:
       lowercase    :				Tuple       		=							load_dataset("hf-internal-testing/fixtures_image_utils"     , split="test"  )

       lowercase    :				List[Any]       		=							Image.open(dataset[4]["file"]  )
       lowercase    :				Any       		=							Image.open(dataset[5]["file"]  )

       lowercase    :				str       		=							[imagea, imagea]

       return images

@require_vision
@require_torch
class 		a__						(      unittest.TestCase						):
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"				)

          lowercase    :				List[str]       		=							prepare_images()

          # test non-batched
          lowercase    :				Any       		=							image_processing(images[0]	,    return_tensors="pt"				)

          self.assertIsInstance(encoding.input_ids	,    torch.LongTensor				)
          self.assertEqual(encoding.input_ids.shape	,    (1, 1_024)				)

          lowercase    :				Union[str, Any]       		=							[306, 191, 191]
          self.assertEqual(encoding.input_ids[0, :3].tolist()	,    _a				)

          # test batched
          lowercase    :				Optional[Any]       		=							image_processing(_a	,    return_tensors="pt"				)

          self.assertIsInstance(encoding.input_ids	,    torch.LongTensor				)
          self.assertEqual(encoding.input_ids.shape	,    (2, 1_024)				)

          lowercase    :				Union[str, Any]       		=							[303, 13, 13]
          self.assertEqual(encoding.input_ids[1, -3:].tolist()	,    _a				)




"""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()


