
"""simple docstring"""




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 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							tempfile.mkdtemp()

          # fmt: off
          lowercase    :				Any       		=							["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
          lowercase    :				Optional[int]       		=							dict(zip(_a	,    range(len(_a				)				)				)				)
          lowercase    :				Any       		=							["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
          lowercase    :				Dict       		=							{"unk_token": "<unk>"}

          lowercase    :				Union[str, Any]       		=							os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["vocab_file"]				)
          lowercase    :				Dict       		=							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(_a				) + "\n"				)
          with open(self.merges_file	,    "w"	,    encoding="utf-8"				) as fp:
                 fp.write("\n".join(_a				)				)

          lowercase    :				Optional[Any]       		=							{
              "do_resize": True,
              "size": 20,
              "do_center_crop": True,
              "crop_size": 18,
              "do_normalize": True,
              "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
              "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
          }
          lowercase    :				List[str]       		=							os.path.join(self.tmpdirname	,    _a				)
          with open(self.image_processor_file	,    "w"	,    encoding="utf-8"				) as fp:
                 json.dump(_a	,    _a				)
   def 			__magic_name__			(			self	,    **_a				):
          return CLIPTokenizer.from_pretrained(self.tmpdirname	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return CLIPImageProcessor.from_pretrained(self.tmpdirname	,    **_a				)
   def 			__magic_name__			(			self				):
          shutil.rmtree(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							[np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta				)]

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

          return image_inputs
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_tokenizer()
          lowercase    :				int       		=							self.get_rust_tokenizer()
          lowercase    :				List[str]       		=							self.get_image_processor()

          lowercase    :				int       		=							CLIPProcessor(tokenizer=_a	,    image_processor=_a				)
          processor_slow.save_pretrained(self.tmpdirname				)
          lowercase    :				List[str]       		=							CLIPProcessor.from_pretrained(self.tmpdirname	,    use_fast=_a				)

          lowercase    :				int       		=							CLIPProcessor(tokenizer=_a	,    image_processor=_a				)
          processor_fast.save_pretrained(self.tmpdirname				)
          lowercase    :				Dict       		=							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	,    _a				)
          self.assertIsInstance(processor_fast.tokenizer	,    _a				)

          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	,    _a				)
          self.assertIsInstance(processor_fast.image_processor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							CLIPProcessor(tokenizer=self.get_tokenizer()	,    image_processor=self.get_image_processor()				)
          processor.save_pretrained(self.tmpdirname				)

          lowercase    :				Optional[int]       		=							self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)"				)
          lowercase    :				Optional[Any]       		=							self.get_image_processor(do_normalize=_a	,    padding_value=1.0				)

          lowercase    :				Any       		=							CLIPProcessor.from_pretrained(
              self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=_a	,    padding_value=1.0				)

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

          self.assertEqual(processor.image_processor.to_json_string()	,    image_processor_add_kwargs.to_json_string()				)
          self.assertIsInstance(processor.image_processor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.get_image_processor()
          lowercase    :				Dict       		=							self.get_tokenizer()

          lowercase    :				Any       		=							CLIPProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Dict       		=							self.prepare_image_inputs()

          lowercase    :				Tuple       		=							image_processor(_a	,    return_tensors="np"				)
          lowercase    :				Optional[int]       		=							processor(images=_a	,    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 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							self.get_image_processor()
          lowercase    :				Tuple       		=							self.get_tokenizer()

          lowercase    :				Tuple       		=							CLIPProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Optional[Any]       		=							"lower newer"

          lowercase    :				int       		=							processor(text=_a				)

          lowercase    :				Optional[int]       		=							tokenizer(_a				)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.get_image_processor()
          lowercase    :				Optional[Any]       		=							self.get_tokenizer()

          lowercase    :				Dict       		=							CLIPProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Union[str, Any]       		=							"lower newer"
          lowercase    :				Any       		=							self.prepare_image_inputs()

          lowercase    :				str       		=							processor(text=_a	,    images=_a				)

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

          # test if it raises when no input is passed
          with pytest.raises(_a				):
                 processor()
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.get_image_processor()
          lowercase    :				List[Any]       		=							self.get_tokenizer()

          lowercase    :				str       		=							CLIPProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Optional[int]       		=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase    :				Dict       		=							processor.batch_decode(_a				)
          lowercase    :				Union[str, Any]       		=							tokenizer.batch_decode(_a				)

          self.assertListEqual(_a	,    _a				)

   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.get_image_processor()
          lowercase    :				Dict       		=							self.get_tokenizer()

          lowercase    :				Optional[Any]       		=							CLIPProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Optional[int]       		=							"lower newer"
          lowercase    :				Union[str, Any]       		=							self.prepare_image_inputs()

          lowercase    :				List[str]       		=							processor(text=_a	,    images=_a				)

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




"""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()


