



"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class      UpperCAmelCase_  (    unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       int								=			ViTImageProcessor if is_vision_available() else None



      @property
      def 						snake_case_ (					self					:     Union[str, Any]						):
           return self.image_processor_tester.prepare_image_processor_dict()



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Tuple        =   (3, 3_2, 1_2_8)
           _UpperCAmelCase      :     int        =   tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase      :     List[str]        =   ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
           # fmt: on
           _UpperCAmelCase      :     str        =   dict(zip(A					,     range(len(A						)						)						)						)

           _UpperCAmelCase      :     Dict        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["vocab_file"]						)
           with open(self.vocab_file					,     "w"					,     encoding="utf-8"						) as fp:
                fp.write(json.dumps(A						) + "\n"						)

           _UpperCAmelCase      :     Dict        =   {
               "do_normalize": False,
               "do_resize": True,
               "image_processor_type": "ViTImageProcessor",
               "resample": 3,
               "size": {"height": 3_2, "width": 1_2_8},
           }
           _UpperCAmelCase      :     Optional[int]        =   os.path.join(self.tmpdirname					,     A						)
           with open(self.image_processor_file					,     "w"					,     encoding="utf-8"						) as fp:
                json.dump(A					,     A						)



      def 						snake_case_ (					self					:     List[Any]					,     **A					:     Any						):
           return MgpstrTokenizer.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     int					,     **A					:     str						):
           return ViTImageProcessor.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     List[str]						):
           shutil.rmtree(self.tmpdirname						)



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     str        =   np.random.randint(2_5_5					,     size=(3, 3_0, 4_0_0)					,     dtype=np.uinta						)

           _UpperCAmelCase      :     Dict        =   Image.fromarray(np.moveaxis(A					,     0					,     -1						)						)

           return image_input



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     int        =   self.get_tokenizer()
           _UpperCAmelCase      :     Dict        =   self.get_image_processor()

           _UpperCAmelCase      :     int        =   MgpstrProcessor(tokenizer=A					,     image_processor=A						)
           processor.save_pretrained(self.tmpdirname						)
           _UpperCAmelCase      :     List[Any]        =   MgpstrProcessor.from_pretrained(self.tmpdirname					,     use_fast=A						)

           self.assertEqual(processor.char_tokenizer.get_vocab()					,     tokenizer.get_vocab()						)
           self.assertIsInstance(processor.char_tokenizer					,     A						)

           self.assertEqual(processor.image_processor.to_json_string()					,     image_processor.to_json_string()						)
           self.assertIsInstance(processor.image_processor					,     A						)



      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer()
           _UpperCAmelCase      :     List[str]        =   self.get_image_processor()

           _UpperCAmelCase      :     List[Any]        =   MgpstrProcessor(tokenizer=A					,     image_processor=A						)
           processor.save_pretrained(self.tmpdirname						)

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

           _UpperCAmelCase      :     Tuple        =   MgpstrProcessor.from_pretrained(
               self.tmpdirname					,     bos_token="(BOS)"					,     eos_token="(EOS)"					,     do_normalize=A					,     padding_value=1.0						)

           self.assertEqual(processor.char_tokenizer.get_vocab()					,     tokenizer_add_kwargs.get_vocab()						)
           self.assertIsInstance(processor.char_tokenizer					,     A						)

           self.assertEqual(processor.image_processor.to_json_string()					,     image_processor_add_kwargs.to_json_string()						)
           self.assertIsInstance(processor.image_processor					,     A						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Tuple        =   self.get_image_processor()
           _UpperCAmelCase      :     Optional[Any]        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[str]        =   MgpstrProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     List[Any]        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     Optional[int]        =   image_processor(A					,     return_tensors="np"						)
           _UpperCAmelCase      :     List[str]        =   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 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Optional[Any]        =   self.get_image_processor()
           _UpperCAmelCase      :     Optional[Any]        =   self.get_tokenizer()

           _UpperCAmelCase      :     int        =   MgpstrProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Dict        =   "test"

           _UpperCAmelCase      :     List[str]        =   processor(text=A						)

           _UpperCAmelCase      :     List[Any]        =   tokenizer(A						)
           for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]					,     encoded_processor[key]						)



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Optional[Any]        =   self.get_image_processor()
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer()

           _UpperCAmelCase      :     Dict        =   MgpstrProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     List[str]        =   "test"
           _UpperCAmelCase      :     str        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     str        =   processor(text=A					,     images=A						)

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

           # test if it raises when no input is passed
           with pytest.raises(A						):
                processor()



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     str        =   self.get_image_processor()
           _UpperCAmelCase      :     int        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[Any]        =   MgpstrProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Union[str, Any]        =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase      :     List[Any]        =   processor.char_decode(A						)
           _UpperCAmelCase      :     List[Any]        =   tokenizer.batch_decode(A						)
           _UpperCAmelCase      :     Optional[Any]        =   [seq.replace(" "					,     ""						) for seq in decoded_tok]

           self.assertListEqual(A					,     A						)



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[str]        =   self.get_image_processor()
           _UpperCAmelCase      :     Any        =   self.get_tokenizer()

           _UpperCAmelCase      :     Tuple        =   MgpstrProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Tuple        =   None
           _UpperCAmelCase      :     str        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     str        =   processor(text=A					,     images=A						)

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



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_image_processor()
           _UpperCAmelCase      :     List[Any]        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[str]        =   MgpstrProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Tuple        =   torch.randn(1					,     2_7					,     3_8						)
           _UpperCAmelCase      :     Dict        =   torch.randn(1					,     2_7					,     5_0_2_5_7						)
           _UpperCAmelCase      :     Dict        =   torch.randn(1					,     2_7					,     3_0_5_2_2						)

           _UpperCAmelCase      :     List[Any]        =   processor.batch_decode([char_input, bpe_input, wp_input]						)

           self.assertListEqual(list(results.keys()						)					,     ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]						)





"""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
_lowerCAmelCase    : Any							=							"\nHuman: <<task>>\n\nAssistant: "


_lowerCAmelCase    : str							=							"huggingface-tools/default-prompts"
_lowerCAmelCase    : Union[str, Any]							=							{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[int]						,  SCREAMING_SNAKE_CASE__     :						int="run"    )  ->       int:





     '''simple docstring'''

     if prompt_or_repo_id is None:
          _UpperCAmelCase      :     Optional[int]        =   DEFAULT_PROMPTS_REPO

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

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