

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 __SCREAMING_SNAKE_CASE     (	unittest.TestCase		):
    SCREAMING_SNAKE_CASE__      =ViTImageProcessor if is_vision_available() else None






    @property
    def 						__lowerCAmelCase    (       self )   ->					Optional[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()






    def 						__lowerCAmelCase    (       self )   ->					str:
       __SCREAMING_SNAKE_CASE       =				(3, 32, 1_28)
       __SCREAMING_SNAKE_CASE       =				tempfile.mkdtemp()

       # fmt: off
       __SCREAMING_SNAKE_CASE       =				["""[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
       __SCREAMING_SNAKE_CASE       =				dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__ ) ) ) )

       __SCREAMING_SNAKE_CASE       =				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(lowerCamelCase__ ) + "\n" )

       __SCREAMING_SNAKE_CASE       =				{
           """do_normalize""": False,
           """do_resize""": True,
           """image_processor_type""": """ViTImageProcessor""",
           """resample""": 3,
           """size""": {"""height""": 32, """width""": 1_28},
       }
       __SCREAMING_SNAKE_CASE       =				os.path.join(self.tmpdirname,		lowerCamelCase__ )
       with open(self.image_processor_file,		"w",		encoding="utf-8" ) as fp:
          json.dump(lowerCamelCase__,		lowerCamelCase__ )






    def 						__lowerCAmelCase    (       self,		**_a )   ->					List[str]:
       return MgpstrTokenizer.from_pretrained(self.tmpdirname,		**lowerCamelCase__ )






    def 						__lowerCAmelCase    (       self,		**_a )   ->					List[Any]:
       return ViTImageProcessor.from_pretrained(self.tmpdirname,		**lowerCamelCase__ )






    def 						__lowerCAmelCase    (       self )   ->					Any:
       shutil.rmtree(self.tmpdirname )






    def 						__lowerCAmelCase    (       self )   ->					str:
       __SCREAMING_SNAKE_CASE       =				np.random.randint(2_55,		size=(3, 30, 4_00),		dtype=np.uinta )

       __SCREAMING_SNAKE_CASE       =				Image.fromarray(np.moveaxis(lowerCamelCase__,		0,		-1 ) )

       return image_input






    def 						__lowerCAmelCase    (       self )   ->					Tuple:
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()
       __SCREAMING_SNAKE_CASE       =				self.get_image_processor()

       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__ )
       processor.save_pretrained(self.tmpdirname )
       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor.from_pretrained(self.tmpdirname,		use_fast=lowerCamelCase__ )

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

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






    def 						__lowerCAmelCase    (       self )   ->					int:
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()
       __SCREAMING_SNAKE_CASE       =				self.get_image_processor()

       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__ )
       processor.save_pretrained(self.tmpdirname )

       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer(bos_token="(BOS)",		eos_token="(EOS)" )
       __SCREAMING_SNAKE_CASE       =				self.get_image_processor(do_normalize=lowerCamelCase__,		padding_value=1.0 )

       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor.from_pretrained(
           self.tmpdirname,		bos_token="(BOS)",		eos_token="(EOS)",		do_normalize=lowerCamelCase__,		padding_value=1.0 )

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

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






    def 						__lowerCAmelCase    (       self )   ->					List[Any]:
       __SCREAMING_SNAKE_CASE       =				self.get_image_processor()
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()

       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__ )

       __SCREAMING_SNAKE_CASE       =				self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE       =				image_processor(lowerCamelCase__,		return_tensors="np" )
       __SCREAMING_SNAKE_CASE       =				processor(images=lowerCamelCase__,		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 						__lowerCAmelCase    (       self )   ->					str:
       __SCREAMING_SNAKE_CASE       =				self.get_image_processor()
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()

       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__ )

       __SCREAMING_SNAKE_CASE       =				"""test"""

       __SCREAMING_SNAKE_CASE       =				processor(text=lowerCamelCase__ )

       __SCREAMING_SNAKE_CASE       =				tokenizer(lowerCamelCase__ )
       for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key],		encoded_processor[key] )






    def 						__lowerCAmelCase    (       self )   ->					Optional[Any]:
       __SCREAMING_SNAKE_CASE       =				self.get_image_processor()
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()

       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__ )

       __SCREAMING_SNAKE_CASE       =				"""test"""
       __SCREAMING_SNAKE_CASE       =				self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE       =				processor(text=lowerCamelCase__,		images=lowerCamelCase__ )

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

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






    def 						__lowerCAmelCase    (       self )   ->					int:
       __SCREAMING_SNAKE_CASE       =				self.get_image_processor()
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()

       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__ )

       __SCREAMING_SNAKE_CASE       =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

       __SCREAMING_SNAKE_CASE       =				processor.char_decode(lowerCamelCase__ )
       __SCREAMING_SNAKE_CASE       =				tokenizer.batch_decode(lowerCamelCase__ )
       __SCREAMING_SNAKE_CASE       =				[seq.replace(" ",		"" ) for seq in decoded_tok]

       self.assertListEqual(lowerCamelCase__,		lowerCamelCase__ )






    def 						__lowerCAmelCase    (       self )   ->					Tuple:
       __SCREAMING_SNAKE_CASE       =				self.get_image_processor()
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()

       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__ )

       __SCREAMING_SNAKE_CASE       =				None
       __SCREAMING_SNAKE_CASE       =				self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE       =				processor(text=lowerCamelCase__,		images=lowerCamelCase__ )

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






    def 						__lowerCAmelCase    (       self )   ->					Dict:
       __SCREAMING_SNAKE_CASE       =				self.get_image_processor()
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()

       __SCREAMING_SNAKE_CASE       =				MgpstrProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__ )

       __SCREAMING_SNAKE_CASE       =				torch.randn(1,		27,		38 )
       __SCREAMING_SNAKE_CASE       =				torch.randn(1,		27,		5_02_57 )
       __SCREAMING_SNAKE_CASE       =				torch.randn(1,		27,		3_05_22 )

       __SCREAMING_SNAKE_CASE       =				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"""


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    					=					{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					[
       'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'InformerForPrediction',
       'InformerModel',
       'InformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)