





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       A__     ( unittest.TestCase    ):
      _UpperCAmelCase			:Union[str, Any]							=     ViTImageProcessor if is_vision_available() else None





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     (3, 32, 128)
       UpperCamelCase			:      Tuple 			=     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			:      Tuple 			=     dict(zip(A_ ,							range(len(A_     )     )     )     )

       UpperCamelCase			:      Optional[Any] 			=     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			:      Any 			=     {
           "do_normalize": False,
           "do_resize": True,
           "image_processor_type": "ViTImageProcessor",
           "resample": 3,
           "size": {"height": 32, "width": 128},
       }
       UpperCamelCase			:      int 			=     os.path.join(self.tmpdirname ,							A_     )
       with open(self.image_processor_file ,							"w" ,							encoding="utf-8"     ) as fp:
        json.dump(A_ ,							A_     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       return MgpstrTokenizer.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       return ViTImageProcessor.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       shutil.rmtree(self.tmpdirname     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     np.random.randint(255 ,							size=(3, 30, 400) ,							dtype=np.uinta     )

       UpperCamelCase			:      Union[str, Any] 			=     Image.fromarray(np.moveaxis(A_ ,							0 ,							-1     )     )

       return image_input





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.get_tokenizer()
       UpperCamelCase			:      List[Any] 			=     self.get_image_processor()

       UpperCamelCase			:      Any 			=     MgpstrProcessor(tokenizer=A_ ,							image_processor=A_     )
       processor.save_pretrained(self.tmpdirname     )
       UpperCamelCase			:      Tuple 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.get_tokenizer()
       UpperCamelCase			:      str 			=     self.get_image_processor()

       UpperCamelCase			:      Dict 			=     MgpstrProcessor(tokenizer=A_ ,							image_processor=A_     )
       processor.save_pretrained(self.tmpdirname     )

       UpperCamelCase			:      str 			=     self.get_tokenizer(bos_token="(BOS)" ,							eos_token="(EOS)"     )
       UpperCamelCase			:      int 			=     self.get_image_processor(do_normalize=A_ ,							padding_value=1.0     )

       UpperCamelCase			:      Union[str, Any] 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     self.get_image_processor()
       UpperCamelCase			:      Dict 			=     self.get_tokenizer()

       UpperCamelCase			:      List[Any] 			=     MgpstrProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      List[str] 			=     self.prepare_image_inputs()

       UpperCamelCase			:      List[str] 			=     image_processor(A_ ,							return_tensors="np"     )
       UpperCamelCase			:      Any 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     self.get_image_processor()
       UpperCamelCase			:      Union[str, Any] 			=     self.get_tokenizer()

       UpperCamelCase			:      List[Any] 			=     MgpstrProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      Tuple 			=     "test"

       UpperCamelCase			:      Optional[int] 			=     processor(text=A_     )

       UpperCamelCase			:      Dict 			=     tokenizer(A_     )
       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key] ,							encoded_processor[key]     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.get_image_processor()
       UpperCamelCase			:      List[Any] 			=     self.get_tokenizer()

       UpperCamelCase			:      List[Any] 			=     MgpstrProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      Tuple 			=     "test"
       UpperCamelCase			:      Dict 			=     self.prepare_image_inputs()

       UpperCamelCase			:      Tuple 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     self.get_image_processor()
       UpperCamelCase			:      Tuple 			=     self.get_tokenizer()

       UpperCamelCase			:      str 			=     MgpstrProcessor(tokenizer=A_ ,							image_processor=A_     )

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

       UpperCamelCase			:      Dict 			=     processor.char_decode(A_     )
       UpperCamelCase			:      int 			=     tokenizer.batch_decode(A_     )
       UpperCamelCase			:      str 			=     [seq.replace(" " ,							""     ) for seq in decoded_tok]

       self.assertListEqual(A_ ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.get_image_processor()
       UpperCamelCase			:      Optional[int] 			=     self.get_tokenizer()

       UpperCamelCase			:      Union[str, Any] 			=     MgpstrProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      Dict 			=     None
       UpperCamelCase			:      Any 			=     self.prepare_image_inputs()

       UpperCamelCase			:      List[Any] 			=     processor(text=A_ ,							images=A_     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.get_image_processor()
       UpperCamelCase			:      Optional[int] 			=     self.get_tokenizer()

       UpperCamelCase			:      List[Any] 			=     MgpstrProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      int 			=     torch.randn(1 ,							27 ,							38     )
       UpperCamelCase			:      Tuple 			=     torch.randn(1 ,							27 ,							5_0257     )
       UpperCamelCase			:      Union[str, Any] 			=     torch.randn(1 ,							27 ,							3_0522     )

       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"]     )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase :  Union[str, Any]										=  {
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  Dict										=  ["""VisionEncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  List[str]										=  ["""TFVisionEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  int										=  ["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
    import sys

    __lowerCamelCase :  str										=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
