





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case           =			{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AdaptiveEmbedding""",
      """TransfoXLForSequenceClassification""",
      """TransfoXLLMHeadModel""",
      """TransfoXLModel""",
      """TransfoXLPreTrainedModel""",
      """load_tf_weights_in_transfo_xl""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFAdaptiveEmbedding""",
      """TFTransfoXLForSequenceClassification""",
      """TFTransfoXLLMHeadModel""",
      """TFTransfoXLMainLayer""",
      """TFTransfoXLModel""",
      """TFTransfoXLPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
  from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_transfo_xl import (
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        AdaptiveEmbedding,
        TransfoXLForSequenceClassification,
        TransfoXLLMHeadModel,
        TransfoXLModel,
        TransfoXLPreTrainedModel,
        load_tf_weights_in_transfo_xl,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_transfo_xl import (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAdaptiveEmbedding,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLMainLayer,
        TFTransfoXLModel,
        TFTransfoXLPreTrainedModel,
    )

else:
  import sys

  _snake_case           =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













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  lowerCAmelCase							(       unittest.TestCase ):
  __lowerCamelCase		=      ViTImageProcessor if is_vision_available() else None







  @property
  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     return self.image_processor_tester.prepare_image_processor_dict()







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  (3, 32, 1_28)
     lowercase__			       =  tempfile.mkdtemp()

     # fmt: off
     lowercase__			       =  ["[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
     lowercase__			       =  dict(zip(_lowercase   ,							range(len(_lowercase ) ) ) )

     lowercase__			       =  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(_lowercase ) + "\n" )

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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							**_lowercase  :str ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :List[Any]   ,							**_lowercase  :List[str] ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     shutil.rmtree(self.tmpdirname )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  np.random.randint(2_55   ,							size=(3, 30, 4_00)   ,							dtype=np.uinta )

     lowercase__			       =  Image.fromarray(np.moveaxis(_lowercase   ,							0   ,							-1 ) )

     return image_input







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_tokenizer()
     lowercase__			       =  self.get_image_processor()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )
     processor.save_pretrained(self.tmpdirname )
     lowercase__			       =  MgpstrProcessor.from_pretrained(self.tmpdirname   ,							use_fast=_lowercase )

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

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







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_tokenizer()
     lowercase__			       =  self.get_image_processor()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )
     processor.save_pretrained(self.tmpdirname )

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

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

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

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







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  self.prepare_image_inputs()

     lowercase__			       =  image_processor(_lowercase   ,							return_tensors="np" )
     lowercase__			       =  processor(images=_lowercase   ,							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  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  "test"

     lowercase__			       =  processor(text=_lowercase )

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







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

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

     lowercase__			       =  processor(text=_lowercase   ,							images=_lowercase )

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

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







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

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

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

     self.assertListEqual(_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  None
     lowercase__			       =  self.prepare_image_inputs()

     lowercase__			       =  processor(text=_lowercase   ,							images=_lowercase )

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







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

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

     lowercase__			       =  processor.batch_decode([char_input, bpe_input, wp_input] )

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






