

import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel





class A__    :



 """simple docstring"""







 def __init__(		self :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	str=1_3     ,						A_ :	Tuple=7     ,						A_ :	Dict=True     ,						A_ :	List[Any]=True     ,						A_ :	Optional[int]=True     ,						A_ :	List[str]=True     ,						A_ :	Optional[Any]=9_9     ,						A_ :	Optional[Any]=3_2     ,						A_ :	Optional[Any]=5     ,						A_ :	List[str]=4     ,						A_ :	Dict=4     ,						A_ :	List[str]="gelu"     ,						A_ :	List[str]=0.0     ,						A_ :	List[str]=0.1     ,						A_ :	Union[str, Any]=True     ,						A_ :	Dict=5_1_2     ,						A_ :	List[str]=1_6     ,						A_ :	Union[str, Any]=2     ,						A_ :	str=0.02     ,						A_ :	Optional[int]=3     ,						A_ :	List[Any]=4     ,						A_ :	Any=None     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    parent
      _lowerCAmelCase   :					Union[str, Any]        =    batch_size
      _lowerCAmelCase   :					Optional[int]        =    seq_length
      _lowerCAmelCase   :					Optional[Any]        =    is_training
      _lowerCAmelCase   :					str        =    use_input_mask
      _lowerCAmelCase   :					Optional[Any]        =    use_token_type_ids
      _lowerCAmelCase   :					Tuple        =    use_labels
      _lowerCAmelCase   :					Tuple        =    vocab_size
      _lowerCAmelCase   :					Optional[Any]        =    hidden_size
      _lowerCAmelCase   :					List[Any]        =    num_hidden_layers
      _lowerCAmelCase   :					Optional[Any]        =    num_attention_heads
      _lowerCAmelCase   :					List[Any]        =    intermediate_multiple_size
      _lowerCAmelCase   :					Optional[Any]        =    hidden_act
      _lowerCAmelCase   :					Any        =    hidden_dropout
      _lowerCAmelCase   :					Optional[int]        =    attention_dropout
      _lowerCAmelCase   :					Any        =    weight_tying
      _lowerCAmelCase   :					Any        =    max_position_embeddings
      _lowerCAmelCase   :					Tuple        =    type_vocab_size
      _lowerCAmelCase   :					List[str]        =    type_sequence_label_size
      _lowerCAmelCase   :					int        =    initializer_range
      _lowerCAmelCase   :					Optional[int]        =    num_labels
      _lowerCAmelCase   :					str        =    num_choices
      _lowerCAmelCase   :					List[Any]        =    scope

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					str        =    None
      if self.use_input_mask:
           _lowerCAmelCase   :					Dict        =    random_attention_mask([self.batch_size, self.seq_length]       )

      _lowerCAmelCase   :					Union[str, Any]        =    None
      if self.use_labels:
           _lowerCAmelCase   :					List[str]        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels       )

      _lowerCAmelCase   :					str        =    self.get_config()

      return config, input_ids, input_mask, token_labels

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return GPTNeoXJapaneseConfig(
          vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						num_hidden_layers=self.num_hidden_layers     ,						num_attention_heads=self.num_attention_heads     ,						intermediate_multiple_size=self.intermediate_multiple_size     ,						hidden_act=self.hidden_act     ,						hidden_dropout=self.hidden_dropout     ,						attention_dropout=self.attention_dropout     ,						weight_tying=self.weight_tying     ,						max_position_embeddings=self.max_position_embeddings     ,						type_vocab_size=self.type_vocab_size     ,						is_decoder=A_     ,						initializer_range=self.initializer_range     ,						)

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Any        =    self.prepare_config_and_inputs()

      _lowerCAmelCase   :					List[Any]        =    True

      return config, input_ids, input_mask, token_labels

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Any     ,						A_ :	Tuple     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    GPTNeoXJapaneseModel(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					int        =    model(A_     ,						attention_mask=A_       )
      _lowerCAmelCase   :					str        =    model(A_       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	Any     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    True
      _lowerCAmelCase   :					Optional[int]        =    GPTNeoXJapaneseModel(A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					Dict        =    model(A_     ,						attention_mask=A_       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	Any     ,						A_ :	Union[str, Any]     ,						A_ :	int     ,						A_ :	List[str]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    GPTNeoXJapaneseForCausalLM(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					int        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Optional[int]     ,						A_ :	Optional[int]     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    True
      _lowerCAmelCase   :					Optional[int]        =    GPTNeoXJapaneseForCausalLM(config=A_       )
      model.to(A_       )
      model.eval()

      # first forward pass
      _lowerCAmelCase   :					Tuple        =    model(A_     ,						attention_mask=A_     ,						use_cache=A_       )
      _lowerCAmelCase   :					int        =    outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _lowerCAmelCase   :					List[Any]        =    ids_tensor((self.batch_size, 3)     ,						config.vocab_size       )
      _lowerCAmelCase   :					Optional[Any]        =    ids_tensor((self.batch_size, 3)     ,						vocab_size=2       )

      # append to next input_ids and
      _lowerCAmelCase   :					Optional[Any]        =    torch.cat([input_ids, next_tokens]     ,						dim=-1       )
      _lowerCAmelCase   :					List[str]        =    torch.cat([input_mask, next_mask]     ,						dim=-1       )

      _lowerCAmelCase   :					List[str]        =    model(A_     ,						attention_mask=A_     ,						output_hidden_states=A_       )
      _lowerCAmelCase   :					List[Any]        =    output_from_no_past["hidden_states"][0]
      _lowerCAmelCase   :					List[str]        =    model(
          A_     ,						attention_mask=A_     ,						past_key_values=A_     ,						output_hidden_states=A_     ,						)["hidden_states"][0]

      # select random slice
      _lowerCAmelCase   :					Tuple        =    ids_tensor((1,)     ,						output_from_past.shape[-1]       ).item()
      _lowerCAmelCase   :					Optional[int]        =    output_from_no_past[:, -3:, random_slice_idx].detach()
      _lowerCAmelCase   :					Dict        =    output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(A_     ,						A_     ,						atol=1E-3       )       )


 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.prepare_config_and_inputs()
      _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    config_and_inputs
      _lowerCAmelCase   :					Dict        =    {"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict





@require_torch
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
 _lowercase     :					int      =						(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
 _lowercase     :					Dict      =						(
     {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
     if is_torch_available()
     else {}
 )
 _lowercase     :					Tuple      =						False
 _lowercase     :					Optional[int]      =						False
 _lowercase     :					str      =						False
 _lowercase     :					List[str]      =						False

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    GPTNeoXJapaneseModelTester(self       )
      _lowerCAmelCase   :					Optional[int]        =    ConfigTester(self     ,						config_class=A_     ,						hidden_size=3_7       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					int        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(A_     ,						A_     ,						A_       )

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(A_     ,						A_     ,						A_       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    self.model_tester.prepare_config_and_inputs_for_decoder()

      _lowerCAmelCase   :					Dict        =    None

      self.model_tester.create_and_check_model_as_decoder(A_     ,						A_     ,						A_       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(A_     ,						A_     ,						A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_causal_lm(*A_       )


 @slow
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    "abeja/gpt-neox-japanese-2.7b"

      _lowerCAmelCase   :					Dict        =    ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

      _lowerCAmelCase   :					Tuple        =    [
          "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
          "100年後に必要とされる会社は、「人」が中心の会社です。",
          "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
          "国境の長いトンネルを抜けると、そこは雪国だった。",
          "美味しい日本食といえば、やっぱりお寿司ですよね。",
      ]

      _lowerCAmelCase   :					Optional[int]        =    GPTNeoXJapaneseTokenizer.from_pretrained(A_       )
      _lowerCAmelCase   :					Optional[Any]        =    GPTNeoXJapaneseForCausalLM.from_pretrained(A_       )

      _lowerCAmelCase   :					int        =    []
      for prompt in prompts:
           _lowerCAmelCase   :					Tuple        =    tokenizer(A_     ,						return_tensors="pt"       ).input_ids
           _lowerCAmelCase   :					Union[str, Any]        =    model.generate(A_     ,						max_length=5_0       )
           _lowerCAmelCase   :					Union[str, Any]        =    tokenizer.batch_decode(A_     ,						skip_special_tokens=A_       )
           predicted_outputs += generated_string
      self.assertListEqual(A_     ,						A_       )









from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

__UpperCAmelCase    				=     logging.get_logger(__name__)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     return [
         int(1000 * (box[0] / width)				),
         int(1000 * (box[1] / height)				),
         int(1000 * (box[2] / width)				),
         int(1000 * (box[3] / height)				),
     ]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else ""

     # apply OCR
     _lowerCAmelCase   :					List[str]        =    to_pil_image(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    pil_image.size
     _lowerCAmelCase   :					Union[str, Any]        =    pytesseract.image_to_data(SCREAMING_SNAKE_CASE		,							lang=SCREAMING_SNAKE_CASE		,							output_type="dict"		,							config=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    data["text"], data["left"], data["top"], data["width"], data["height"]

     # filter empty words and corresponding coordinates
     _lowerCAmelCase   :					Dict        =    [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if not word.strip()]
     _lowerCAmelCase   :					Any        =    [word for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Dict        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[int]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     _lowerCAmelCase   :					Union[str, Any]        =    []
     for x, y, w, h in zip(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    [x, y, x + w, y + h]
          actual_boxes.append(SCREAMING_SNAKE_CASE				)

     # finally, normalize the bounding boxes
     _lowerCAmelCase   :					Dict        =    []
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)				)

     assert len(SCREAMING_SNAKE_CASE				) == len(SCREAMING_SNAKE_CASE				), "Not as many words as there are bounding boxes"

     return words, normalized_boxes





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = ""     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					int        =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Any        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Optional[int]        =    apply_ocr
      _lowerCAmelCase   :					Dict        =    ocr_lang
      _lowerCAmelCase   :					Optional[int]        =    tesseract_config

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Dict        =    (size["height"], size["width"])
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Dict     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[Any]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					Union[str, Any]        =    apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowerCAmelCase   :					Tuple        =    ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowerCAmelCase   :					Union[str, Any]        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

      if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Dict        =    [to_numpy_array(A_       ) for image in images]

      if apply_ocr:
           requires_backends(self     ,						"pytesseract"       )
           _lowerCAmelCase   :					Any        =    []
           _lowerCAmelCase   :					List[Any]        =    []
           for image in images:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    apply_tesseract(A_     ,						A_     ,						A_       )
                words_batch.append(A_       )
                boxes_batch.append(A_       )

      if do_resize:
           _lowerCAmelCase   :					Union[str, Any]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      _lowerCAmelCase   :					Optional[int]        =    [flip_channel_order(A_       ) for image in images]
      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    BatchFeature(data={"pixel_values": images}     ,						tensor_type=A_       )

      if apply_ocr:
           _lowerCAmelCase   :					Tuple        =    words_batch
           _lowerCAmelCase   :					Any        =    boxes_batch
      return data






