
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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=13	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=99	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.0	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=5_12	,      SCREAMING_SNAKE_CASE_=16	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=0.02	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    seq_length
       UpperCamelCase__				=    is_training
       UpperCamelCase__				=    use_input_mask
       UpperCamelCase__				=    use_token_type_ids
       UpperCamelCase__				=    use_labels
       UpperCamelCase__				=    vocab_size
       UpperCamelCase__				=    hidden_size
       UpperCamelCase__				=    num_hidden_layers
       UpperCamelCase__				=    num_attention_heads
       UpperCamelCase__				=    intermediate_multiple_size
       UpperCamelCase__				=    hidden_act
       UpperCamelCase__				=    hidden_dropout
       UpperCamelCase__				=    attention_dropout
       UpperCamelCase__				=    weight_tying
       UpperCamelCase__				=    max_position_embeddings
       UpperCamelCase__				=    type_vocab_size
       UpperCamelCase__				=    type_sequence_label_size
       UpperCamelCase__				=    initializer_range
       UpperCamelCase__				=    num_labels
       UpperCamelCase__				=    num_choices
       UpperCamelCase__				=    scope







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size						)

       UpperCamelCase__				=    None
       if self.use_input_mask:
            UpperCamelCase__				=    random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCamelCase__				=    None
       if self.use_labels:
            UpperCamelCase__				=    ids_tensor([self.batch_size, self.seq_length]	,      self.num_labels						)

       UpperCamelCase__				=    self.get_config()

       return config, input_ids, input_mask, token_labels







  def 	UpperCAmelCase_ (self						):
       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=SCREAMING_SNAKE_CASE_	,      initializer_range=self.initializer_range	,      )







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.prepare_config_and_inputs()

       UpperCamelCase__				=    True

       return config, input_ids, input_mask, token_labels







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    GPTNeoXJapaneseModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    True
       UpperCamelCase__				=    GPTNeoXJapaneseModel(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    True
       UpperCamelCase__				=    GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()

       # first forward pass
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      use_cache=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       UpperCamelCase__				=    ids_tensor((self.batch_size, 3)	,      config.vocab_size						)
       UpperCamelCase__				=    ids_tensor((self.batch_size, 3)	,      vocab_size=2						)

       # append to next input_ids and
       UpperCamelCase__				=    torch.cat([input_ids, next_tokens]	,      dim=-1						)
       UpperCamelCase__				=    torch.cat([input_mask, next_mask]	,      dim=-1						)

       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      output_hidden_states=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    output_from_no_past["""hidden_states"""][0]
       UpperCamelCase__				=    model(
           SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      past_key_values=SCREAMING_SNAKE_CASE_	,      output_hidden_states=SCREAMING_SNAKE_CASE_	,      )["""hidden_states"""][0]

       # select random slice
       UpperCamelCase__				=    ids_tensor((1,)	,      output_from_past.shape[-1]						).item()
       UpperCamelCase__				=    output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCamelCase__				=    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(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      atol=1E-3						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.prepare_config_and_inputs()
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    config_and_inputs
       UpperCamelCase__				=    {"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict






@require_torch
class      __A(    __lowerCamelCase    ,				__lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__         =      (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__         =      (
      {"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    GPTNeoXJapaneseModelTester(self						)
       UpperCamelCase__				=    ConfigTester(self	,      config_class=SCREAMING_SNAKE_CASE_	,      hidden_size=37						)







  def 	UpperCAmelCase_ (self						):
       self.config_tester.run_common_tests()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # This regression test was failing with PyTorch < 1.3
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_decoder()

       UpperCamelCase__				=    None

       self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE_						)







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """abeja/gpt-neox-japanese-2.7b"""

       UpperCamelCase__				=    ["""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、"""]

       UpperCamelCase__				=    [
           """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。""",
       ]

       UpperCamelCase__				=    GPTNeoXJapaneseTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    GPTNeoXJapaneseForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    []
       for prompt in prompts:
            UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						).input_ids
            UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_length=50						)
            UpperCamelCase__				=    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
            predicted_outputs += generated_string
       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
    from PIL import Image
else:




    class      __A:

      """simple docstring"""







      @staticmethod
      def 	UpperCAmelCase_ (*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
           pass






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




     UpperCamelCase__				=    hashlib.mda(image.tobytes()						)
     return m.hexdigest()[:10]






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




     UpperCamelCase__				=    np.array(__a						)
     UpperCamelCase__				=    npimg.shape
     return {"hash": hashimage(__a						), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class      __A(    unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  SCREAMING_SNAKE_CASE__         =      dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    MaskGenerationPipeline(model=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       pass







  @require_tf
  @unittest.skip("""Image segmentation not implemented in TF"""						)
  def 	UpperCAmelCase_ (self						):
       pass







  @slow
  @require_torch
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    pipeline("""mask-generation"""	,      model="""facebook/sam-vit-huge"""						)

       UpperCamelCase__				=    image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""	,      points_per_batch=2_56						)

       # Shortening by hashing
       UpperCamelCase__				=    []
       for i, o in enumerate(outputs["""masks"""]						):
            new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_						), "scores": outputs["scores"][i]}]

       # fmt: off
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE_	,      decimals=4						)	,      [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.021},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
               {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9967},
               {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.993},
               {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9909},
               {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9879},
               {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9834},
               {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9716},
               {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9612},
               {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9599},
               {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9552},
               {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9532},
               {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9516},
               {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9499},
               {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9483},
               {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9464},
               {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
               {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
               {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9408},
               {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9335},
               {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9326},
               {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9262},
               {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8999},
               {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8986},
               {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8984},
               {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8873},
               {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8871}
           ]	,      )
       # fmt: on







  @require_torch
  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """facebook/sam-vit-huge"""
       UpperCamelCase__				=    pipeline("""mask-generation"""	,      model=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    image_segmenter(
           """http://images.cocodataset.org/val2017/000000039769.jpg"""	,      pred_iou_thresh=1	,      points_per_batch=2_56						)

       # Shortening by hashing
       UpperCamelCase__				=    []
       for i, o in enumerate(outputs["""masks"""]						):
            new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_						), "scores": outputs["scores"][i]}]

       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE_	,      decimals=4						)	,      [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0210},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
           ]	,      )





