
"""simple docstring"""




import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import FlavaImageProcessor, FlavaProcessor
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )

@require_vision
class 		a__						(				unittest.TestCase						):
   def       __magic_name__      (      self    ):
          lowercase   :    List[str]			=							tempfile.mkdtemp()

          # fmt: off
          lowercase   :    Optional[Any]			=							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
          # fmt: on
          lowercase   :    Any			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]    )

          with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 fp.write("".join([x + "\n" for x in vocab_tokens]    )    )

          lowercase   :    Optional[Any]			=							{
              "image_mean": FLAVA_IMAGE_MEAN,
              "image_std": FLAVA_IMAGE_STD,
              "do_normalize": True,
              "do_resize": True,
              "size": 224,
              "do_center_crop": True,
              "crop_size": 224,
              "input_size_patches": 14,
              "total_mask_patches": 75,
              "mask_group_max_patches": None,
              "mask_group_min_patches": 16,
              "mask_group_min_aspect_ratio": 0.3,
              "mask_group_max_aspect_ratio": None,
              "codebook_do_resize": True,
              "codebook_size": 112,
              "codebook_do_center_crop": True,
              "codebook_crop_size": 112,
              "codebook_do_map_pixels": True,
              "codebook_do_normalize": True,
              "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
              "codebook_image_std": FLAVA_CODEBOOK_STD,
          }

          lowercase   :    Optional[Any]			=							os.path.join(self.tmpdirname     ,   _a    )
          with open(self.image_processor_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 json.dump(_a     ,   _a    )
   def       __magic_name__      (      self     ,   **_a    ):
          return BertTokenizer.from_pretrained(self.tmpdirname     ,   **_a    )
   def       __magic_name__      (      self     ,   **_a    ):
          return BertTokenizerFast.from_pretrained(self.tmpdirname     ,   **_a    )
   def       __magic_name__      (      self     ,   **_a    ):
          return FlavaImageProcessor.from_pretrained(self.tmpdirname     ,   **_a    )
   def       __magic_name__      (      self    ):
          shutil.rmtree(self.tmpdirname    )
   def       __magic_name__      (      self    ):
          lowercase   :    Dict			=							[np.random.randint(255     ,   size=(3, 30, 400)     ,   dtype=np.uinta    )]

          lowercase   :    Optional[int]			=							[Image.fromarray(np.moveaxis(_a     ,   0     ,   -1    )    ) for x in image_inputs]

          return image_inputs
   def       __magic_name__      (      self    ):
          lowercase   :    Tuple			=							self.get_tokenizer()
          lowercase   :    Union[str, Any]			=							self.get_rust_tokenizer()
          lowercase   :    int			=							self.get_image_processor()

          lowercase   :    Any			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )
          processor_slow.save_pretrained(self.tmpdirname    )
          lowercase   :    Union[str, Any]			=							FlavaProcessor.from_pretrained(self.tmpdirname     ,   use_fast=_a    )

          lowercase   :    Union[str, Any]			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )
          processor_fast.save_pretrained(self.tmpdirname    )
          lowercase   :    List[str]			=							FlavaProcessor.from_pretrained(self.tmpdirname    )

          self.assertEqual(processor_slow.tokenizer.get_vocab()     ,   tokenizer_slow.get_vocab()    )
          self.assertEqual(processor_fast.tokenizer.get_vocab()     ,   tokenizer_fast.get_vocab()    )
          self.assertEqual(tokenizer_slow.get_vocab()     ,   tokenizer_fast.get_vocab()    )
          self.assertIsInstance(processor_slow.tokenizer     ,   _a    )
          self.assertIsInstance(processor_fast.tokenizer     ,   _a    )

          self.assertEqual(processor_slow.image_processor.to_json_string()     ,   image_processor.to_json_string()    )
          self.assertEqual(processor_fast.image_processor.to_json_string()     ,   image_processor.to_json_string()    )
          self.assertIsInstance(processor_slow.image_processor     ,   _a    )
          self.assertIsInstance(processor_fast.image_processor     ,   _a    )
   def       __magic_name__      (      self    ):
          lowercase   :    List[str]			=							FlavaProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()    )
          processor.save_pretrained(self.tmpdirname    )

          lowercase   :    Optional[Any]			=							self.get_tokenizer(bos_token="(BOS)"     ,   eos_token="(EOS)"    )
          lowercase   :    Any			=							self.get_image_processor(do_normalize=_a     ,   padding_value=1.0    )

          lowercase   :    Tuple			=							FlavaProcessor.from_pretrained(
              self.tmpdirname     ,   bos_token="(BOS)"     ,   eos_token="(EOS)"     ,   do_normalize=_a     ,   padding_value=1.0    )

          self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()    )
          self.assertIsInstance(processor.tokenizer     ,   _a    )

          self.assertEqual(processor.image_processor.to_json_string()     ,   image_processor_add_kwargs.to_json_string()    )
          self.assertIsInstance(processor.image_processor     ,   _a    )
   def       __magic_name__      (      self    ):
          lowercase   :    List[Any]			=							self.get_image_processor()
          lowercase   :    List[str]			=							self.get_tokenizer()

          lowercase   :    Any			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

          lowercase   :    int			=							self.prepare_image_inputs()

          lowercase   :    Optional[int]			=							image_processor(_a     ,   return_tensors="np"    )
          lowercase   :    Optional[int]			=							processor(images=_a     ,   return_tensors="np"    )

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2    )

          # With rest of the args
          random.seed(1_234    )
          lowercase   :    Any			=							image_processor(
              _a     ,   return_image_mask=_a     ,   return_codebook_pixels=_a     ,   return_tensors="np"    )
          random.seed(1_234    )
          lowercase   :    int			=							processor(
              images=_a     ,   return_image_mask=_a     ,   return_codebook_pixels=_a     ,   return_tensors="np"    )

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2    )
   def       __magic_name__      (      self    ):
          lowercase   :    str			=							self.get_image_processor()
          lowercase   :    Optional[Any]			=							self.get_tokenizer()

          lowercase   :    Optional[Any]			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

          lowercase   :    Any			=							"lower newer"

          lowercase   :    List[str]			=							processor(text=_a    )

          lowercase   :    Dict			=							tokenizer(_a    )

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]    )
   def       __magic_name__      (      self    ):
          lowercase   :    str			=							self.get_image_processor()
          lowercase   :    Optional[Any]			=							self.get_tokenizer()

          lowercase   :    Tuple			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

          lowercase   :    Union[str, Any]			=							"lower newer"
          lowercase   :    str			=							self.prepare_image_inputs()

          lowercase   :    int			=							processor(text=_a     ,   images=_a    )

          self.assertListEqual(list(inputs.keys()    )     ,   ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]    )

          # add extra args
          lowercase   :    str			=							processor(text=_a     ,   images=_a     ,   return_codebook_pixels=_a     ,   return_image_mask=_a    )

          self.assertListEqual(
              list(inputs.keys()    )     ,   [
                  "input_ids",
                  "token_type_ids",
                  "attention_mask",
                  "pixel_values",
                  "codebook_pixel_values",
                  "bool_masked_pos",
              ]     ,   )

          # test if it raises when no input is passed
          with pytest.raises(_a    ):
                 processor()
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							self.get_image_processor()
          lowercase   :    List[Any]			=							self.get_tokenizer()

          lowercase   :    List[Any]			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

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

          lowercase   :    Any			=							processor.batch_decode(_a    )
          lowercase   :    List[Any]			=							tokenizer.batch_decode(_a    )

          self.assertListEqual(_a     ,   _a    )

   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							self.get_image_processor()
          lowercase   :    str			=							self.get_tokenizer()

          lowercase   :    List[str]			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

          lowercase   :    int			=							"lower newer"
          lowercase   :    Dict			=							self.prepare_image_inputs()

          lowercase   :    List[str]			=							processor(text=_a     ,   images=_a    )

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




"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A      :	List[Any]       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """graphormer"""
   __lowerCAmelCase						=    ["""past_key_values"""]
   def __init__(      self     ,   _a = 1     ,   _a = 512 * 9     ,   _a = 512 * 3     ,   _a = 512     ,   _a = 512     ,   _a = 512     ,   _a = 128     ,   _a = 5     ,   _a = 1_024     ,   _a = "multi_hop"     ,   _a = 512     ,   _a = False     ,   _a = 12     ,   _a = 768     ,   _a = 768     ,   _a = 32     ,   _a = 0.1     ,   _a = 0.1     ,   _a = 0.0     ,   _a = False     ,   _a = False     ,   _a = False     ,   _a = "gelu"     ,   _a = None     ,   _a = False     ,   _a = 0     ,   _a = False     ,   _a = 0.0     ,   _a = 8     ,   _a = None     ,   _a = None     ,   _a = True     ,   _a = True     ,   _a=0     ,   _a=1     ,   _a=2     ,   **_a     ,   ):
          lowercase   :    Any			=							num_classes
          lowercase   :    Optional[Any]			=							num_atoms
          lowercase   :    Optional[Any]			=							num_in_degree
          lowercase   :    Any			=							num_out_degree
          lowercase   :    Optional[int]			=							num_edges
          lowercase   :    Any			=							num_spatial
          lowercase   :    Any			=							num_edge_dis
          lowercase   :    str			=							edge_type
          lowercase   :    Union[str, Any]			=							multi_hop_max_dist
          lowercase   :    List[str]			=							spatial_pos_max
          lowercase   :    Dict			=							max_nodes
          lowercase   :    str			=							num_hidden_layers
          lowercase   :    Union[str, Any]			=							embedding_dim
          lowercase   :    Optional[int]			=							embedding_dim
          lowercase   :    int			=							ffn_embedding_dim
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    Optional[int]			=							dropout
          lowercase   :    Dict			=							attention_dropout
          lowercase   :    int			=							layerdrop
          lowercase   :    int			=							encoder_normalize_before
          lowercase   :    List[str]			=							pre_layernorm
          lowercase   :    Union[str, Any]			=							apply_graphormer_init
          lowercase   :    Tuple			=							activation_fn
          lowercase   :    Tuple			=							embed_scale
          lowercase   :    List[Any]			=							freeze_embeddings
          lowercase   :    List[Any]			=							num_trans_layers_to_freeze
          lowercase   :    str			=							share_input_output_embed
          lowercase   :    List[str]			=							traceable
          lowercase   :    str			=							q_noise
          lowercase   :    List[str]			=							qn_block_size

          # These parameters are here for future extensions
          # atm, the model only supports self attention
          lowercase   :    Optional[int]			=							kdim
          lowercase   :    str			=							vdim
          lowercase   :    List[Any]			=							self_attention
          lowercase   :    Any			=							bias

          super().__init__(
              pad_token_id=_a     ,   bos_token_id=_a     ,   eos_token_id=_a     ,   **_a     ,   )


