


from typing import Callable, Optional, Union

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


_snake_case					=					logging.get_logger(__name__)

_snake_case					=					{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}
class      UpperCAmelCase_ ( _UpperCAmelCase   ):

   '''simple docstring'''




   __A			:				Tuple							=		"prophetnet"
   __A			:				int							=		["past_key_values"]
   __A			:				List[str]							=		{
       "num_attention_heads": "num_encoder_attention_heads",
   }






   def __init__(		self						,    __A = 0.1						,    __A = "gelu"						,    __A = 3_0522						,    __A = 1024						,    __A = 4096						,    __A = 12						,    __A = 16						,    __A = 4096						,    __A = 12						,    __A = 16						,    __A = 0.1						,    __A = 0.1						,    __A = 512						,    __A = 0.02						,    __A = True						,    __A = True						,    __A = 0						,    __A = 2						,    __A = 32						,    __A = 128						,    __A = False						,    __A = 0.0						,    __A = True						,    __A = 0						,    __A = 1						,    __A = 2						,    **__A						,    ):






     """simple docstring"""





     lowerCamelCase			: str   =    vocab_size
     lowerCamelCase			: Optional[Any]   =    hidden_size
     lowerCamelCase			: List[str]   =    encoder_ffn_dim
     lowerCamelCase			: str   =    num_encoder_layers
     lowerCamelCase			: Dict   =    num_encoder_attention_heads
     lowerCamelCase			: str   =    decoder_ffn_dim
     lowerCamelCase			: Tuple   =    num_decoder_layers
     lowerCamelCase			: int   =    num_decoder_attention_heads
     lowerCamelCase			: int   =    max_position_embeddings
     lowerCamelCase			: str   =    init_std  # Normal(0, this parameter)
     lowerCamelCase			: str   =    activation_function

     # parameters for prophetnet
     lowerCamelCase			: str   =    ngram
     lowerCamelCase			: Optional[int]   =    num_buckets
     lowerCamelCase			: Union[str, Any]   =    relative_max_distance
     lowerCamelCase			: Any   =    disable_ngram_loss
     lowerCamelCase			: Any   =    eps

     # 3 Types of Dropout
     lowerCamelCase			: Optional[int]   =    attention_dropout
     lowerCamelCase			: Any   =    activation_dropout
     lowerCamelCase			: str   =    dropout

     lowerCamelCase			: Dict   =    use_cache

     super().__init__(
         pad_token_id=lowercase__						,    bos_token_id=lowercase__						,    eos_token_id=lowercase__						,    is_encoder_decoder=lowercase__						,    add_cross_attention=lowercase__						,    decoder_start_token_id=lowercase__						,    **lowercase__						,    )






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     return self.num_encoder_layers + self.num_decoder_layers






   @num_hidden_layers.setter
   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     raise NotImplementedError(
         "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
         " `num_decoder_layers`."			)







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
@require_vision
class      UpperCAmelCase_ ( unittest.TestCase   ):

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    tempfile.mkdtemp()

     lowerCamelCase			: List[Any]   =    CLIPImageProcessor()
     lowerCamelCase			: str   =    BertTokenizer.from_pretrained(
         "hf-internal-testing/tiny-random-BertModel"						,    model_input_names=["input_ids", "attention_mask"]			)

     lowerCamelCase			: List[str]   =    GitProcessor(__A						,    __A			)

     processor.save_pretrained(self.tmpdirname			)






   def _snake_case   (		self						,    **__A			):






     """simple docstring"""





     return AutoProcessor.from_pretrained(self.tmpdirname						,    **__A			).tokenizer






   def _snake_case   (		self						,    **__A			):






     """simple docstring"""





     return AutoProcessor.from_pretrained(self.tmpdirname						,    **__A			).image_processor






   def _snake_case   (		self			):






     """simple docstring"""





     shutil.rmtree(self.tmpdirname			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    [np.random.randint(255						,    size=(3, 30, 400)						,    dtype=np.uinta			)]

     lowerCamelCase			: Any   =    [Image.fromarray(np.moveaxis(__A						,    0						,    -1			)			) for x in image_inputs]

     return image_inputs






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    GitProcessor(tokenizer=self.get_tokenizer()						,    image_processor=self.get_image_processor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase			: Optional[Any]   =    self.get_tokenizer(bos_token="(BOS)"						,    eos_token="(EOS)"			)
     lowerCamelCase			: List[str]   =    self.get_image_processor(do_normalize=__A						,    padding_value=1.0			)

     lowerCamelCase			: Dict   =    GitProcessor.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 _snake_case   (		self			):






     """simple docstring"""





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

     lowerCamelCase			: Optional[Any]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

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

     lowerCamelCase			: str   =    image_processor(__A						,    return_tensors="np"			)
     lowerCamelCase			: Union[str, Any]   =    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			)






   def _snake_case   (		self			):






     """simple docstring"""





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

     lowerCamelCase			: Optional[int]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: int   =    "lower newer"

     lowerCamelCase			: str   =    processor(text=__A			)

     lowerCamelCase			: Union[str, Any]   =    tokenizer(__A						,    return_token_type_ids=__A			)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.get_image_processor()
     lowerCamelCase			: Optional[int]   =    self.get_tokenizer()

     lowerCamelCase			: List[str]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: str   =    "lower newer"
     lowerCamelCase			: Tuple   =    self.prepare_image_inputs()

     lowerCamelCase			: str   =    processor(text=__A						,    images=__A			)

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

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






   def _snake_case   (		self			):






     """simple docstring"""





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

     lowerCamelCase			: Tuple   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: Optional[Any]   =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase			: str   =    processor.batch_decode(__A			)
     lowerCamelCase			: int   =    tokenizer.batch_decode(__A			)

     self.assertListEqual(__A						,    __A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    self.get_image_processor()
     lowerCamelCase			: int   =    self.get_tokenizer()

     lowerCamelCase			: Optional[int]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: Optional[Any]   =    "lower newer"
     lowerCamelCase			: List[Any]   =    self.prepare_image_inputs()

     lowerCamelCase			: Optional[int]   =    processor(text=__A						,    images=__A			)

     # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
     self.assertListEqual(list(inputs.keys()			)						,    ["input_ids", "attention_mask", "pixel_values"]			)



