

from typing import Callable, Optional, Union

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    '''microsoft/xprophetnet-large-wiki100-cased''': (
        '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
    ),
}

class 			a					(		a_			):
      _lowercase        =     "xlm-prophetnet"
      _lowercase        =     ["past_key_values"]
      _lowercase        =     {
          "num_attention_heads": "num_encoder_attention_heads",
      }


      def __init__(    self  ,   A_ = 0.1  ,   A_ = "gelu"  ,   A_ = 30522  ,   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'''


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

          # parameters for xlmprophetnet
          _UpperCAmelCase    :						Dict							=      ngram
          _UpperCAmelCase    :						List[Any]							=      num_buckets
          _UpperCAmelCase    :						Dict							=      relative_max_distance
          _UpperCAmelCase    :						Any							=      disable_ngram_loss
          _UpperCAmelCase    :						str							=      eps

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

          _UpperCAmelCase    :						str							=      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     _UpperCAmelCase			(    self		):







          '''simple docstring'''


          return self.num_encoder_layers + self.num_decoder_layers





      @num_hidden_layers.setter
      def     _UpperCAmelCase			(    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 warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class 			a					(		UpperCAmelCase			):
						_lowercase        =     ["image_processor", "tokenizer"]
						_lowercase        =     "OwlViTImageProcessor"
						_lowercase        =     ("CLIPTokenizer", "CLIPTokenizerFast")


						def __init__(    self  ,   A_=None  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,   A_  ,   )
														_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("feature_extractor"		)

										_UpperCAmelCase    :						Any							=      image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."		)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."		)

										super().__init__(A_  ,   A_		)


						def __call__(    self  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_="max_length"  ,   A_="np"  ,   **A_		):







										'''simple docstring'''


										if text is None and query_images is None and images is None:
														raise ValueError(
														    "You have to specify at least one text or query image or image. All three cannot be none."		)

										if text is not None:
														if isinstance(A_  ,   A_		) or (isinstance(A_  ,   A_		) and not isinstance(text[0]  ,   A_		)):
																		_UpperCAmelCase    :						Optional[int]							=      [self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)]

														elif isinstance(A_  ,   A_		) and isinstance(text[0]  ,   A_		):
																		_UpperCAmelCase    :						Optional[int]							=      []

																		# Maximum number of queries across batch
																		_UpperCAmelCase    :						Optional[Any]							=      max([len(A_		) for t in text]		)

																		# Pad all batch samples to max number of text queries
																		for t in text:
																						if len(A_		) != max_num_queries:
																										_UpperCAmelCase    :						Optional[int]							=      t + [" "] * (max_num_queries - len(A_		))

																						_UpperCAmelCase    :						str							=      self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)
																						encodings.append(A_		)
														else:
																		raise TypeError("Input text should be a string, a list of strings or a nested list of strings"		)

														if return_tensors == "np":
																		_UpperCAmelCase    :						List[str]							=      np.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Tuple							=      np.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "jax" and is_flax_available():
																		import jax.numpy as jnp

																		_UpperCAmelCase    :						Optional[Any]							=      jnp.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						str							=      jnp.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "pt" and is_torch_available():
																		import torch

																		_UpperCAmelCase    :						str							=      torch.cat([encoding["input_ids"] for encoding in encodings]  ,   dim=0		)
																		_UpperCAmelCase    :						Dict							=      torch.cat([encoding["attention_mask"] for encoding in encodings]  ,   dim=0		)

														elif return_tensors == "tf" and is_tf_available():
																		import tensorflow as tf

																		_UpperCAmelCase    :						Union[str, Any]							=      tf.stack([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Optional[int]							=      tf.stack([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														else:
																		raise ValueError("Target return tensor type could not be returned"		)

														_UpperCAmelCase    :						Optional[int]							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      input_ids
														_UpperCAmelCase    :						Optional[Any]							=      attention_mask

										if query_images is not None:
														_UpperCAmelCase    :						int							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      self.image_processor(
														    A_  ,   return_tensors=A_  ,   **A_		).pixel_values
														_UpperCAmelCase    :						Optional[Any]							=      query_pixel_values

										if images is not None:
														_UpperCAmelCase    :						int							=      self.image_processor(A_  ,   return_tensors=A_  ,   **A_		)

										if text is not None and images is not None:
														_UpperCAmelCase    :						Optional[int]							=      image_features.pixel_values
														return encoding
										elif query_images is not None and images is not None:
														_UpperCAmelCase    :						Any							=      image_features.pixel_values
														return encoding
										elif text is not None or query_images is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**A_		)  ,   tensor_type=A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_object_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_image_guided_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.decode(*A_  ,   **A_		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,   A_  ,   )
										return self.image_processor_class





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,   A_  ,   )
										return self.image_processor


