




'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a		:  int   			=			logging.get_logger(__name__)

a		:  Tuple   			=			{
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}




class     UpperCamelCase					(  A_					):
		a    :Dict      					=				'''mbart'''
		a    :Any      					=				['''past_key_values''']
		a    :Union[str, Any]      					=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



		def __init__( self		,    __lowerCAmelCase=5_0265		,    __lowerCAmelCase=1024		,    __lowerCAmelCase=12		,    __lowerCAmelCase=4096		,    __lowerCAmelCase=16		,    __lowerCAmelCase=12		,    __lowerCAmelCase=4096		,    __lowerCAmelCase=16		,    __lowerCAmelCase=0.0		,    __lowerCAmelCase=0.0		,    __lowerCAmelCase=True		,    __lowerCAmelCase=True		,    __lowerCAmelCase="gelu"		,    __lowerCAmelCase=1024		,    __lowerCAmelCase=0.1		,    __lowerCAmelCase=0.0		,    __lowerCAmelCase=0.0		,    __lowerCAmelCase=0.02		,    __lowerCAmelCase=0.0		,    __lowerCAmelCase=False		,    __lowerCAmelCase=1		,    __lowerCAmelCase=0		,    __lowerCAmelCase=2		,    __lowerCAmelCase=2		,    **__lowerCAmelCase		,    ):







						'''simple docstring'''


						_a							:       Optional[Any]       					=			vocab_size
						_a							:       Any       					=			max_position_embeddings
						_a							:       List[Any]       					=			d_model
						_a							:       Optional[int]       					=			encoder_ffn_dim
						_a							:       Union[str, Any]       					=			encoder_layers
						_a							:       Dict       					=			encoder_attention_heads
						_a							:       Any       					=			decoder_ffn_dim
						_a							:       Tuple       					=			decoder_layers
						_a							:       List[Any]       					=			decoder_attention_heads
						_a							:       Tuple       					=			dropout
						_a							:       Dict       					=			attention_dropout
						_a							:       str       					=			activation_dropout
						_a							:       int       					=			activation_function
						_a							:       Tuple       					=			init_std
						_a							:       str       					=			encoder_layerdrop
						_a							:       List[str]       					=			decoder_layerdrop
						_a							:       Dict       					=			classifier_dropout
						_a							:       Optional[Any]       					=			use_cache
						_a							:       Any       					=			encoder_layers
						_a							:       Tuple       					=			scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=__lowerCAmelCase		,    bos_token_id=__lowerCAmelCase		,    eos_token_id=__lowerCAmelCase		,    is_encoder_decoder=__lowerCAmelCase		,    forced_eos_token_id=__lowerCAmelCase		,    **__lowerCAmelCase		,    )






class     UpperCamelCase					(  A_					):



		@property
		def 						a_       ( self   ):







						'''simple docstring'''


						if self.task in ["default", "seq2seq-lm"]:
										_a							:       Dict       					=			OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										    ]   )

										if self.use_past:
														_a							:       str       					=			{0: """batch"""}
														_a							:       List[Any]       					=			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
														_a							:       int       					=			{0: """batch""", 1: """decoder_sequence"""}
														_a							:       Tuple       					=			{0: """batch""", 1: """decoder_sequence"""}

										if self.use_past:
														self.fill_with_past_key_values_(__lowerCAmelCase		,    direction="""inputs"""   )
						elif self.task == "causal-lm":
										# TODO: figure this case out.
										_a							:       Optional[Any]       					=			OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										    ]   )
										if self.use_past:
														_a					,   _a							:       int       					=			self.num_layers
														for i in range(__lowerCAmelCase   ):
																		_a							:       Optional[Any]       					=			{0: """batch""", 2: """past_sequence + sequence"""}
																		_a							:       Tuple       					=			{0: """batch""", 2: """past_sequence + sequence"""}
						else:
										_a							:       Dict       					=			OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
										        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
										    ]   )

						return common_inputs



		@property
		def 						a_       ( self   ):







						'''simple docstring'''


						if self.task in ["default", "seq2seq-lm"]:
										_a							:       Dict       					=			super().outputs
						else:
										_a							:       Dict       					=			super(__lowerCAmelCase		,    self   ).outputs
										if self.use_past:
														_a					,   _a							:       Optional[int]       					=			self.num_layers
														for i in range(__lowerCAmelCase   ):
																		_a							:       List[Any]       					=			{0: """batch""", 2: """past_sequence + sequence"""}
																		_a							:       int       					=			{0: """batch""", 2: """past_sequence + sequence"""}
						return common_outputs



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = False		,    __lowerCAmelCase = None		,    ):







						'''simple docstring'''


						_a							:       List[str]       					=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )

						# Generate decoder inputs
						_a							:       Any       					=			seq_length if not self.use_past else 1
						_a							:       List[Any]       					=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
						_a							:       Any       					=			{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						_a							:       List[Any]       					=			dict(**__lowerCAmelCase		,    **__lowerCAmelCase   )

						if self.use_past:
										if not is_torch_available():
														raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
										else:
														import torch
										_a					,   _a							:       List[Any]       					=			common_inputs["""input_ids"""].shape
										_a							:       List[Any]       					=			common_inputs["""decoder_input_ids"""].shape[1]
										_a					,   _a							:       Dict       					=			self.num_attention_heads
										_a							:       Tuple       					=			(
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										_a							:       Optional[int]       					=			decoder_seq_length + 3
										_a							:       Optional[int]       					=			(
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										_a							:       Optional[int]       					=			torch.cat(
										    [common_inputs["""decoder_attention_mask"""], torch.ones(__lowerCAmelCase		,    __lowerCAmelCase   )]		,    dim=1   )

										_a							:       Tuple       					=			[]
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										_a					,   _a							:       Dict       					=			self.num_layers
										_a							:       str       					=			min(__lowerCAmelCase		,    __lowerCAmelCase   )
										_a							:       int       					=			max(__lowerCAmelCase		,    __lowerCAmelCase   ) - min_num_layers
										_a							:       str       					=			"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

										for _ in range(__lowerCAmelCase   ):
														common_inputs["past_key_values"].append(
														    (
														        torch.zeros(__lowerCAmelCase   ),
														        torch.zeros(__lowerCAmelCase   ),
														        torch.zeros(__lowerCAmelCase   ),
														        torch.zeros(__lowerCAmelCase   ),
														    )   )
										# TODO: test this.
										_a							:       Dict       					=			encoder_shape if remaining_side_name == """encoder""" else decoder_shape
										for _ in range(__lowerCAmelCase		,    __lowerCAmelCase   ):
														common_inputs["past_key_values"].append((torch.zeros(__lowerCAmelCase   ), torch.zeros(__lowerCAmelCase   ))   )
						return common_inputs



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = False		,    __lowerCAmelCase = None		,    ):







						'''simple docstring'''


						_a							:       Tuple       					=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )

						if self.use_past:
										if not is_torch_available():
														raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
										else:
														import torch
										_a					,   _a							:       str       					=			common_inputs["""input_ids"""].shape
										# Not using the same length for past_key_values
										_a							:       Tuple       					=			seqlen + 2
										_a					,   _a							:       Optional[Any]       					=			self.num_layers
										_a					,   _a							:       Tuple       					=			self.num_attention_heads
										_a							:       Union[str, Any]       					=			(
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

										_a							:       str       					=			common_inputs["""attention_mask"""].dtype
										_a							:       Optional[Any]       					=			torch.cat(
										    [common_inputs["""attention_mask"""], torch.ones(__lowerCAmelCase		,    __lowerCAmelCase		,    dtype=__lowerCAmelCase   )]		,    dim=1   )
										_a							:       List[str]       					=			[
										    (torch.zeros(__lowerCAmelCase   ), torch.zeros(__lowerCAmelCase   )) for _ in range(__lowerCAmelCase   )
										]
						return common_inputs



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = False		,    __lowerCAmelCase = None		,    ):







						'''simple docstring'''


						_a							:       Optional[int]       					=			compute_effective_axis_dimension(
						    __lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_batch		,    num_token_to_add=0   )

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						_a							:       List[Any]       					=			tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
						_a							:       str       					=			compute_effective_axis_dimension(
						    __lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_sequence		,    num_token_to_add=__lowerCAmelCase   )

						# Generate dummy inputs according to compute batch and sequence
						_a							:       List[Any]       					=			[""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
						_a							:       Union[str, Any]       					=			dict(tokenizer(__lowerCAmelCase		,    return_tensors=__lowerCAmelCase   )   )
						return common_inputs



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = False		,    __lowerCAmelCase = None		,    ):







						'''simple docstring'''


						if self.task in ["default", "seq2seq-lm"]:
										_a							:       List[str]       					=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    __lowerCAmelCase		,    batch_size=__lowerCAmelCase		,    seq_length=__lowerCAmelCase		,    is_pair=__lowerCAmelCase		,    framework=__lowerCAmelCase   )

						elif self.task == "causal-lm":
										_a							:       Any       					=			self._generate_dummy_inputs_for_causal_lm(
										    __lowerCAmelCase		,    batch_size=__lowerCAmelCase		,    seq_length=__lowerCAmelCase		,    is_pair=__lowerCAmelCase		,    framework=__lowerCAmelCase   )
						else:
										_a							:       Optional[int]       					=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __lowerCAmelCase		,    batch_size=__lowerCAmelCase		,    seq_length=__lowerCAmelCase		,    is_pair=__lowerCAmelCase		,    framework=__lowerCAmelCase   )

						return common_inputs



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







						'''simple docstring'''


						if self.task in ["default", "seq2seq-lm"]:
										_a							:       Union[str, Any]       					=			super()._flatten_past_key_values_(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
						else:
										_a							:       List[str]       					=			super(__lowerCAmelCase		,    self   )._flatten_past_key_values_(
										    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )








'''simple docstring'''





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=3	):
    _a							:       Optional[Any]       					=			int(mask / 2	)
    # copy image size
    _a							:       Optional[int]       					=			zeros_like(UpperCAmelCase_	)
    for i in range(UpperCAmelCase_      ,  gray_img.shape[0] - bd	):
        for j in range(UpperCAmelCase_      ,  gray_img.shape[1] - bd	):
            # get mask according with mask
            _a							:       List[str]       					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _a							:       Optional[Any]       					=			sort(UpperCAmelCase_	)[inta(divide((multiply(UpperCAmelCase_      ,  UpperCAmelCase_	))      ,  2	) + 1	)]
            _a							:       int       					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  a		:  List[Any]   			=			imread('''../image_data/lena.jpg''')
  # turn image in gray scale value
  a		:  Optional[Any]   			=			cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

  # show result images
  imshow('''median filter with 3x3 mask''', medianaxa)
  imshow('''median filter with 5x5 mask''', medianaxa)
  waitKey(0)


