


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

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


__UpperCAmelCase : Optional[int] 			=				logging.get_logger(__name__)

__UpperCAmelCase : Optional[int] 			=				{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class    UpperCAmelCase_	(							SCREAMING_SNAKE_CASE__):
       '''simple docstring'''
       __UpperCamelCase      :     Any							=					"blenderbot-small"
       __UpperCamelCase      :     int							=					["past_key_values"]
       __UpperCamelCase      :     Union[str, Any]							=					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=50_265 ,					__SCREAMING_SNAKE_CASE=512 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=16 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=16 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=512 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=0 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=2 ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		vocab_size
              UpperCamelCase    :	Dict   	=		max_position_embeddings
              UpperCamelCase    :	Union[str, Any]   	=		d_model
              UpperCamelCase    :	List[str]   	=		encoder_ffn_dim
              UpperCamelCase    :	Any   	=		encoder_layers
              UpperCamelCase    :	Optional[Any]   	=		encoder_attention_heads
              UpperCamelCase    :	int   	=		decoder_ffn_dim
              UpperCamelCase    :	List[str]   	=		decoder_layers
              UpperCamelCase    :	str   	=		decoder_attention_heads
              UpperCamelCase    :	str   	=		dropout
              UpperCamelCase    :	List[str]   	=		attention_dropout
              UpperCamelCase    :	Any   	=		activation_dropout
              UpperCamelCase    :	Optional[int]   	=		activation_function
              UpperCamelCase    :	int   	=		init_std
              UpperCamelCase    :	List[str]   	=		encoder_layerdrop
              UpperCamelCase    :	Dict   	=		decoder_layerdrop
              UpperCamelCase    :	List[Any]   	=		use_cache
              UpperCamelCase    :	Optional[Any]   	=		encoder_layers
              UpperCamelCase    :	Any   	=		scale_embedding  # scale factor will be sqrt(d_model) if True

              super().__init__(
                  pad_token_id=_lowercase ,					bos_token_id=_lowercase ,					eos_token_id=_lowercase ,					is_encoder_decoder=_lowercase ,					decoder_start_token_id=_lowercase ,					forced_eos_token_id=_lowercase ,					**_lowercase ,					)




class    UpperCAmelCase_	(							SCREAMING_SNAKE_CASE__):
       '''simple docstring'''






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




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

                     if self.use_past:
                            UpperCamelCase    :	int   	=		{0: """batch"""}
                            UpperCamelCase    :	str   	=		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
                     else:
                            UpperCamelCase    :	Union[str, Any]   	=		{0: """batch""", 1: """decoder_sequence"""}
                            UpperCamelCase    :	Optional[Any]   	=		{0: """batch""", 1: """decoder_sequence"""}

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




              """simple docstring"""




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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = -1 ,					__SCREAMING_SNAKE_CASE = -1 ,					__SCREAMING_SNAKE_CASE = False ,					__SCREAMING_SNAKE_CASE = None ,					):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  _lowercase ,					_lowercase ,					_lowercase ,					_lowercase ,					_lowercase						)

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

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

                     UpperCamelCase    :	Optional[Any]   	=		torch.cat(
                         [common_inputs['''decoder_attention_mask'''], torch.ones(_lowercase ,					_lowercase						)] ,					dim=1						)

                     UpperCamelCase    :	Optional[Any]   	=		[]
                     # If the number of encoder and decoder layers are present in the model configuration, both are considered
                     UpperCamelCase    :	List[str]   	=		self.num_layers
                     UpperCamelCase    :	List[str]   	=		min(_lowercase ,					_lowercase						)
                     UpperCamelCase    :	Optional[Any]   	=		max(_lowercase ,					_lowercase						) - min_num_layers
                     UpperCamelCase    :	Optional[Any]   	=		"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = -1 ,					__SCREAMING_SNAKE_CASE = -1 ,					__SCREAMING_SNAKE_CASE = False ,					__SCREAMING_SNAKE_CASE = None ,					):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  _lowercase ,					_lowercase ,					_lowercase ,					_lowercase ,					_lowercase						)

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

                     UpperCamelCase    :	str   	=		common_inputs["""attention_mask"""].dtype
                     UpperCamelCase    :	Tuple   	=		torch.cat(
                         [common_inputs['''attention_mask'''], torch.ones(_lowercase ,					_lowercase ,					dtype=_lowercase						)] ,					dim=1						)
                     UpperCamelCase    :	int   	=		[
                         (torch.zeros(_lowercase						), torch.zeros(_lowercase						)) for _ in range(_lowercase						)
                     ]
              return common_inputs






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = -1 ,					__SCREAMING_SNAKE_CASE = -1 ,					__SCREAMING_SNAKE_CASE = False ,					__SCREAMING_SNAKE_CASE = None ,					):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		compute_effective_axis_dimension(
                  _lowercase ,					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
              UpperCamelCase    :	Any   	=		tokenizer.num_special_tokens_to_add(_lowercase						)
              UpperCamelCase    :	Dict   	=		compute_effective_axis_dimension(
                  _lowercase ,					fixed_dimension=OnnxConfig.default_fixed_sequence ,					num_token_to_add=_lowercase						)

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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = -1 ,					__SCREAMING_SNAKE_CASE = -1 ,					__SCREAMING_SNAKE_CASE = False ,					__SCREAMING_SNAKE_CASE = None ,					):




              """simple docstring"""




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

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

              return common_inputs






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase    :	Union[str, Any]   	=		super()._flatten_past_key_values_(_lowercase ,					_lowercase ,					_lowercase ,					_lowercase						)
              else:
                     UpperCamelCase    :	int   	=		super(_lowercase ,					self						)._flatten_past_key_values_(
                         _lowercase ,					_lowercase ,					_lowercase ,					_lowercase						)





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		cva.getAffineTransform(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       return cva.warpAffine(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			(rows, cols)       )


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Tuple 			=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __UpperCAmelCase : int 			=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __UpperCAmelCase			,    __UpperCAmelCase : Tuple 			=				gray_img.shape

  # set different points to rotate image
  __UpperCAmelCase : Optional[int] 			=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __UpperCAmelCase : Optional[int] 			=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __UpperCAmelCase : Any 			=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __UpperCAmelCase : int 			=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __UpperCAmelCase : Union[str, Any] 			=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __UpperCAmelCase : List[str] 			=				plt.figure(1)
  __UpperCAmelCase : Dict 			=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
  plt.show()

