

"""simple docstring"""


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


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}





class    snake_case     (    UpperCAmelCase__  ):
				SCREAMING_SNAKE_CASE_       :				Any       				=  'mctct'

				def __init__(     self							:    Dict					,  UpperCamelCase__							:    Any=8_0_6_5					,  UpperCamelCase__							:    Optional[int]=1_5_3_6					,  UpperCamelCase__							:    Union[str, Any]=3_6					,  UpperCamelCase__							:    List[Any]=6_1_4_4					,  UpperCamelCase__							:    List[Any]=4					,  UpperCamelCase__							:    int=3_8_4					,  UpperCamelCase__							:    Dict=9_2_0					,  UpperCamelCase__							:    Tuple=1e-5					,  UpperCamelCase__							:    Tuple=0.3					,  UpperCamelCase__							:    int="relu"					,  UpperCamelCase__							:    Union[str, Any]=0.02					,  UpperCamelCase__							:    Dict=0.3					,  UpperCamelCase__							:    Dict=0.3					,  UpperCamelCase__							:    Optional[Any]=1					,  UpperCamelCase__							:    Union[str, Any]=0					,  UpperCamelCase__							:    int=2					,  UpperCamelCase__							:    Union[str, Any]=1					,  UpperCamelCase__							:    Optional[Any]=0.3					,  UpperCamelCase__							:    Tuple=1					,  UpperCamelCase__							:    Optional[Any]=(7,)					,  UpperCamelCase__							:    Any=(3,)					,  UpperCamelCase__							:    str=8_0					,  UpperCamelCase__							:    Tuple=1					,  UpperCamelCase__							:    int=None					,  UpperCamelCase__							:    List[Any]="sum"					,  UpperCamelCase__							:    int=False					,  **UpperCamelCase__							:    List[str]					,  )->       Union[str, Any]:



						'''simple docstring'''


						super().__init__(**_SCREAMING_SNAKE_CASE					,  pad_token_id=_SCREAMING_SNAKE_CASE					,  bos_token_id=_SCREAMING_SNAKE_CASE					,  eos_token_id=_SCREAMING_SNAKE_CASE)
						__lowerCAmelCase:	int   				=      vocab_size
						__lowerCAmelCase:	str   				=      hidden_size
						__lowerCAmelCase:	str   				=      num_hidden_layers
						__lowerCAmelCase:	Dict   				=      intermediate_size
						__lowerCAmelCase:	List[str]   				=      num_attention_heads
						__lowerCAmelCase:	List[Any]   				=      attention_head_dim
						__lowerCAmelCase:	Union[str, Any]   				=      max_position_embeddings
						__lowerCAmelCase:	Optional[int]   				=      layer_norm_eps
						__lowerCAmelCase:	int   				=      layerdrop
						__lowerCAmelCase:	Tuple   				=      hidden_act
						__lowerCAmelCase:	Optional[int]   				=      initializer_range
						__lowerCAmelCase:	str   				=      hidden_dropout_prob
						__lowerCAmelCase:	Optional[int]   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	Dict   				=      pad_token_id
						__lowerCAmelCase:	List[Any]   				=      bos_token_id
						__lowerCAmelCase:	List[str]   				=      eos_token_id
						__lowerCAmelCase:	Optional[int]   				=      conv_glu_dim
						__lowerCAmelCase:	Tuple   				=      conv_dropout
						__lowerCAmelCase:	Dict   				=      num_conv_layers
						__lowerCAmelCase:	Tuple   				=      input_feat_per_channel
						__lowerCAmelCase:	int   				=      input_channels
						__lowerCAmelCase:	str   				=      conv_channels
						__lowerCAmelCase:	Any   				=      ctc_loss_reduction
						__lowerCAmelCase:	Tuple   				=      ctc_zero_infinity

						# prevents config testing fail with exporting to json
						__lowerCAmelCase:	Optional[Any]   				=      list(_SCREAMING_SNAKE_CASE)
						__lowerCAmelCase:	str   				=      list(_SCREAMING_SNAKE_CASE)

						if len(self.conv_kernel) != self.num_conv_layers:
								raise ValueError(
								    "Configuration for convolutional module is incorrect. "
								    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
								    f"but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, "
								    f"`config.num_conv_layers = {self.num_conv_layers}`.")





"""simple docstring"""


from pathlib import Path

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      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
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

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

			# add all rotated images in a list
			__A						       =      [
			    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
			__A						       =      plt.figure(1)
			__A						       =      ["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()


