import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
      import tensorflow as tf

if is_torch_available():
      import torch


A_      =      logging.get_logger(__name__)






class      __lowercase					:

      def __init__(	self				:      str							,							__lowerCamelCase				:      str = None							,							__lowerCamelCase				:      uuid.UUID = None							,							__lowerCamelCase				:      Any=None							,							__lowerCamelCase				:      List[Any]=None   )							->		Dict:


         '''simple docstring'''

         if not conversation_id:
            lowercase							=		uuid.uuida()
         if past_user_inputs is None:
            lowercase							=		[]
         if generated_responses is None:
            lowercase							=		[]

         lowercase							=		conversation_id
         lowercase							=		past_user_inputs
         lowercase							=		generated_responses
         lowercase							=		text

      def __eq__(	self				:      List[str]							,							__lowerCamelCase				:      Optional[Any]   )							->		List[Any]:


         '''simple docstring'''

         if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
            return False
         if self.uuid == other.uuid:
            return True
         return (
             self.new_user_input == other.new_user_input
             and self.past_user_inputs == other.past_user_inputs
             and self.generated_responses == other.generated_responses
         )

      def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      bool = False   )							->		List[Any]:


         '''simple docstring'''

         if self.new_user_input:
            if overwrite:
               logger.warning(
                   f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
                   f'with: "{text}".'   )
               lowercase							=		text
            else:
               logger.warning(
                   f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
                   f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'   )
         else:
            lowercase							=		text

      def        __a		(	self				:      List[Any]   )							->		List[str]:


         '''simple docstring'''

         if self.new_user_input:
            self.past_user_inputs.append(self.new_user_input   )
         lowercase							=		None

      def        __a		(	self				:      str							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


         '''simple docstring'''

         self.generated_responses.append(__lowerCamelCase   )

      def        __a		(	self				:      Optional[Any]   )							->		List[str]:


         '''simple docstring'''

         for user_input, generated_response in zip(self.past_user_inputs							,							self.generated_responses   ):
            yield True, user_input
            yield False, generated_response
         if self.new_user_input:
            yield True, self.new_user_input



      def __repr__(	self				:      Any   )							->		Any:


         '''simple docstring'''

         lowercase							=		f'Conversation id: {self.uuid} \n'
         for is_user, text in self.iter_texts():
            lowercase							=		'''user''' if is_user else '''bot'''
            output += f'{name} >> {text} \n'
         return output






@add_end_docstrings(
    _A       ,     R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '       ,     )
class      __lowercase					(    _A    ):

      def __init__(	self				:      Dict							,							*__lowerCamelCase				:      List[Any]							,							**__lowerCamelCase				:      Optional[Any]   )							->		Optional[int]:


         '''simple docstring'''

         super().__init__(*__lowerCamelCase							,							**__lowerCamelCase   )
         if self.tokenizer.pad_token_id is None:
            lowercase							=		self.tokenizer.eos_token

      def        __a		(	self				:      str							,							__lowerCamelCase				:      int=None							,							__lowerCamelCase				:      Tuple=None							,							__lowerCamelCase				:      str=None							,							**__lowerCamelCase				:      Optional[Any]   )							->		Optional[Any]:


         '''simple docstring'''

         lowercase							=		{}
         lowercase							=		{}
         lowercase							=		{}

         if min_length_for_response is not None:
            lowercase							=		min_length_for_response
         if minimum_tokens is not None:
            lowercase							=		minimum_tokens

         if "max_length" in generate_kwargs:
            lowercase							=		generate_kwargs['''max_length''']
            # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
         if clean_up_tokenization_spaces is not None:
            lowercase							=		clean_up_tokenization_spaces

         if generate_kwargs:
            forward_params.update(__lowerCamelCase   )
         return preprocess_params, forward_params, postprocess_params

      def __call__(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[Conversation, List[Conversation]]							,							__lowerCamelCase				:      List[Any]=0							,							**__lowerCamelCase				:      Dict   )							->		str:


         '''simple docstring'''

         lowercase							=		super().__call__(__lowerCamelCase							,							num_workers=__lowerCamelCase							,							**__lowerCamelCase   )
         if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) and len(__lowerCamelCase   ) == 1:
            return outputs[0]
         return outputs

      def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Conversation							,							__lowerCamelCase				:      Optional[int]=32   )							->		Dict[str, Any]:


         '''simple docstring'''

         if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
            raise ValueError('''ConversationalPipeline, expects Conversation as inputs'''   )
         if conversation.new_user_input is None:
            raise ValueError(
                f'Conversation with UUID {type(conversation.uuid   )} does not contain new user input to process. '
                '''Add user inputs with the conversation\'s `add_user_input` method'''   )
         if hasattr(self.tokenizer							,							'''_build_conversation_input_ids'''   ):
            lowercase							=		self.tokenizer._build_conversation_input_ids(__lowerCamelCase   )
         else:
            # If the tokenizer cannot handle conversations, we default to only the old version
            lowercase							=		self._legacy_parse_and_tokenize(__lowerCamelCase   )

         if self.framework == "pt":
            lowercase							=		torch.LongTensor([input_ids]   )
         elif self.framework == "tf":
            lowercase							=		tf.constant([input_ids]   )
         return {"input_ids": input_ids, "conversation": conversation}

      def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]=10							,							**__lowerCamelCase				:      int   )							->		Any:


         '''simple docstring'''

         lowercase							=		generate_kwargs.get('''max_length'''							,							self.model.config.max_length   )

         lowercase							=		model_inputs['''input_ids'''].shape[1]
         if max_length - minimum_tokens < n:
            logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'   )
            lowercase							=		max_length - minimum_tokens
            lowercase							=		model_inputs['''input_ids'''][:, -trim:]
            if "attention_mask" in model_inputs:
               lowercase							=		model_inputs['''attention_mask'''][:, -trim:]
         lowercase							=		model_inputs.pop('''conversation'''   )
         lowercase							=		max_length
         lowercase							=		self.model.generate(**__lowerCamelCase							,							**__lowerCamelCase   )
         if self.model.config.is_encoder_decoder:
            lowercase							=		1
         else:
            lowercase							=		n
         return {"output_ids": output_ids[:, start_position:], "conversation": conversation}

      def        __a		(	self				:      Dict							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      int=True   )							->		str:


         '''simple docstring'''

         lowercase							=		model_outputs['''output_ids''']
         lowercase							=		self.tokenizer.decode(
             output_ids[0]							,							skip_special_tokens=__lowerCamelCase							,							clean_up_tokenization_spaces=__lowerCamelCase							,							)
         lowercase							=		model_outputs['''conversation''']
         conversation.mark_processed()
         conversation.append_response(__lowerCamelCase   )
         return conversation



      def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Conversation   )							->		Dict:


         '''simple docstring'''

         lowercase							=		self.tokenizer.eos_token_id
         lowercase							=		[]
         for is_user, text in conversation.iter_texts():
            if eos_token_id is not None:
               input_ids.extend(self.tokenizer.encode(__lowerCamelCase							,							add_special_tokens=__lowerCamelCase   ) + [eos_token_id]   )
            else:
               input_ids.extend(self.tokenizer.encode(__lowerCamelCase							,							add_special_tokens=__lowerCamelCase   )   )

         if len(__lowerCamelCase   ) > self.tokenizer.model_max_length:
            lowercase							=		input_ids[-self.tokenizer.model_max_length :]
         return input_ids






import math
import sys

import cva
import numpy as np






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		math.sqrt(UpperCAmelCase       )
			lowercase							=		1 / (sigma * math.sqrt(2 * math.pi       ))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros((kernel_size, kernel_size)       )
			for i in range(0,		UpperCAmelCase       ):
						for j in range(0,		UpperCAmelCase       ):
									lowercase							=		math.sqrt(
									    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
			return vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		)->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros(img.shape       )
			lowercase							=		get_gauss_kernel(UpperCAmelCase,		UpperCAmelCase       )
			lowercase		,lowercase							=		img.shape
			for i in range(kernel_size // 2,		size_x - kernel_size // 2       ):
						for j in range(kernel_size // 2,		size_y - kernel_size // 2       ):
									lowercase							=		get_slice(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		img_s - img_s[kernel_size // 2, kernel_size // 2]
									lowercase							=		vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.sum(UpperCAmelCase       ) / np.sum(UpperCAmelCase       )
									lowercase							=		val
			return imga






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple:



			"""simple docstring"""







			lowercase							=		args[1] if args[1:] else '''../image_data/lena.jpg'''
			lowercase							=		float(args[2]       ) if args[2:] else 1.0
			lowercase							=		float(args[3]       ) if args[3:] else 1.0
			if args[4:]:
						lowercase							=		int(args[4]       )
						lowercase							=		kernel_size + abs(kernel_size % 2 - 1       )
			else:
						lowercase							=		5
			return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
						A_    ,    A_    ,    A_    ,    A_      =      parse_args(sys.argv)
						A_      =      cva.imread(filename, 0)
						cva.imshow("input image", img)

						A_      =      img / 255
						A_      =      out.astype("float32")
						A_      =      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
						A_      =      out * 255
						A_      =      np.uinta(out)
						cva.imshow("output image", out)
						cva.waitKey(0)
						cva.destroyAllWindows()





