
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


_UpperCAmelCase      :				str          = logging.get_logger(__name__)


class        lowercase      :



		def __init__(				self       ,   A_ = None       ,   A_ = None       ,   A_=None       ,   A_=None )						->      str:
				"""simple docstring"""
				if not conversation_id:
						UpperCamelCase						=  uuid.uuida()
				if past_user_inputs is None:
						UpperCamelCase						=  []
				if generated_responses is None:
						UpperCamelCase						=  []

				UpperCamelCase						=  conversation_id
				UpperCamelCase						=  past_user_inputs
				UpperCamelCase						=  generated_responses
				UpperCamelCase						=  text



		def __eq__(				self       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				if not isinstance(A_       ,   A_ ):
						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 __UpperCamelCase				(				self       ,   A_       ,   A_ = False )						->      Dict:
				"""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}".''' )
								UpperCamelCase						=  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:
						UpperCamelCase						=  text



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				if self.new_user_input:
						self.past_user_inputs.append(self.new_user_input )
				UpperCamelCase						=  None



		def __UpperCamelCase				(				self       ,   A_ )						->      Any:
				"""simple docstring"""
				self.generated_responses.append(A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""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 )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  F'''Conversation id: {self.uuid} \n'''
				for is_user, text in self.iter_texts():
						UpperCamelCase						=  'user' if is_user else 'bot'
						output += F'''{name} >> {text} \n'''
				return output






@add_end_docstrings(
    _SCREAMING_SNAKE_CASE						,     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      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   *A_       ,   **A_ )						->      List[str]:
				"""simple docstring"""
				super().__init__(*A_       ,   **A_ )
				if self.tokenizer.pad_token_id is None:
						UpperCamelCase						=  self.tokenizer.eos_token



		def __UpperCamelCase				(				self       ,   A_=None       ,   A_=None       ,   A_=None       ,   **A_ )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  {}
				UpperCamelCase						=  {}
				UpperCamelCase						=  {}

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

				if "max_length" in generate_kwargs:
						UpperCamelCase						=  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:
						UpperCamelCase						=  clean_up_tokenization_spaces

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



		def __call__(				self       ,   A_       ,   A_=0       ,   **A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  super().__call__(A_       ,   num_workers=A_       ,   **A_ )
				if isinstance(A_       ,   A_ ) and len(A_ ) == 1:
						return outputs[0]
				return outputs



		def __UpperCamelCase				(				self       ,   A_       ,   A_=32 )						->      Dict[str, Any]:
				"""simple docstring"""
				if not isinstance(A_       ,   A_ ):
						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' ):
						UpperCamelCase						=  self.tokenizer._build_conversation_input_ids(A_ )
				else:
						# If the tokenizer cannot handle conversations, we default to only the old version
						UpperCamelCase						=  self._legacy_parse_and_tokenize(A_ )

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



		def __UpperCamelCase				(				self       ,   A_       ,   A_=10       ,   **A_ )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  generate_kwargs.get('max_length'       ,   self.model.config.max_length )

				UpperCamelCase						=  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})''' )
						UpperCamelCase						=  max_length - minimum_tokens
						UpperCamelCase						=  model_inputs['input_ids'][:, -trim:]
						if "attention_mask" in model_inputs:
								UpperCamelCase						=  model_inputs['attention_mask'][:, -trim:]
				UpperCamelCase						=  model_inputs.pop('conversation' )
				UpperCamelCase						=  max_length
				UpperCamelCase						=  self.model.generate(**A_       ,   **A_ )
				if self.model.config.is_encoder_decoder:
						UpperCamelCase						=  1
				else:
						UpperCamelCase						=  n
				return {"output_ids": output_ids[:, start_position:], "conversation": conversation}



		def __UpperCamelCase				(				self       ,   A_       ,   A_=True )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  model_outputs['output_ids']
				UpperCamelCase						=  self.tokenizer.decode(
				    output_ids[0]       ,   skip_special_tokens=A_       ,   clean_up_tokenization_spaces=A_       ,   )
				UpperCamelCase						=  model_outputs['conversation']
				conversation.mark_processed()
				conversation.append_response(A_ )
				return conversation



		def __UpperCamelCase				(				self       ,   A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.tokenizer.eos_token_id
				UpperCamelCase						=  []
				for is_user, text in conversation.iter_texts():
						if eos_token_id is not None:
								input_ids.extend(self.tokenizer.encode(A_       ,   add_special_tokens=A_ ) + [eos_token_id] )
						else:
								input_ids.extend(self.tokenizer.encode(A_       ,   add_special_tokens=A_ ) )

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

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       (DDPMScheduler,)



		def __UpperCamelCase				(				self       ,   **A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  {
				    'num_train_timesteps': 1_000,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'variance_type': 'fixed_small',
				    'clip_sample': True,
				}

				config.update(**A_ )
				return config



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				for timesteps in [1, 5, 100, 1_000]:
						self.check_over_configs(num_train_timesteps=A_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,   [0.002, 0.02, 0.2, 2] ):
						self.check_over_configs(beta_start=A_       ,   beta_end=A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				for clip_sample in [True, False]:
						self.check_over_configs(clip_sample=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				self.check_over_configs(thresholding=A_ )
				for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(
								    thresholding=A_       ,   prediction_type=A_       ,   sample_max_value=A_       ,   )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				for prediction_type in ["epsilon", "sample", "v_prediction"]:
						self.check_over_configs(prediction_type=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				for t in [0, 500, 999]:
						self.check_over_forward(time_step=A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

				UpperCamelCase						=  self.dummy_model()
				UpperCamelCase						=  self.dummy_sample_deter
				UpperCamelCase						=  torch.manual_seed(0 )

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

						# 2. predict previous mean of sample x_t-1
						UpperCamelCase						=  scheduler.step(A_       ,   A_       ,   A_       ,   generator=A_ ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 258.9606 ) < 1e-2
				assert abs(result_mean.item() - 0.3372 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config(prediction_type='v_prediction' )
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

				UpperCamelCase						=  self.dummy_model()
				UpperCamelCase						=  self.dummy_sample_deter
				UpperCamelCase						=  torch.manual_seed(0 )

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

						# 2. predict previous mean of sample x_t-1
						UpperCamelCase						=  scheduler.step(A_       ,   A_       ,   A_       ,   generator=A_ ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 202.0296 ) < 1e-2
				assert abs(result_mean.item() - 0.2631 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=A_ )

				UpperCamelCase						=  scheduler.timesteps

				for i, timestep in enumerate(A_ ):
						if i == len(A_ ) - 1:
								UpperCamelCase						=  -1
						else:
								UpperCamelCase						=  timesteps[i + 1]

						UpperCamelCase						=  scheduler.previous_timestep(A_ )
						UpperCamelCase						=  prev_t.item()

						self.assertEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 51, 0]

				with self.assertRaises(A_       ,   msg='`custom_timesteps` must be in descending order.' ):
						scheduler.set_timesteps(timesteps=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]
				UpperCamelCase						=  len(A_ )

				with self.assertRaises(A_       ,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
						scheduler.set_timesteps(num_inference_steps=A_       ,   timesteps=A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    A_       ,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,   ):
						scheduler.set_timesteps(timesteps=A_ )
