



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


_lowercase: Optional[Any]      			=		logging.get_logger(__name__)



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		):
						"""simple docstring"""

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

						a					     =   conversation_id
						a					     =   past_user_inputs
						a					     =   generated_responses
						a					     =   text




	def __eq__(self		,       lowerCamelCase_		):
						"""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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = False		):
						"""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}".'''		)
																a					     =   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:
											a					     =   text




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						self.generated_responses.append(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""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		):
						"""simple docstring"""

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




@add_end_docstrings(
    lowerCAmelCase,      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							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}
						a					     =   {}

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

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

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




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_=0		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=32		):
						"""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"		):
											a					     =   self.tokenizer._build_conversation_input_ids(lowerCamelCase_		)
						else:
											# If the tokenizer cannot handle conversations, we default to only the old version
											a					     =   self._legacy_parse_and_tokenize(lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=10		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   generate_kwargs.get("max_length"		,       self.model.config.max_length		)

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=True		):
						"""simple docstring"""

						a					     =   model_outputs["output_ids"]
						a					     =   self.tokenizer.decode(
						    output_ids[0]		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		,       )
						a					     =   model_outputs["conversation"]
						conversation.mark_processed()
						conversation.append_response(lowerCamelCase_		)
						return conversation




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.tokenizer.eos_token_id
						a					     =   []
						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:
											a					     =   input_ids[-self.tokenizer.model_max_length :]
						return input_ids





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							(DDPMScheduler,)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "variance_type": "fixed_small",
						    "clip_sample": True,
						}

						config.update(**lowerCamelCase_		)
						return config




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""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=lowerCamelCase_		,       beta_end=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.check_over_configs(thresholding=lowerCamelCase_		)
						for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase_		,       prediction_type=lowerCamelCase_		,       sample_max_value=lowerCamelCase_		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for t in [0, 500, 999]:
											self.check_over_forward(time_step=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						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		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

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

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

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

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

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config(prediction_type="v_prediction"		)
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

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

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

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

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

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

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

						scheduler.set_timesteps(timesteps=lowerCamelCase_		)

						a					     =   scheduler.timesteps

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

											a					     =   scheduler.previous_timestep(lowerCamelCase_		)
											a					     =   prev_t.item()

											self.assertEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]
						a					     =   len(lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [scheduler.config.num_train_timesteps]

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