
from abc import ABC, abstractmethod
from typing import List, Optional


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self       )  ->					Tuple:
				"""simple docstring"""
				# test for the above condition
				self.test()



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     0
				UpperCamelCase		=     False
				while not completed:
						if counter == 1:
								self.reset()
						UpperCamelCase		=     self.advance()
						if not self.does_advance(A_       ):
								raise Exception(
								    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'       )

						UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     self.update(A_       )
						counter += 1

						if counter > 10_000:
								raise Exception('update() does not fulfill the constraint.'       )

				if self.remaining() != 0:
						raise Exception('Custom Constraint is not defined correctly.'       )



		@abstractmethod
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''       )



		@abstractmethod
		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''       )



		@abstractmethod
		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''       )



		@abstractmethod
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''       )



		@abstractmethod
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''       )



		@abstractmethod
		def __UpperCamelCase ( self				,      A_=False       )  ->					List[str]:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''       )






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_       )  ->					str:
				"""simple docstring"""
				super(A_				,      self       ).__init__()

				if not isinstance(A_				,      A_       ) or len(A_       ) == 0:
						raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.'''       )
				if any((not isinstance(A_				,      A_       ) or token_id < 0) for token_id in token_ids       ):
						raise ValueError(F'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'''       )

				UpperCamelCase		=     token_ids

				UpperCamelCase		=     len(self.token_ids       )
				UpperCamelCase		=     -1  # the index of the currently fulfilled step
				UpperCamelCase		=     False



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				if self.completed:
						return None
				return self.token_ids[self.fulfilled_idx + 1]



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				if not isinstance(A_				,      A_       ):
						raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(A_       )}'''       )

				if self.completed:
						return False

				return token_id == self.token_ids[self.fulfilled_idx + 1]



		def __UpperCamelCase ( self				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				if not isinstance(A_				,      A_       ):
						raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(A_       )}'''       )

				UpperCamelCase		=     False
				UpperCamelCase		=     False
				UpperCamelCase		=     False

				if self.does_advance(A_       ):
						self.fulfilled_idx += 1
						UpperCamelCase		=     True
						if self.fulfilled_idx == (self.seqlen - 1):
								UpperCamelCase		=     True
						UpperCamelCase		=     completed
				else:
						# failed to make progress.
						UpperCamelCase		=     True
						self.reset()
				return stepped, completed, reset



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     False
				UpperCamelCase		=     0



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				return self.seqlen - (self.fulfilled_idx + 1)



		def __UpperCamelCase ( self				,      A_=False       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     PhrasalConstraint(self.token_ids       )

				if stateful:
						UpperCamelCase		=     self.seqlen
						UpperCamelCase		=     self.fulfilled_idx
						UpperCamelCase		=     self.completed

				return new_constraint






class 				lowercase					:



		def __init__( self				,      A_				,      A_=True       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     max([len(A_       ) for one in nested_token_ids]       )

				UpperCamelCase		=     {}
				for token_ids in nested_token_ids:
						UpperCamelCase		=     root
						for tidx, token_id in enumerate(A_       ):
								if token_id not in level:
										UpperCamelCase		=     {}

								UpperCamelCase		=     level[token_id]

				if no_subsets and self.has_subsets(A_				,      A_       ):
						raise ValueError(
						    'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
						    F''' {nested_token_ids}.'''       )

				UpperCamelCase		=     root



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     self.trie

				for current_token in current_seq:
						UpperCamelCase		=     start[current_token]

				UpperCamelCase		=     list(start.keys()       )

				return next_tokens



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     self.next_tokens(A_       )

				return len(A_       ) == 0



		def __UpperCamelCase ( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     list(root.values()       )
				if len(A_       ) == 0:
						return 1
				else:
						return sum([self.count_leaves(A_       ) for nn in next_nodes]       )



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.count_leaves(A_       )
				return len(A_       ) != leaf_count






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				super(A_				,      self       ).__init__()

				if not isinstance(A_				,      A_       ) or len(A_       ) == 0:
						raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'''       )
				if any(not isinstance(A_				,      A_       ) for token_ids in nested_token_ids       ):
						raise ValueError(F'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'''       )
				if any(
				    any((not isinstance(A_				,      A_       ) or token_id < 0) for token_id in token_ids       )
				    for token_ids in nested_token_ids       ):
						raise ValueError(
						    F'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'''       )

				UpperCamelCase		=     DisjunctiveTrie(A_       )
				UpperCamelCase		=     nested_token_ids

				UpperCamelCase		=     self.trie.max_height
				UpperCamelCase		=     []
				UpperCamelCase		=     False



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.trie.next_tokens(self.current_seq       )

				if len(A_       ) == 0:
						return None
				else:
						return token_list



		def __UpperCamelCase ( self				,      A_       )  ->					Any:
				"""simple docstring"""
				if not isinstance(A_				,      A_       ):
						raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_       )}'''       )

				UpperCamelCase		=     self.trie.next_tokens(self.current_seq       )

				return token_id in next_tokens



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				if not isinstance(A_				,      A_       ):
						raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_       )}'''       )

				UpperCamelCase		=     False
				UpperCamelCase		=     False
				UpperCamelCase		=     False

				if self.does_advance(A_       ):
						self.current_seq.append(A_       )
						UpperCamelCase		=     True
				else:
						UpperCamelCase		=     True
						self.reset()

				UpperCamelCase		=     self.trie.reached_leaf(self.current_seq       )
				UpperCamelCase		=     completed

				return stepped, completed, reset



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     False
				UpperCamelCase		=     []



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				if self.completed:
						# since this can be completed without reaching max height
						return 0
				else:
						return self.seqlen - len(self.current_seq       )



		def __UpperCamelCase ( self				,      A_=False       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     DisjunctiveConstraint(self.token_ids       )

				if stateful:
						UpperCamelCase		=     self.seqlen
						UpperCamelCase		=     self.current_seq
						UpperCamelCase		=     self.completed

				return new_constraint






class 				lowercase					:



		def __init__( self				,      A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     constraints

				# max # of steps required to fulfill a given constraint
				UpperCamelCase		=     max([c.seqlen for c in constraints]       )
				UpperCamelCase		=     len(A_       )
				UpperCamelCase		=     False

				self.init_state()



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     []
				UpperCamelCase		=     None
				UpperCamelCase		=     [constraint.copy(stateful=A_       ) for constraint in self.constraints]



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     0
				if self.inprogress_constraint:
						# extra points for having a constraint mid-fulfilled
						add += self.max_seqlen - self.inprogress_constraint.remaining()

				return (len(self.complete_constraints       ) * self.max_seqlen) + add



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     []
				if self.inprogress_constraint is None:
						for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
								UpperCamelCase		=     constraint.advance()
								if isinstance(A_				,      A_       ):
										token_list.append(A_       )
								elif isinstance(A_				,      A_       ):
										token_list.extend(A_       )
				else:
						UpperCamelCase		=     self.inprogress_constraint.advance()
						if isinstance(A_				,      A_       ):
								token_list.append(A_       )
						elif isinstance(A_				,      A_       ):
								token_list.extend(A_       )

				if len(A_       ) == 0:
						return None
				else:
						return token_list



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

				if token_ids is not None:
						for token in token_ids:
								# completes or steps **one** constraint
								UpperCamelCase	,  UpperCamelCase		=     self.add(A_       )

								# the entire list of constraints are fulfilled
								if self.completed:
										break



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				if not isinstance(A_				,      A_       ):
						raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.'''       )

				UpperCamelCase	,  UpperCamelCase		=     False, False

				if self.completed:
						UpperCamelCase		=     True
						UpperCamelCase		=     False
						return complete, stepped

				if self.inprogress_constraint is not None:
						# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
						# job, simply update the state

						UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     self.inprogress_constraint.update(A_       )
						if reset:
								# 1. If the next token breaks the progress, then we must restart.
								#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

								#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
								#     constraint, not the full list of constraints.

								self.pending_constraints.append(self.inprogress_constraint.copy(stateful=A_       )       )
								UpperCamelCase		=     None

						if complete:
								# 2. If the next token completes the constraint, move it to completed list, set
								#     inprogress to None. If there are no pending constraints either, then this full list of constraints
								#     is complete.

								self.complete_constraints.append(self.inprogress_constraint       )
								UpperCamelCase		=     None

								if len(self.pending_constraints       ) == 0:
										# we're done!
										UpperCamelCase		=     True

				else:
						# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
						# of constraints?

						for cidx, pending_constraint in enumerate(self.pending_constraints       ):
								if pending_constraint.does_advance(A_       ):
										UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     pending_constraint.update(A_       )

										if not stepped:
												raise Exception(
												    '`constraint.update(token_id)` is not yielding incremental progress, '
												    'even though `constraint.does_advance(token_id)` is true.'       )

										if complete:
												self.complete_constraints.append(A_       )
												UpperCamelCase		=     None

										if not complete and stepped:
												UpperCamelCase		=     pending_constraint

										if complete or stepped:
												# If we made any progress at all, then it's at least not a "pending constraint".

												UpperCamelCase		=     (
												    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
												)

												if len(self.pending_constraints       ) == 0 and self.inprogress_constraint is None:
														# If there's no longer any pending after this and no inprogress either, then we must be
														# complete.

														UpperCamelCase		=     True

												break  # prevent accidentally stepping through multiple constraints with just one token.

				return complete, stepped



		def __UpperCamelCase ( self				,      A_=True       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     ConstraintListState(self.constraints       )  # we actually never though self.constraints objects
				# throughout this process. So it's at initialization state.

				if stateful:
						UpperCamelCase		=     [
						    constraint.copy(stateful=A_       ) for constraint in self.complete_constraints
						]
						if self.inprogress_constraint is not None:
								UpperCamelCase		=     self.inprogress_constraint.copy(stateful=A_       )
						UpperCamelCase		=     [constraint.copy() for constraint in self.pending_constraints]

				return new_state


import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )

				UpperCamelCase		=     {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'mask_image': mask_image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'output_type': 'numpy',
				}

				return inputs



		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()				,      reason='XFormers attention is only available with CUDA and `xformers` installed'				,      )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
