

import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict




class 					_snake_case						(						lowerCAmelCase_				):
					pass




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__=None,    snake_case__=False							)						->   List[Any]:
							try:
														__UpperCAmelCase						:			int         =   ast.parse(_SCREAMING_SNAKE_CASE							)
							except SyntaxError as e:
														print("The code generated by the agent is not valid.\n",    _SCREAMING_SNAKE_CASE							)
														return
							if state is None:
														__UpperCAmelCase						:			Optional[Any]         =   {}
							__UpperCAmelCase						:			List[Any]         =   None
							for idx, node in enumerate(expression.body							):
														try:
																					__UpperCAmelCase						:			List[str]         =   evaluate_ast(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
														except InterpretorError as e:
																					__UpperCAmelCase						:			str         =   f'''Evaluation of the code stopped at line {idx} before the end because of the following error'''
																					if chat_mode:
																												msg += (
																												    f'''. Copy paste the following error message and send it back to the agent:\nI get an error: \'{e}\''''
																												)
																					else:
																												msg += f''':\n{e}'''
																					print(_SCREAMING_SNAKE_CASE							)
																					break
														if line_result is not None:
																					__UpperCAmelCase						:			Union[str, Any]         =   line_result

							return result




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   Union[str, Any]:
							if isinstance(_SCREAMING_SNAKE_CASE,    ast.Assign							):
														# Assignement -> we evaluate the assignement which should update the state
														# We return the variable assigned as it may be used to determine the final result.
														return evaluate_assign(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.Call							):
														# Function call -> we return the value of the function call
														return evaluate_call(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.Constant							):
														# Constant -> just return the value
														return expression.value
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.Dict							):
														# Dict -> evaluate all keys and values
														__UpperCAmelCase						:			Optional[Any]         =   [evaluate_ast(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							) for k in expression.keys]
														__UpperCAmelCase						:			List[str]         =   [evaluate_ast(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							) for v in expression.values]
														return dict(zip(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)							)
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.Expr							):
														# Expression -> evaluate the content
														return evaluate_ast(expression.value,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.For							):
														# For loop -> execute the loop
														return evaluate_for(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.FormattedValue							):
														# Formatted value (part of f-string) -> evaluate the content and return
														return evaluate_ast(expression.value,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.If							):
														# If -> execute the right branch
														return evaluate_if(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							elif hasattr(_SCREAMING_SNAKE_CASE,    "Index"							) and isinstance(_SCREAMING_SNAKE_CASE,    ast.Index							):
														return evaluate_ast(expression.value,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.JoinedStr							):
														return "".join([str(evaluate_ast(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)							) for v in expression.values]							)
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.List							):
														# List -> evaluate all elements
														return [evaluate_ast(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							) for elt in expression.elts]
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.Name							):
														# Name -> pick up the value in the state
														return evaluate_name(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.Subscript							):
														# Subscript -> return the value of the indexing
														return evaluate_subscript(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							else:
														# For now we refuse anything else. Let's add things as we need them.
														raise InterpretorError(f'''{expression.__class__.__name__} is not supported.'''							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   Tuple:
							__UpperCAmelCase						:			Any         =   assign.targets
							__UpperCAmelCase						:			int         =   evaluate_ast(assign.value,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)

							if len(_SCREAMING_SNAKE_CASE							) == 1:
														__UpperCAmelCase						:			Optional[Any]         =   result
							else:
														if len(_SCREAMING_SNAKE_CASE							) != len(_SCREAMING_SNAKE_CASE							):
																					raise InterpretorError(f'''Expected {len(_SCREAMING_SNAKE_CASE							)} values but got {len(_SCREAMING_SNAKE_CASE							)}.'''							)
														for var_name, r in zip(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							):
																					__UpperCAmelCase						:			Optional[int]         =   r
							return result




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   Optional[int]:
							if not isinstance(call.func,    ast.Name							):
														raise InterpretorError(
														    f'''It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of '''
														    f'''type {type(call.func							)}.'''							)
							__UpperCAmelCase						:			Any         =   call.func.id
							if func_name not in tools:
														raise InterpretorError(
														    f'''It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id}).'''							)

							__UpperCAmelCase						:			str         =   tools[func_name]
							# Todo deal with args
							__UpperCAmelCase						:			List[Any]         =   [evaluate_ast(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							) for arg in call.args]
							__UpperCAmelCase						:			int         =   {keyword.arg: evaluate_ast(keyword.value,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							) for keyword in call.keywords}
							return func(*_SCREAMING_SNAKE_CASE,    **_SCREAMING_SNAKE_CASE							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   List[str]:
							__UpperCAmelCase						:			Optional[int]         =   evaluate_ast(subscript.slice,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							__UpperCAmelCase						:			str         =   evaluate_ast(subscript.value,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							if isinstance(_SCREAMING_SNAKE_CASE,    (list, tuple)							):
														return value[int(_SCREAMING_SNAKE_CASE							)]
							if index in value:
														return value[index]
							if isinstance(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							) and isinstance(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							):
														__UpperCAmelCase						:			str         =   difflib.get_close_matches(_SCREAMING_SNAKE_CASE,    list(value.keys()							)							)
														if len(_SCREAMING_SNAKE_CASE							) > 0:
																					return value[close_matches[0]]

							raise InterpretorError(f'''Could not index {value} with \'{index}\'.'''							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   List[Any]:
							if name.id in state:
														return state[name.id]
							__UpperCAmelCase						:			Tuple         =   difflib.get_close_matches(name.id,    list(state.keys()							)							)
							if len(_SCREAMING_SNAKE_CASE							) > 0:
														return state[close_matches[0]]
							raise InterpretorError(f'''The variable `{name.id}` is not defined.'''							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   List[str]:
							if len(condition.ops							) > 1:
														raise InterpretorError("Cannot evaluate conditions with multiple operators"							)

							__UpperCAmelCase						:			Union[str, Any]         =   evaluate_ast(condition.left,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							__UpperCAmelCase						:			Optional[int]         =   condition.ops[0]
							__UpperCAmelCase						:			Dict         =   evaluate_ast(condition.comparators[0],    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)

							if isinstance(_SCREAMING_SNAKE_CASE,    ast.Eq							):
														return left == right
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.NotEq							):
														return left != right
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.Lt							):
														return left < right
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.LtE							):
														return left <= right
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.Gt							):
														return left > right
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.GtE							):
														return left >= right
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.Is							):
														return left is right
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.IsNot							):
														return left is not right
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.In							):
														return left in right
							elif isinstance(_SCREAMING_SNAKE_CASE,    ast.NotIn							):
														return left not in right
							else:
														raise InterpretorError(f'''Operator not supported: {comparator}'''							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   List[Any]:
							__UpperCAmelCase						:			Optional[int]         =   None
							if evaluate_condition(if_statement.test,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							):
														for line in if_statement.body:
																					__UpperCAmelCase						:			List[Any]         =   evaluate_ast(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
																					if line_result is not None:
																												__UpperCAmelCase						:			Dict         =   line_result
							else:
														for line in if_statement.orelse:
																					__UpperCAmelCase						:			Dict         =   evaluate_ast(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
																					if line_result is not None:
																												__UpperCAmelCase						:			Optional[Any]         =   line_result
							return result







def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   Tuple:
							__UpperCAmelCase						:			int         =   None
							__UpperCAmelCase						:			Any         =   evaluate_ast(for_loop.iter,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
							for counter in iterator:
														__UpperCAmelCase						:			List[Any]         =   counter
														for expression in for_loop.body:
																					__UpperCAmelCase						:			List[str]         =   evaluate_ast(_SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE							)
																					if line_result is not None:
																												__UpperCAmelCase						:			Tuple         =   line_result
							return result





import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
	from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import SwinaSRImageProcessor




class 					_snake_case						:
					def __init__(  self:				Tuple	,  __lowerCamelCase:				Tuple	,  __lowerCamelCase:				Union[str, Any]=13	,  __lowerCamelCase:				str=32	,  __lowerCamelCase:				Dict=1	,  __lowerCamelCase:				List[Any]=3	,  __lowerCamelCase:				List[Any]=16	,  __lowerCamelCase:				Any=[1, 2, 1]	,  __lowerCamelCase:				List[str]=[2, 2, 4]	,  __lowerCamelCase:				Tuple=2	,  __lowerCamelCase:				Tuple=2.0	,  __lowerCamelCase:				Optional[Any]=True	,  __lowerCamelCase:				List[str]=0.0	,  __lowerCamelCase:				str=0.0	,  __lowerCamelCase:				List[str]=0.1	,  __lowerCamelCase:				Optional[Any]="gelu"	,  __lowerCamelCase:				List[str]=False	,  __lowerCamelCase:				int=True	,  __lowerCamelCase:				List[str]=0.02	,  __lowerCamelCase:				Dict=1e-5	,  __lowerCamelCase:				Dict=True	,  __lowerCamelCase:				List[Any]=None	,  __lowerCamelCase:				Tuple=False	,  __lowerCamelCase:				Optional[Any]=2	,  )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Any         =   parent
												__UpperCAmelCase						:			Optional[int]         =   batch_size
												__UpperCAmelCase						:			Union[str, Any]         =   image_size
												__UpperCAmelCase						:			int         =   patch_size
												__UpperCAmelCase						:			str         =   num_channels
												__UpperCAmelCase						:			Any         =   embed_dim
												__UpperCAmelCase						:			str         =   depths
												__UpperCAmelCase						:			Optional[int]         =   num_heads
												__UpperCAmelCase						:			Any         =   window_size
												__UpperCAmelCase						:			Tuple         =   mlp_ratio
												__UpperCAmelCase						:			List[str]         =   qkv_bias
												__UpperCAmelCase						:			int         =   hidden_dropout_prob
												__UpperCAmelCase						:			Dict         =   attention_probs_dropout_prob
												__UpperCAmelCase						:			Union[str, Any]         =   drop_path_rate
												__UpperCAmelCase						:			Union[str, Any]         =   hidden_act
												__UpperCAmelCase						:			Optional[int]         =   use_absolute_embeddings
												__UpperCAmelCase						:			Any         =   patch_norm
												__UpperCAmelCase						:			str         =   layer_norm_eps
												__UpperCAmelCase						:			Any         =   initializer_range
												__UpperCAmelCase						:			List[str]         =   is_training
												__UpperCAmelCase						:			Union[str, Any]         =   scope
												__UpperCAmelCase						:			Optional[int]         =   use_labels
												__UpperCAmelCase						:			int         =   upscale

												# here we set some attributes to make tests pass
												__UpperCAmelCase						:			Union[str, Any]         =   len(__lowerCamelCase     )
												__UpperCAmelCase						:			Optional[Any]         =   embed_dim
												__UpperCAmelCase						:			List[Any]         =   (image_size // patch_size) ** 2
					def    _lowerCamelCase (  self:				List[Any]     )    ->				List[Any]:
												__UpperCAmelCase						:			Any         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

												__UpperCAmelCase						:			Union[str, Any]         =   None
												if self.use_labels:
																			__UpperCAmelCase						:			Any         =   ids_tensor([self.batch_size]	,  self.type_sequence_label_size     )

												__UpperCAmelCase						:			Optional[Any]         =   self.get_config()

												return config, pixel_values, labels
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Tuple:
												return SwinaSRConfig(
												    image_size=self.image_size	,  patch_size=self.patch_size	,  num_channels=self.num_channels	,  embed_dim=self.embed_dim	,  depths=self.depths	,  num_heads=self.num_heads	,  window_size=self.window_size	,  mlp_ratio=self.mlp_ratio	,  qkv_bias=self.qkv_bias	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  drop_path_rate=self.drop_path_rate	,  hidden_act=self.hidden_act	,  use_absolute_embeddings=self.use_absolute_embeddings	,  path_norm=self.patch_norm	,  layer_norm_eps=self.layer_norm_eps	,  initializer_range=self.initializer_range	,  upscale=self.upscale	,  )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				List[Any]	,  __lowerCamelCase:				Any	,  __lowerCamelCase:				List[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			str         =   SwinaSRModel(config=__lowerCamelCase     )
												model.to(__lowerCamelCase     )
												model.eval()
												__UpperCAmelCase						:			List[str]         =   model(__lowerCamelCase     )

												self.parent.assertEqual(
												    result.last_hidden_state.shape	,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)     )
					def    _lowerCamelCase (  self:				Union[str, Any]	,  __lowerCamelCase:				Optional[int]	,  __lowerCamelCase:				Tuple	,  __lowerCamelCase:				int     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Dict         =   SwinaSRForImageSuperResolution(__lowerCamelCase     )
												model.to(__lowerCamelCase     )
												model.eval()
												__UpperCAmelCase						:			List[str]         =   model(__lowerCamelCase     )

												__UpperCAmelCase						:			Optional[Any]         =   self.image_size * self.upscale
												self.parent.assertEqual(
												    result.reconstruction.shape	,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)     )





					def    _lowerCamelCase (  self:				Any     )    ->				Dict:
												__UpperCAmelCase						:			List[Any]         =   self.prepare_config_and_inputs()
												__UpperCAmelCase    ,	__UpperCAmelCase    ,	__UpperCAmelCase						:			Any         =   config_and_inputs
												__UpperCAmelCase						:			Dict         =   {"pixel_values": pixel_values}
												return config, inputs_dict




@require_torch
class 					_snake_case						(						_lowercase					,      _lowercase					,      unittest.TestCase				):
					lowerCamelCase__:		List[str]  =     (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
					lowerCamelCase__:		List[Any]  =     {"feature-extraction": SwinaSRModel} if is_torch_available() else {}

					lowerCamelCase__:		List[Any]  =     False
					lowerCamelCase__:		Any  =     False
					lowerCamelCase__:		int  =     False
					lowerCamelCase__:		Optional[Any]  =     False
					lowerCamelCase__:		str  =     False
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Union[str, Any]         =   SwinaSRModelTester(self     )
												__UpperCAmelCase						:			Optional[int]         =   ConfigTester(self	,  config_class=__lowerCamelCase	,  embed_dim=37     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				List[Any]:
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Optional[Any]:
												__UpperCAmelCase						:			Optional[int]         =   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				List[str]:
												__UpperCAmelCase						:			Union[str, Any]         =   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_super_resolution(*__lowerCamelCase     )
					@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."     )
					def    _lowerCamelCase (  self:				str     )    ->				List[Any]:
												pass
					@unittest.skip(reason="Swin2SR does not use inputs_embeds"     )
					def    _lowerCamelCase (  self:				str     )    ->				Optional[Any]:
												pass
					@unittest.skip(reason="Swin2SR does not support training yet"     )
					def    _lowerCamelCase (  self:				int     )    ->				Dict:
												pass
					@unittest.skip(reason="Swin2SR does not support training yet"     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				Optional[Any]:
												pass
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Union[str, Any]         =   self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			Dict         =   model_class(__lowerCamelCase     )
																			self.assertIsInstance(model.get_input_embeddings()	,  (nn.Module)     )
																			__UpperCAmelCase						:			Union[str, Any]         =   model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__lowerCamelCase	,  nn.Linear     )     )
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Dict         =   self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			List[Any]         =   model_class(__lowerCamelCase     )
																			__UpperCAmelCase						:			Dict         =   inspect.signature(model.forward     )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCAmelCase						:			int         =   [*signature.parameters.keys()]

																			__UpperCAmelCase						:			int         =   ["pixel_values"]
																			self.assertListEqual(arg_names[:1]	,  __lowerCamelCase     )
					@slow
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCAmelCase						:			Union[str, Any]         =   SwinaSRModel.from_pretrained(__lowerCamelCase     )
																			self.assertIsNotNone(__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Dict     )    ->				Optional[Any]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Any         =   self.model_tester.prepare_config_and_inputs_for_common()

												__UpperCAmelCase						:			Union[str, Any]         =   _config_zero_init(__lowerCamelCase     )
												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			Tuple         =   model_class(config=__lowerCamelCase     )
																			for name, param in model.named_parameters():
																										if "logit_scale" in name:
																																	continue
																										if param.requires_grad:
																																	self.assertIn(
																																	    ((param.data.mean() * 1e9).round() / 1e9).item()	,  [0.0, 1.0]	,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''	,  )





					def    _lowerCamelCase (  self:				Optional[int]     )    ->				List[Any]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			int         =   self.model_tester.prepare_config_and_inputs_for_common()
												__UpperCAmelCase						:			List[Any]         =   True

												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			Optional[int]         =   True
																			__UpperCAmelCase						:			str         =   False
																			__UpperCAmelCase						:			int         =   True
																			__UpperCAmelCase						:			Dict         =   model_class(__lowerCamelCase     )
																			model.to(__lowerCamelCase     )
																			model.eval()
																			with torch.no_grad():
																										__UpperCAmelCase						:			Optional[int]         =   model(**self._prepare_for_class(__lowerCamelCase	,  __lowerCamelCase     )     )
																			__UpperCAmelCase						:			Any         =   outputs.attentions
																			__UpperCAmelCase						:			Union[str, Any]         =   len(self.model_tester.depths     )
																			self.assertEqual(len(__lowerCamelCase     )	,  __lowerCamelCase     )

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			__UpperCAmelCase						:			List[str]         =   True
																			__UpperCAmelCase						:			Any         =   config.window_size**2
																			__UpperCAmelCase						:			List[str]         =   model_class(__lowerCamelCase     )
																			model.to(__lowerCamelCase     )
																			model.eval()
																			with torch.no_grad():
																										__UpperCAmelCase						:			int         =   model(**self._prepare_for_class(__lowerCamelCase	,  __lowerCamelCase     )     )
																			__UpperCAmelCase						:			List[Any]         =   outputs.attentions
																			self.assertEqual(len(__lowerCamelCase     )	,  __lowerCamelCase     )

																			self.assertListEqual(
																			    list(attentions[0].shape[-3:]     )	,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,  )
																			__UpperCAmelCase						:			List[str]         =   len(__lowerCamelCase     )

																			# Check attention is always last and order is fine
																			__UpperCAmelCase						:			Tuple         =   True
																			__UpperCAmelCase						:			List[str]         =   True
																			__UpperCAmelCase						:			List[Any]         =   model_class(__lowerCamelCase     )
																			model.to(__lowerCamelCase     )
																			model.eval()
																			with torch.no_grad():
																										__UpperCAmelCase						:			Optional[int]         =   model(**self._prepare_for_class(__lowerCamelCase	,  __lowerCamelCase     )     )

																			self.assertEqual(out_len + 1	,  len(__lowerCamelCase     )     )

																			__UpperCAmelCase						:			List[Any]         =   outputs.attentions

																			self.assertEqual(len(__lowerCamelCase     )	,  __lowerCamelCase     )

																			self.assertListEqual(
																			    list(self_attentions[0].shape[-3:]     )	,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,  )




@require_vision
@require_torch
@slow
class 					_snake_case						(						unittest.TestCase				):
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[str]         =   SwinaSRImageProcessor()
												__UpperCAmelCase						:			int         =   SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"     ).to(__lowerCamelCase     )

												__UpperCAmelCase						:			Union[str, Any]         =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
												__UpperCAmelCase						:			Dict         =   processor(images=__lowerCamelCase	,  return_tensors="pt"     ).to(__lowerCamelCase     )

												# forward pass
												with torch.no_grad():
																			__UpperCAmelCase						:			int         =   model(**__lowerCamelCase     )

												# verify the logits
												__UpperCAmelCase						:			str         =   torch.Size([1, 3, 9_76, 12_96]     )
												self.assertEqual(outputs.reconstruction.shape	,  __lowerCamelCase     )
												__UpperCAmelCase						:			Union[str, Any]         =   torch.tensor(
												    [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]     ).to(__lowerCamelCase     )
												self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]	,  __lowerCamelCase	,  atol=1e-4     )     )




