

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFResNetForImageClassification, TFResNetModel
    from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[Any]=3     , __lowerCamelCase					:			Any=32     , __lowerCamelCase					:			Tuple=3     , __lowerCamelCase					:			Optional[int]=10     , __lowerCamelCase					:			int=[10, 20, 30, 40]     , __lowerCamelCase					:			str=[1, 1, 2, 1]     , __lowerCamelCase					:			str=True     , __lowerCamelCase					:			Union[str, Any]=True     , __lowerCamelCase					:			List[Any]="relu"     , __lowerCamelCase					:			List[Any]=3     , __lowerCamelCase					:			Union[str, Any]=None     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   image_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   embeddings_size
       SCREAMING_SNAKE_CASE										=   hidden_sizes
       SCREAMING_SNAKE_CASE										=   depths
       SCREAMING_SNAKE_CASE										=   is_training
       SCREAMING_SNAKE_CASE										=   use_labels
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   num_labels
       SCREAMING_SNAKE_CASE										=   scope
       SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       SCREAMING_SNAKE_CASE										=   None
       if self.use_labels:
           SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size]     , self.num_labels			)

       SCREAMING_SNAKE_CASE										=   self.get_config()

       return config, pixel_values, labels





   def      _snake_case		(       self					:			List[str]			):
       return ResNetConfig(
           num_channels=self.num_channels     , embeddings_size=self.embeddings_size     , hidden_sizes=self.hidden_sizes     , depths=self.depths     , hidden_act=self.hidden_act     , num_labels=self.num_labels     , image_size=self.image_size     , )





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   TFResNetModel(config=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape     , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     , )





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.num_labels
       SCREAMING_SNAKE_CASE										=   TFResNetForImageClassification(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , labels=__lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)			)





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   config_and_inputs
       SCREAMING_SNAKE_CASE										=   {"pixel_values": pixel_values}
       return config, inputs_dict





@require_tf
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
   lowerCamelCase__				=      (
       {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
       if is_tf_available()
       else {}
   )

   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   TFResNetModelTester(self			)
       SCREAMING_SNAKE_CASE										=   ConfigTester(self     , config_class=__lowerCamelCase     , has_text_modality=__lowerCamelCase			)





   def      _snake_case		(       self					:			int			):
       self.create_and_test_config_common_properties()
       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      _snake_case		(       self					:			List[str]			):
       return





   @unittest.skip(reason="ResNet does not use inputs_embeds"			)
   def      _snake_case		(       self					:			List[str]			):
       pass





   @unittest.skip(reason="ResNet does not support input and output embeddings"			)
   def      _snake_case		(       self					:			Any			):
       pass





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   inspect.signature(model.call			)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           SCREAMING_SNAKE_CASE										=   [*signature.parameters.keys()]

           SCREAMING_SNAKE_CASE										=   ["pixel_values"]
           self.assertListEqual(arg_names[:1]     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       def check_hidden_states_output(__lowerCamelCase					:			Any     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Union[str, Any]			):
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   model(**self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase			)			)

           SCREAMING_SNAKE_CASE										=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           SCREAMING_SNAKE_CASE										=   self.model_tester.num_stages
           self.assertEqual(len(__lowerCamelCase			)     , expected_num_stages + 1			)

           # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]			)     , [self.model_tester.image_size // 4, self.model_tester.image_size // 4]     , )

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE										=   ["basic", "bottleneck"]
       for model_class in self.all_model_classes:
           for layer_type in layers_type:
               SCREAMING_SNAKE_CASE										=   layer_type
               SCREAMING_SNAKE_CASE										=   True
               check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               SCREAMING_SNAKE_CASE										=   True

               check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





   @slow
   def      _snake_case		(       self					:			int			):
       for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE										=   TFResNetModel.from_pretrained(__lowerCamelCase			)
           self.assertIsNotNone(__lowerCamelCase			)





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image





@require_tf
@require_vision
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   @cached_property
   def      _snake_case		(       self					:			Optional[int]			):
       return (
           AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
           if is_vision_available()
           else None
       )





   @slow
   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

       SCREAMING_SNAKE_CASE										=   self.default_image_processor
       SCREAMING_SNAKE_CASE										=   prepare_img()
       SCREAMING_SNAKE_CASE										=   image_processor(images=__lowerCamelCase     , return_tensors="tf"			)

       # forward pass
       SCREAMING_SNAKE_CASE										=   model(**__lowerCamelCase			)

       # verify the logits
       SCREAMING_SNAKE_CASE										=   tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tf.constant([-11.1_069, -9.7_877, -8.3_777]			)

       self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()     , __lowerCamelCase     , atol=1e-4			)			)






'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(UnCLIPScheduler,)


					def lowerCamelCase_			(						self				:			List[str]      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			{
							    """num_train_timesteps""": 10_00,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for timesteps in [1, 5, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for time_step in [0, 5_00, 9_99]:
									for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
											if prev_timestep is not None and prev_timestep >= time_step:
													continue

											self.check_over_forward(time_step=lowerCamelCase_      ,						prev_timestep=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config(variance_type="""fixed_small_log"""						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0000e-10						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_549_625						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.9_994_987						)						) < 1e-5


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_scheduler_config(variance_type="""learned_range"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			0.5

							assert scheduler._get_variance(1      ,						predicted_variance=lowerCamelCase_						) - -10.1_712_790 < 1e-5
							assert scheduler._get_variance(4_87      ,						predicted_variance=lowerCamelCase_						) - -5.7_998_052 < 1e-5
							assert scheduler._get_variance(9_99      ,						predicted_variance=lowerCamelCase_						) - -0.0_010_011 < 1e-5


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				Optional[int]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 252.2_682_495						) < 1e-2
							assert abs(result_mean.item() - 0.3_284_743						) < 1e-3


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							scheduler.set_timesteps(25						)

							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				Any		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				int		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									if i + 1 == timesteps.shape[0]:
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			None
									else:
											SCREAMING_SNAKE_CASE :				int		=			timesteps[i + 1]

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

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 258.2_044_983						) < 1e-2
							assert abs(result_mean.item() - 0.3_362_038						) < 1e-3


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							pass



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							pass



