






"""simple docstring"""





import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
UpperCamelCase					=     logging.getLogger(__name__)






def 							A(    snake_case_						):
       """simple docstring"""





       np.random.seed(args.seed						)
       torch.manual_seed(args.seed						)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed						)






def 							A(    ):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     argparse.ArgumentParser()
       parser.add_argument(
           "--model_name_or_path"		,  default=snake_case_		,  type=snake_case_		,  required=snake_case_		,  )
       parser.add_argument("--prompt"		,  type=snake_case_		,  default=""						)
       parser.add_argument("--length"		,  type=snake_case_		,  default=20						)
       parser.add_argument("--stop_token"		,  type=snake_case_		,  default=snake_case_		,  help="Token at which text generation is stopped"						)
       parser.add_argument(
           "--temperature"		,  type=snake_case_		,  default=1.0		,  help="temperature of 1.0 has no effect, lower tend toward greedy sampling"		,  )
       parser.add_argument(
           "--repetition_penalty"		,  type=snake_case_		,  default=1.0		,  help="primarily useful for CTRL model; in that case, use 1.2"						)
       parser.add_argument("--k"		,  type=snake_case_		,  default=0						)
       parser.add_argument("--penalty_alpha"		,  type=snake_case_		,  default=0.0						)
       parser.add_argument("--p"		,  type=snake_case_		,  default=0.9						)

       parser.add_argument("--prefix"		,  type=snake_case_		,  default=""		,  help="Text added prior to input."						)
       parser.add_argument("--padding_text"		,  type=snake_case_		,  default=""		,  help="Deprecated, the use of `--prefix` is preferred."						)
       parser.add_argument("--xlm_language"		,  type=snake_case_		,  default=""		,  help="Optional language when used with the XLM model."						)

       parser.add_argument("--seed"		,  type=snake_case_		,  default=42		,  help="random seed for initialization"						)
       parser.add_argument("--no_cuda"		,  action="store_true"		,  help="Avoid using CUDA when available"						)
       parser.add_argument(
           "--fp16"		,  action="store_true"		,  help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"		,  )
       lowercase__:							Optional[Any]         =     parser.parse_args()

       lowercase__:							List[Any]         =     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
       lowercase__:							Union[str, Any]         =     0 if args.no_cuda else torch.cuda.device_count()

       logger.warning(F"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""						)

       set_seed(snake_case_						)

       # Initialize the model and tokenizer
       lowercase__:							Optional[int]         =     AutoTokenizer.from_pretrained(args.model_name_or_path						)
       lowercase__:							str         =     AutoModelForCausalLM.from_pretrained(args.model_name_or_path						)

       # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
       # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
       model.to(args.device						)

       if args.fpaa:
              model.half()

       logger.info(snake_case_						)
       lowercase__:							Union[str, Any]         =     args.prompt if args.prompt else input("Model prompt >>> "						)

       lowercase__:							Optional[int]         =     tokenizer(snake_case_		,  return_tensors="pt"		,  add_special_tokens=snake_case_						)
       lowercase__:							Optional[Any]         =     {key: value.to(args.device						) for key, value in inputs.items()}

       lowercase__:							Optional[int]         =     model.generate(
           **snake_case_		,  max_length=args.length + len(inputs["input_ids"][0]						)		,  penalty_alpha=args.penalty_alpha		,  top_k=args.k		,  )

       lowercase__:							Tuple         =     []
       for generated_sequence_idx, generated_sequence in enumerate(snake_case_						):
              print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""						)
              lowercase__:							Any         =     generated_sequence.tolist()

              # Decode text
              lowercase__:							Tuple         =     tokenizer.decode(snake_case_		,  clean_up_tokenization_spaces=snake_case_		,  add_special_tokens=snake_case_						)

              # Remove all text after the stop token
              lowercase__:							Any         =     text[: text.find(args.stop_token						) if args.stop_token else None]

              # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
              lowercase__:							Optional[Any]         =     (
                  prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]		,  clean_up_tokenization_spaces=snake_case_						)						) :]
              )

              generated_sequences.append(snake_case_						)
              print(snake_case_						)

       return generated_sequences


if __name__ == "__main__":
  main()











import collections
import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class    _lowercase :
				'''simple docstring'''







				def __init__(			self  ,			snake_case__  ,			snake_case__=13  ,			snake_case__=32  ,			snake_case__=2  ,			snake_case__=3  ,			snake_case__=16  ,			snake_case__=[1, 2, 1]  ,			snake_case__=[2, 2, 4]  ,			snake_case__=2  ,			snake_case__=2.0  ,			snake_case__=True  ,			snake_case__=0.0  ,			snake_case__=0.0  ,			snake_case__=0.1  ,			snake_case__="gelu"  ,			snake_case__=False  ,			snake_case__=True  ,			snake_case__=0.02  ,			snake_case__=1e-5  ,			snake_case__=True  ,			snake_case__=None  ,			snake_case__=True  ,			snake_case__=10  ,			snake_case__=8  ,			snake_case__=["stage1", "stage2"]  ,			snake_case__=[1, 2]  ,			):

									'''simple docstring'''


									UpperCamelCase_			= parent
									UpperCamelCase_			= batch_size
									UpperCamelCase_			= image_size
									UpperCamelCase_			= patch_size
									UpperCamelCase_			= num_channels
									UpperCamelCase_			= embed_dim
									UpperCamelCase_			= depths
									UpperCamelCase_			= num_heads
									UpperCamelCase_			= window_size
									UpperCamelCase_			= mlp_ratio
									UpperCamelCase_			= qkv_bias
									UpperCamelCase_			= hidden_dropout_prob
									UpperCamelCase_			= attention_probs_dropout_prob
									UpperCamelCase_			= drop_path_rate
									UpperCamelCase_			= hidden_act
									UpperCamelCase_			= use_absolute_embeddings
									UpperCamelCase_			= patch_norm
									UpperCamelCase_			= layer_norm_eps
									UpperCamelCase_			= initializer_range
									UpperCamelCase_			= is_training
									UpperCamelCase_			= scope
									UpperCamelCase_			= use_labels
									UpperCamelCase_			= type_sequence_label_size
									UpperCamelCase_			= encoder_stride
									UpperCamelCase_			= out_features
									UpperCamelCase_			= out_indices

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

									UpperCamelCase_			= None
									if self.use_labels:
														UpperCamelCase_			= ids_tensor([self.batch_size]  ,			self.type_sequence_label_size     )

									UpperCamelCase_			= self.get_config()

									return config, pixel_values, labels

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return SwinConfig(
									    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  ,			encoder_stride=self.encoder_stride  ,			out_features=self.out_features  ,			out_indices=self.out_indices  ,			)

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinModel(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )

									UpperCamelCase_			= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
									UpperCamelCase_			= int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

									self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, expected_seq_len, expected_dim)     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinBackbone(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps     )  ,			len(config.out_features     )     )
									self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,			[self.batch_size, model.channels[0], 16, 16]     )

									# verify channels
									self.parent.assertEqual(len(model.channels     )  ,			len(config.out_features     )     )

									# verify backbone works with out_features=None
									UpperCamelCase_			= None
									UpperCamelCase_			= SwinBackbone(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps     )  ,			1     )
									self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,			[self.batch_size, model.channels[-1], 4, 4]     )

									# verify channels
									self.parent.assertEqual(len(model.channels     )  ,			1     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinForMaskedImageModeling(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )
									self.parent.assertEqual(
									    result.logits.shape  ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

									# test greyscale images
									UpperCamelCase_			= 1
									UpperCamelCase_			= SwinForMaskedImageModeling(snake_case__     )
									model.to(snake_case__     )
									model.eval()

									UpperCamelCase_			= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									UpperCamelCase_			= model(snake_case__     )
									self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, 1, self.image_size, self.image_size)     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= self.type_sequence_label_size
									UpperCamelCase_			= SwinForImageClassification(snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__  ,			labels=snake_case__     )
									self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.type_sequence_label_size)     )

									# test greyscale images
									UpperCamelCase_			= 1
									UpperCamelCase_			= SwinForImageClassification(snake_case__     )
									model.to(snake_case__     )
									model.eval()

									UpperCamelCase_			= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									UpperCamelCase_			= model(snake_case__     )
									self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.type_sequence_label_size)     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.prepare_config_and_inputs()
									(
									    (
									    UpperCamelCase_
									)	,     (
									    UpperCamelCase_
									)	,     (
									    UpperCamelCase_
									)	,     
									)			= config_and_inputs
									UpperCamelCase_			= {"pixel_values": pixel_values}
									return config, inputs_dict




@require_torch
class    _lowercase (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    (
				    (
				        SwinModel,
				        SwinBackbone,
				        SwinForImageClassification,
				        SwinForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__     =    (
				    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
				    if is_torch_available()
				    else {}
				)
				lowercase__     =    True

				lowercase__     =    False
				lowercase__     =    False
				lowercase__     =    False

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinModelTester(self     )
									UpperCamelCase_			= ConfigTester(self  ,			config_class=snake_case__  ,			embed_dim=37     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									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    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case__     )

				@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									super().test_training_gradient_checkpointing()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case__     )

				@unittest.skip(reason="Swin does not use inputs_embeds"     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				@unittest.skip(reason="Swin Transformer does not use feedforward chunking"     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														UpperCamelCase_			= model_class(snake_case__     )
														self.assertIsInstance(model.get_input_embeddings()  ,			(nn.Module)     )
														UpperCamelCase_			= model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(snake_case__  ,			nn.Linear     )     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

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

														UpperCamelCase_			= ["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase_			= True

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														UpperCamelCase_			= False
														UpperCamelCase_			= True
														UpperCamelCase_			= model_class(snake_case__     )
														model.to(snake_case__     )
														model.eval()
														with torch.no_grad():
																			UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )
														UpperCamelCase_			= outputs.attentions
														UpperCamelCase_			= len(self.model_tester.depths     )
														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

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

														self.assertListEqual(
														    list(attentions[0].shape[-3:]     )  ,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,			)
														UpperCamelCase_			= len(snake_case__     )

														# Check attention is always last and order is fine
														UpperCamelCase_			= True
														UpperCamelCase_			= True
														UpperCamelCase_			= model_class(snake_case__     )
														model.to(snake_case__     )
														model.eval()
														with torch.no_grad():
																			UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )

														# also another +1 for reshaped_hidden_states
														UpperCamelCase_			= 1 if model_class.__name__ == "SwinBackbone" else 2
														self.assertEqual(out_len + added_hidden_states  ,			len(snake_case__     )     )

														UpperCamelCase_			= outputs.attentions

														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

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

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= model_class(snake_case__     )
									model.to(snake_case__     )
									model.eval()

									with torch.no_grad():
														UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )

									UpperCamelCase_			= outputs.hidden_states

									UpperCamelCase_			= getattr(
									    self.model_tester  ,			"expected_num_hidden_layers"  ,			len(self.model_tester.depths     ) + 1     )
									self.assertEqual(len(snake_case__     )  ,			snake_case__     )

									# Swin has a different seq_length
									UpperCamelCase_			= (
									    config.patch_size
									    if isinstance(config.patch_size  ,			collections.abc.Iterable     )
									    else (config.patch_size, config.patch_size)
									)

									UpperCamelCase_			= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]     )  ,			[num_patches, self.model_tester.embed_dim]  ,			)

									if not model_class.__name__ == "SwinBackbone":
														UpperCamelCase_			= outputs.reshaped_hidden_states
														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

														UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= reshaped_hidden_states[0].shape
														UpperCamelCase_			= (
														    reshaped_hidden_states[0].view(snake_case__  ,			snake_case__  ,			height * width     ).permute(0  ,			2  ,			1     )
														)
														self.assertListEqual(
														    list(reshaped_hidden_states.shape[-2:]     )  ,			[num_patches, self.model_tester.embed_dim]  ,			)

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_			= (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size  ,			collections.abc.Iterable     )
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

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

														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase_			= 3

									UpperCamelCase_			= (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size  ,			collections.abc.Iterable     )
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)
									UpperCamelCase_			= (
									    config.patch_size
									    if isinstance(config.patch_size  ,			collections.abc.Iterable     )
									    else (config.patch_size, config.patch_size)
									)

									UpperCamelCase_			= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									UpperCamelCase_			= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			(padded_height, padded_width)     )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			(padded_height, padded_width)     )

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCamelCase_			= SwinModel.from_pretrained(snake_case__     )
														self.assertIsNotNone(snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_			= _config_zero_init(snake_case__     )
									for model_class in self.all_model_classes:
														UpperCamelCase_			= model_class(config=snake_case__     )
														for name, param in model.named_parameters():
																			if "embeddings" not in name and 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"""  ,			)




@require_vision
@require_torch
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@cached_property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return (
									    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"     )
									    if is_vision_available()
									    else None
									)

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"     ).to(snake_case__     )
									UpperCamelCase_			= self.default_image_processor

									UpperCamelCase_			= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
									UpperCamelCase_			= image_processor(images=snake_case__  ,			return_tensors="pt"     ).to(snake_case__     )

									# forward pass
									with torch.no_grad():
														UpperCamelCase_			= model(**snake_case__     )

									# verify the logits
									UpperCamelCase_			= torch.Size((1, 1000)     )
									self.assertEqual(outputs.logits.shape  ,			snake_case__     )
									UpperCamelCase_			= torch.tensor([-0.0_948, -0.6_454, -0.0_921]     ).to(snake_case__     )
									self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,			snake_case__  ,			atol=1e-4     )     )




@require_torch
class    _lowercase (unittest.TestCase   , a_   ):
				'''simple docstring'''







				lowercase__     =    (SwinBackbone,) if is_torch_available() else ()
				lowercase__     =    SwinConfig

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinModelTester(self     )
