import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


_lowerCamelCase    :      int	 =						logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCamelCase    :      List[Any]	 =						"""cuda""" if torch.cuda.is_available() else """cpu"""





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							Optional[int]=1_0_0					,       UpperCAmelCase__   :							List[Any]=" "     ):
					SCREAMING_SNAKE_CASE				=				text.split(a__     )
					return [character.join(text[i : i + n]     ).strip() for i in range(0					,       len(a__     )					,       a__     )]





def 		__lowerCamelCase							(UpperCAmelCase__   :							dict     ):
					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				[], []
					for title, text in zip(documents["title"]					,       documents["text"]     ):
										if text is not None:
															for passage in split_text(a__     ):
																				titles.append(title if title is not None else ""     )
																				texts.append(a__     )
					return {"title": titles, "text": texts}





def 		__lowerCamelCase							(UpperCAmelCase__   :							dict					,       UpperCAmelCase__   :							DPRContextEncoder					,       UpperCAmelCase__   :							DPRContextEncoderTokenizerFast     ):
					SCREAMING_SNAKE_CASE				=				ctx_tokenizer(
					    documents["title"]					,       documents["text"]					,       truncation=a__					,       padding="longest"					,       return_tensors="pt"     )["input_ids"]
					SCREAMING_SNAKE_CASE				=				ctx_encoder(input_ids.to(device=a__     )					,       return_dict=a__     ).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}





def 		__lowerCamelCase							(UpperCAmelCase__   :							"RagExampleArguments"					,       UpperCAmelCase__   :							"ProcessingArguments"					,       UpperCAmelCase__   :							"IndexHnswArguments"					,       ):
					logger.info("Step 1 - Create the dataset"     )
					######################################

					# The dataset needed for RAG must have three columns:
					# - title (string): title of the document
					# - text (string): text of a passage of the document
					# - embeddings (array of dimension d): DPR representation of the passage

					# Let's say you have documents in tab-separated csv files with columns "title" and "text"
					assert os.path.isfile(rag_example_args.csv_path     ), "Please provide a valid path to a csv file"

					# You can load a Dataset object this way
					SCREAMING_SNAKE_CASE				=				load_dataset(
					    "csv"					,       data_files=[rag_example_args.csv_path]					,       split="train"					,       delimiter="\t"					,       column_names=["title", "text"]     )

					# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

					# Then split the documents into passages of 100 words
					SCREAMING_SNAKE_CASE				=				dataset.map(a__					,       batched=a__					,       num_proc=processing_args.num_proc     )

					# And compute the embeddings
					SCREAMING_SNAKE_CASE				=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=a__     )
					SCREAMING_SNAKE_CASE				=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     )
					SCREAMING_SNAKE_CASE				=				Features(
					    {"text": Value("string"     ), "title": Value("string"     ), "embeddings": Sequence(Value("float32"     )     )}     )  # optional, save as float32 instead of float64 to save space
					SCREAMING_SNAKE_CASE				=				dataset.map(
					    partial(a__					,       ctx_encoder=a__					,       ctx_tokenizer=a__     )					,       batched=a__					,       batch_size=processing_args.batch_size					,       features=a__					,       )

					# And finally save your dataset
					SCREAMING_SNAKE_CASE				=				os.path.join(rag_example_args.output_dir					,       "my_knowledge_dataset"     )
					dataset.save_to_disk(a__     )
					# from datasets import load_from_disk
					# dataset = load_from_disk(passages_path)  # to reload the dataset

					######################################
					logger.info("Step 2 - Index the dataset"     )
					######################################

					# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
					SCREAMING_SNAKE_CASE				=				faiss.IndexHNSWFlat(index_hnsw_args.d					,       index_hnsw_args.m					,       faiss.METRIC_INNER_PRODUCT     )
					dataset.add_faiss_index("embeddings"					,       custom_index=a__     )

					# And save the index
					SCREAMING_SNAKE_CASE				=				os.path.join(rag_example_args.output_dir					,       "my_knowledge_dataset_hnsw_index.faiss"     )
					dataset.get_index("embeddings"     ).save(a__     )
					# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

					######################################
					logger.info("Step 3 - Load RAG"     )
					######################################

					# Easy way to load the model
					SCREAMING_SNAKE_CASE				=				RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name					,       index_name="custom"					,       indexed_dataset=a__     )
					SCREAMING_SNAKE_CASE				=				RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name					,       retriever=a__     )
					SCREAMING_SNAKE_CASE				=				RagTokenizer.from_pretrained(rag_example_args.rag_model_name     )

					# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
					# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

					######################################
					logger.info("Step 4 - Have fun"     )
					######################################

					SCREAMING_SNAKE_CASE				=				rag_example_args.question or "What does Moses' rod turn into ?"
					SCREAMING_SNAKE_CASE				=				tokenizer.question_encoder(a__					,       return_tensors="pt"     )["input_ids"]
					SCREAMING_SNAKE_CASE				=				model.generate(a__     )
					SCREAMING_SNAKE_CASE				=				tokenizer.batch_decode(a__					,       skip_special_tokens=a__     )[0]
					logger.info("Q: " + question     )
					logger.info("A: " + generated_string     )


@dataclass
class   lowercase			:
							lowercase__  :				str									=							field(
							    default=str(Path(__A   ).parent / """test_data""" / """my_knowledge_dataset.csv"""   ) ,				metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""} ,				)
							lowercase__  :				Optional[str]									=							field(
							    default=__A ,				metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""} ,				)
							lowercase__  :				str									=							field(
							    default="""facebook/rag-sequence-nq""" ,				metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""} ,				)
							lowercase__  :				str									=							field(
							    default="""facebook/dpr-ctx_encoder-multiset-base""" ,				metadata={
							        """help""": (
							            """The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
							            """ 'facebook/dpr-ctx_encoder-multiset-base'"""
							        )
							    } ,				)
							lowercase__  :				Optional[str]									=							field(
							    default=__A ,				metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""} ,				)



@dataclass
class   lowercase			:
							lowercase__  :				Optional[int]									=							field(
							    default=__A ,				metadata={
							        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
							    } ,				)
							lowercase__  :				int									=							field(
							    default=16 ,				metadata={
							        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
							    } ,				)



@dataclass
class   lowercase			:
							lowercase__  :				int									=							field(
							    default=768 ,				metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""} ,				)
							lowercase__  :				int									=							field(
							    default=128 ,				metadata={
							        """help""": (
							            """The number of bi-directional links created for every new element during the HNSW index construction."""
							        )
							    } ,				)


if __name__ == "__main__":
							logging.basicConfig(level=logging.WARNING)
							logger.setLevel(logging.INFO)

							_lowerCamelCase    :      Dict	 =						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							_lowerCamelCase    :      Tuple	 =						parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														_lowerCamelCase    :      Tuple	 =						rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)










'''simple docstring'''
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        SCREAMING_SNAKE_CASE:

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

           """simple docstring"""



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

       def     snake_case__			(    self     )    ->					List[str]:

           """simple docstring"""



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

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

           __lowercase							=						self.get_config()

           return config, pixel_values, labels

       def     snake_case__			(    self     )    ->					Optional[int]:

           """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     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						SwinModel(config=lowerCamelCase__     )
           model.to(lowerCamelCase__     )
           model.eval()
           __lowercase							=						model(lowerCamelCase__     )

           __lowercase							=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
           __lowercase							=						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     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						SwinBackbone(config=lowerCamelCase__     )
           model.to(lowerCamelCase__     )
           model.eval()
           __lowercase							=						model(lowerCamelCase__     )

           # 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
           __lowercase							=						None
           __lowercase							=						SwinBackbone(config=lowerCamelCase__     )
           model.to(lowerCamelCase__     )
           model.eval()
           __lowercase							=						model(lowerCamelCase__     )

           # 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     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					List[str]:

           """simple docstring"""



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

           # test greyscale images
           __lowercase							=						1
           __lowercase							=						SwinForMaskedImageModeling(lowerCamelCase__     )
           model.to(lowerCamelCase__     )
           model.eval()

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

       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					int:

           """simple docstring"""



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

           # test greyscale images
           __lowercase							=						1
           __lowercase							=						SwinForImageClassification(lowerCamelCase__     )
           model.to(lowerCamelCase__     )
           model.eval()

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




       def     snake_case__			(    self     )    ->					List[str]:

           """simple docstring"""



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






@require_torch
class        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      unittest.TestCase     ):
       snake_case_ :     Optional[Any]    =					(
           (
               SwinModel,
               SwinBackbone,
               SwinForImageClassification,
               SwinForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       snake_case_ :     Any    =					(
           {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
           if is_torch_available()
           else {}
       )
       snake_case_ :     Any    =					True

       snake_case_ :     int    =					False
       snake_case_ :     Tuple    =					False
       snake_case_ :     Optional[int]    =					False

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase							=						SwinModelTester(self     )
           __lowercase							=						ConfigTester(self					,		config_class=lowerCamelCase__					,		embed_dim=37     )

       def     snake_case__			(    self     )    ->					int:

           """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     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           return

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__     )

       @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""     )
       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           pass

       def     snake_case__			(    self     )    ->					Optional[Any]:

           """simple docstring"""



           super().test_training_gradient_checkpointing()

       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__     )

       @unittest.skip(reason="""Swin does not use inputs_embeds"""     )
       def     snake_case__			(    self     )    ->					Tuple:

           """simple docstring"""



           pass

       @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""     )
       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           pass

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           __lowercase				,__lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

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

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase				,__lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

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

               __lowercase							=						["""pixel_values"""]
               self.assertListEqual(arg_names[:1]					,		lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



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

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

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

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

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

               __lowercase							=						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]					,		)

       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					Optional[int]:

           """simple docstring"""



           __lowercase							=						model_class(lowerCamelCase__     )
           model.to(lowerCamelCase__     )
           model.eval()

           with torch.no_grad():
               __lowercase							=						model(**self._prepare_for_class(lowerCamelCase__					,		lowerCamelCase__     )     )

           __lowercase							=						outputs.hidden_states

           __lowercase							=						getattr(
               self.model_tester					,		"""expected_num_hidden_layers"""					,		len(self.model_tester.depths     ) + 1     )
           self.assertEqual(len(lowerCamelCase__     )					,		lowerCamelCase__     )

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

           __lowercase							=						(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":
               __lowercase							=						outputs.reshaped_hidden_states
               self.assertEqual(len(lowerCamelCase__     )					,		lowerCamelCase__     )

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

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           __lowercase				,__lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

           __lowercase							=						(
               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:
               __lowercase							=						True
               self.check_hidden_states_output(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )

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

               self.check_hidden_states_output(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



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

           __lowercase							=						(
               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)
           )
           __lowercase							=						(
               config.patch_size
               if isinstance(config.patch_size					,		collections.abc.Iterable     )
               else (config.patch_size, config.patch_size)
           )

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

           for model_class in self.all_model_classes:
               __lowercase							=						True
               self.check_hidden_states_output(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		(padded_height, padded_width)     )

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

       @slow
       def     snake_case__			(    self     )    ->					Optional[Any]:

           """simple docstring"""



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




       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase				,__lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

           __lowercase							=						_config_zero_init(lowerCamelCase__     )
           for model_class in self.all_model_classes:
               __lowercase							=						model_class(config=lowerCamelCase__     )
               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        SCREAMING_SNAKE_CASE(				unittest.TestCase     ):

       @cached_property
       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



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




       @slow
       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



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

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

           # forward pass
           with torch.no_grad():
               __lowercase							=						model(**lowerCamelCase__     )

           # verify the logits
           __lowercase							=						torch.Size((1, 1000)     )
           self.assertEqual(outputs.logits.shape					,		lowerCamelCase__     )
           __lowercase							=						torch.tensor([-0.09_48, -0.64_54, -0.09_21]     ).to(lowerCamelCase__     )
           self.assertTrue(torch.allclose(outputs.logits[0, :3]					,		lowerCamelCase__					,		atol=1E-4     )     )






@require_torch
class        SCREAMING_SNAKE_CASE(				unittest.TestCase   ,      __A     ):
       snake_case_ :     List[Any]    =					(SwinBackbone,) if is_torch_available() else ()
       snake_case_ :     Optional[Any]    =					SwinConfig

       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



           __lowercase							=						SwinModelTester(self     )




