




'''simple docstring'''

import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification


def 						__magic_name__       (      __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							SwinConfig()
					snake_case_		     =							swin_name.split('''_'''      )

					snake_case_		     =							name_split[1]
					snake_case_		     =							int(name_split[4]      )
					snake_case_		     =							int(name_split[3][-1]      )

					if model_size == "tiny":
										snake_case_		     =							96
										snake_case_		     =							(2, 2, 6, 2)
										snake_case_		     =							(3, 6, 12, 24)
					elif model_size == "small":
										snake_case_		     =							96
										snake_case_		     =							(2, 2, 18, 2)
										snake_case_		     =							(3, 6, 12, 24)
					elif model_size == "base":
										snake_case_		     =							128
										snake_case_		     =							(2, 2, 18, 2)
										snake_case_		     =							(4, 8, 16, 32)
					else:
										snake_case_		     =							192
										snake_case_		     =							(2, 2, 18, 2)
										snake_case_		     =							(6, 12, 24, 48)

					if "in22k" in swin_name:
										snake_case_		     =							2_1841
					else:
										snake_case_		     =							1000
										snake_case_		     =							'''huggingface/label-files'''
										snake_case_		     =							'''imagenet-1k-id2label.json'''
										snake_case_		     =							json.load(open(hf_hub_download(__UpperCAmelCase, __UpperCAmelCase, repo_type='''dataset'''      ), '''r'''      )      )
										snake_case_		     =							{int(__UpperCAmelCase      ): v for k, v in idalabel.items()}
										snake_case_		     =							idalabel
										snake_case_		     =							{v: k for k, v in idalabel.items()}

					snake_case_		     =							img_size
					snake_case_		     =							num_classes
					snake_case_		     =							embed_dim
					snake_case_		     =							depths
					snake_case_		     =							num_heads
					snake_case_		     =							window_size

					return config


def 						__magic_name__       (      __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					if "patch_embed.proj" in name:
										snake_case_		     =							name.replace('''patch_embed.proj''', '''embeddings.patch_embeddings.projection'''      )
					if "patch_embed.norm" in name:
										snake_case_		     =							name.replace('''patch_embed.norm''', '''embeddings.norm'''      )
					if "layers" in name:
										snake_case_		     =							'''encoder.''' + name
					if "attn.proj" in name:
										snake_case_		     =							name.replace('''attn.proj''', '''attention.output.dense'''      )
					if "attn" in name:
										snake_case_		     =							name.replace('''attn''', '''attention.self'''      )
					if "norm1" in name:
										snake_case_		     =							name.replace('''norm1''', '''layernorm_before'''      )
					if "norm2" in name:
										snake_case_		     =							name.replace('''norm2''', '''layernorm_after'''      )
					if "mlp.fc1" in name:
										snake_case_		     =							name.replace('''mlp.fc1''', '''intermediate.dense'''      )
					if "mlp.fc2" in name:
										snake_case_		     =							name.replace('''mlp.fc2''', '''output.dense'''      )

					if name == "norm.weight":
										snake_case_		     =							'''layernorm.weight'''
					if name == "norm.bias":
										snake_case_		     =							'''layernorm.bias'''

					if "head" in name:
										snake_case_		     =							name.replace('''head''', '''classifier'''      )
					else:
										snake_case_		     =							'''swin.''' + name

					return name


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					for key in orig_state_dict.copy().keys():
										snake_case_		     =							orig_state_dict.pop(__UpperCAmelCase      )

										if "mask" in key:
															continue
										elif "qkv" in key:
															snake_case_		     =							key.split('''.'''      )
															snake_case_		     =							int(key_split[1]      )
															snake_case_		     =							int(key_split[3]      )
															snake_case_		     =							model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

															if "weight" in key:
																				snake_case_		     =							val[:dim, :]
																				snake_case_		     =							val[
																				    dim : dim * 2, :
																				]
																				snake_case_		     =							val[-dim:, :]
															else:
																				snake_case_		     =							val[
																				    :dim
																				]
																				snake_case_		     =							val[
																				    dim : dim * 2
																				]
																				snake_case_		     =							val[
																				    -dim:
																				]
										else:
															snake_case_		     =							val

					return orig_state_dict


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							timm.create_model(__UpperCAmelCase, pretrained=__UpperCAmelCase      )
					timm_model.eval()

					snake_case_		     =							get_swin_config(__UpperCAmelCase      )
					snake_case_		     =							SwinForImageClassification(__UpperCAmelCase      )
					model.eval()

					snake_case_		     =							convert_state_dict(timm_model.state_dict(), __UpperCAmelCase      )
					model.load_state_dict(__UpperCAmelCase      )

					snake_case_		     =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''

					snake_case_		     =							AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_''', '''-'''      )      )      )
					snake_case_		     =							Image.open(requests.get(__UpperCAmelCase, stream=__UpperCAmelCase      ).raw      )
					snake_case_		     =							image_processor(images=__UpperCAmelCase, return_tensors='''pt'''      )

					snake_case_		     =							timm_model(inputs['''pixel_values''']      )
					snake_case_		     =							model(**__UpperCAmelCase      ).logits

					assert torch.allclose(__UpperCAmelCase, __UpperCAmelCase, atol=1e-3      )

					print(F"Saving model {swin_name} to {pytorch_dump_folder_path}"      )
					model.save_pretrained(__UpperCAmelCase      )

					print(F"Saving image processor to {pytorch_dump_folder_path}"      )
					image_processor.save_pretrained(__UpperCAmelCase      )


if __name__ == "__main__":
							a    :							int										=    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--swin_name',
							    default='swin_tiny_patch4_window7_224',
							    type=str,
							    help='Name of the Swin timm model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)

							a    :							str										=    parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
							import numpy
							import tensorflow as tf

							from transformers import (
							    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    BertConfig,
							    DPRConfig,
							    TFDPRContextEncoder,
							    TFDPRQuestionEncoder,
							    TFDPRReader,
							)






class 		a							:


				def __init__(						self       :   Any			,							lowercase_       :   int			,							lowercase_       :   str=13			,							lowercase_       :   Union[str, Any]=7			,							lowercase_       :   Any=True			,							lowercase_       :   List[str]=True			,							lowercase_       :   Tuple=True			,							lowercase_       :   List[Any]=True			,							lowercase_       :   str=99			,							lowercase_       :   int=32			,							lowercase_       :   int=2			,							lowercase_       :   Any=4			,							lowercase_       :   Union[str, Any]=37			,							lowercase_       :   Optional[int]="gelu"			,							lowercase_       :   Dict=0.1			,							lowercase_       :   Optional[Any]=0.1			,							lowercase_       :   Union[str, Any]=512			,							lowercase_       :   Dict=16			,							lowercase_       :   int=2			,							lowercase_       :   Any=0.02			,							lowercase_       :   int=3			,							lowercase_       :   Any=4			,							lowercase_       :   List[Any]=None			,							lowercase_       :   List[str]=0			,							):
									snake_case_		     =							parent
									snake_case_		     =							batch_size
									snake_case_		     =							seq_length
									snake_case_		     =							is_training
									snake_case_		     =							use_input_mask
									snake_case_		     =							use_token_type_ids
									snake_case_		     =							use_labels
									snake_case_		     =							vocab_size
									snake_case_		     =							hidden_size
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							num_attention_heads
									snake_case_		     =							intermediate_size
									snake_case_		     =							hidden_act
									snake_case_		     =							hidden_dropout_prob
									snake_case_		     =							attention_probs_dropout_prob
									snake_case_		     =							max_position_embeddings
									snake_case_		     =							type_vocab_size
									snake_case_		     =							type_sequence_label_size
									snake_case_		     =							initializer_range
									snake_case_		     =							num_labels
									snake_case_		     =							num_choices
									snake_case_		     =							scope
									snake_case_		     =							projection_dim


				def     A_					(						self       :   int   ):
									snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size   )

									snake_case_		     =							None
									if self.use_input_mask:
														# follow test_modeling_tf_ctrl.py
														snake_case_		     =							random_attention_mask([self.batch_size, self.seq_length]   )

									snake_case_		     =							None
									if self.use_token_type_ids:
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.type_vocab_size   )

									snake_case_		     =							None
									snake_case_		     =							None
									snake_case_		     =							None
									if self.use_labels:
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.type_sequence_label_size   )
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.num_labels   )
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.num_choices   )

									snake_case_		     =							BertConfig(
									    vocab_size=self.vocab_size			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							max_position_embeddings=self.max_position_embeddings			,							type_vocab_size=self.type_vocab_size			,							is_decoder=lowercase_			,							initializer_range=self.initializer_range			,							)
									snake_case_		     =							DPRConfig(projection_dim=self.projection_dim			,							**config.to_dict()   )

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


				def     A_					(						self       :   Dict			,							lowercase_       :   Optional[Any]			,							lowercase_       :   int			,							lowercase_       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   str   ):
									snake_case_		     =							TFDPRContextEncoder(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.projection_dim or self.hidden_size)   )


				def     A_					(						self       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Dict			,							lowercase_       :   Dict			,							lowercase_       :   Dict			,							lowercase_       :   Any   ):
									snake_case_		     =							TFDPRQuestionEncoder(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.projection_dim or self.hidden_size)   )


				def     A_					(						self       :   str			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									snake_case_		     =							TFDPRReader(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_   )

									self.parent.assertEqual(result.start_logits.shape			,							(self.batch_size, self.seq_length)   )
									self.parent.assertEqual(result.end_logits.shape			,							(self.batch_size, self.seq_length)   )
									self.parent.assertEqual(result.relevance_logits.shape			,							(self.batch_size,)   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,
									)		     =							config_and_inputs
									snake_case_		     =							{'''input_ids''': input_ids}
									return config, inputs_dict







@require_tf
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(
				    (
				        TFDPRContextEncoder,
				        TFDPRQuestionEncoder,
				        TFDPRReader,
				    )
				    if is_tf_available()
				    else ()
				)
				snake_case_           =						{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							TFDPRModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_			,							hidden_size=37   )


				def     A_					(						self       :   Any   ):
									self.config_tester.run_common_tests()


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_context_encoder(*lowercase_   )


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_question_encoder(*lowercase_   )


				def     A_					(						self       :   Any   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_reader(*lowercase_   )


				@slow
				def     A_					(						self       :   int   ):
									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRContextEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRContextEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRQuestionEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRReader.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )







@require_tf
class 		a							(  unittest.TestCase	):


				@slow
				def     A_					(						self       :   Dict   ):
									snake_case_		     =							TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''   )

									snake_case_		     =							tf.constant(
									    [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]   )  # [CLS] hello, is my dog cute? [SEP]
									snake_case_		     =							model(lowercase_   )[0]  # embedding shape = (1, 768)
									# compare the actual values for a slice.
									snake_case_		     =							tf.constant(
									    [
									        [
									            0.0323_6253,
									            0.1275_3335,
									            0.1681_8509,
									            0.0027_9786,
									            0.389_6933,
									            0.2426_4945,
									            0.217_8971,
									            -0.0233_5227,
									            -0.0848_1959,
									            -0.1432_4117,
									        ]
									    ]   )
									self.assertTrue(numpy.allclose(output[:, :10].numpy()			,							expected_slice.numpy()			,							atol=1e-4   )   )

