
"""simple docstring"""





import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
				raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
				raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
_a								=						logging.get_logger(__name__)

_a								=						"""Hello, World!"""
_a								=						"""en_XX"""



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    Path('''data_bin'''      )
					_UpperCamelCase									=    FairseqXmodModel.from_pretrained(
					    model_name_or_path=str(Path(__snake_case      ).parent      ),	checkpoint_file=Path(__snake_case      ).name,	_name='''xmod_base''',	arch='''xmod_base''',	task='''multilingual_masked_lm''',	data_name_or_path=str(__snake_case      ),	bpe='''sentencepiece''',	sentencepiece_model=str(Path(__snake_case      ).parent / '''sentencepiece.bpe.model'''      ),	src_dict=str(data_dir / '''dict.txt'''      ),	)
					xmod.eval()  # disable dropout
					print(__snake_case      )

					_UpperCamelCase									=    xmod.model.encoder.sentence_encoder
					_UpperCamelCase									=    XmodConfig(
					    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings,	hidden_size=xmod.cfg.model.encoder_embed_dim,	num_hidden_layers=xmod.cfg.model.encoder_layers,	num_attention_heads=xmod.cfg.model.encoder_attention_heads,	intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim,	max_position_embeddings=5_14,	type_vocab_size=1,	layer_norm_eps=1e-5,	pre_norm=xmod.cfg.model.encoder_normalize_before,	adapter_reduction_factor=getattr(xmod.cfg.model,	'''bottleneck''',	2      ),	adapter_layer_norm=xmod.cfg.model.adapter_layer_norm,	adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm,	ln_before_adapter=xmod.cfg.model.ln_before_adapter,	languages=xmod.cfg.model.languages,	)
					if classification_head:
										_UpperCamelCase									=    xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

					print('''Our X-MOD config:''',	__snake_case      )

					_UpperCamelCase									=    XmodForSequenceClassification(__snake_case      ) if classification_head else XmodForMaskedLM(__snake_case      )
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					_UpperCamelCase									=    xmod_sent_encoder.embed_tokens.weight
					_UpperCamelCase									=    xmod_sent_encoder.embed_positions.weight
					_UpperCamelCase									=    torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c xmod doesn't use them.

					_UpperCamelCase									=    xmod_sent_encoder.layernorm_embedding.weight
					_UpperCamelCase									=    xmod_sent_encoder.layernorm_embedding.bias

					for i in range(config.num_hidden_layers      ):
										# Encoder: start of layer
										_UpperCamelCase									=    model.roberta.encoder.layer[i]
										_UpperCamelCase									=    xmod_sent_encoder.layers[i]

										# self attention
										_UpperCamelCase									=    layer.attention.self
										if not (
										    xmod_layer.self_attn.k_proj.weight.data.shape
										    == xmod_layer.self_attn.q_proj.weight.data.shape
										    == xmod_layer.self_attn.v_proj.weight.data.shape
										    == torch.Size((config.hidden_size, config.hidden_size)      )
										):
															raise AssertionError('''Dimensions of self-attention weights do not match.'''      )

										_UpperCamelCase									=    xmod_layer.self_attn.q_proj.weight
										_UpperCamelCase									=    xmod_layer.self_attn.q_proj.bias
										_UpperCamelCase									=    xmod_layer.self_attn.k_proj.weight
										_UpperCamelCase									=    xmod_layer.self_attn.k_proj.bias
										_UpperCamelCase									=    xmod_layer.self_attn.v_proj.weight
										_UpperCamelCase									=    xmod_layer.self_attn.v_proj.bias

										# self-attention output
										_UpperCamelCase									=    layer.attention.output
										if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
															raise AssertionError('''Dimensions of self-attention output weights do not match.'''      )
										_UpperCamelCase									=    xmod_layer.self_attn.out_proj.weight
										_UpperCamelCase									=    xmod_layer.self_attn.out_proj.bias
										_UpperCamelCase									=    xmod_layer.self_attn_layer_norm.weight
										_UpperCamelCase									=    xmod_layer.self_attn_layer_norm.bias

										# intermediate
										_UpperCamelCase									=    layer.intermediate
										if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError('''Dimensions of intermediate weights do not match.'''      )
										_UpperCamelCase									=    xmod_layer.fca.weight
										_UpperCamelCase									=    xmod_layer.fca.bias

										# output
										_UpperCamelCase									=    layer.output
										if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError('''Dimensions of feed-forward weights do not match.'''      )
										_UpperCamelCase									=    xmod_layer.fca.weight
										_UpperCamelCase									=    xmod_layer.fca.bias
										_UpperCamelCase									=    xmod_layer.final_layer_norm.weight
										_UpperCamelCase									=    xmod_layer.final_layer_norm.bias
										if bert_output.adapter_layer_norm is not None:
															_UpperCamelCase									=    xmod_layer.adapter_layer_norm.weight
															_UpperCamelCase									=    xmod_layer.adapter_layer_norm.bias

										if sorted(bert_output.adapter_modules.keys()      ) != sorted(xmod_layer.adapter_modules.keys()      ):
															raise AssertionError('''Lists of language adapters do not match.'''      )
										for lang_code, adapter in xmod_layer.adapter_modules.items():
															_UpperCamelCase									=    bert_output.adapter_modules[lang_code]
															_UpperCamelCase									=    xmod_layer.adapter_modules[lang_code]
															_UpperCamelCase									=    from_adapter.fca.weight
															_UpperCamelCase									=    from_adapter.fca.bias
															_UpperCamelCase									=    from_adapter.fca.weight
															_UpperCamelCase									=    from_adapter.fca.bias

										# end of layer

					if xmod_sent_encoder.layer_norm is not None:
										_UpperCamelCase									=    xmod_sent_encoder.layer_norm.weight
										_UpperCamelCase									=    xmod_sent_encoder.layer_norm.bias

					if classification_head:
										_UpperCamelCase									=    xmod.model.classification_heads['''mnli'''].dense.weight
										_UpperCamelCase									=    xmod.model.classification_heads['''mnli'''].dense.bias
										_UpperCamelCase									=    xmod.model.classification_heads['''mnli'''].out_proj.weight
										_UpperCamelCase									=    xmod.model.classification_heads['''mnli'''].out_proj.bias
					else:
										# LM Head
										_UpperCamelCase									=    xmod.model.encoder.lm_head.dense.weight
										_UpperCamelCase									=    xmod.model.encoder.lm_head.dense.bias
										_UpperCamelCase									=    xmod.model.encoder.lm_head.layer_norm.weight
										_UpperCamelCase									=    xmod.model.encoder.lm_head.layer_norm.bias
										_UpperCamelCase									=    xmod.model.encoder.lm_head.weight
										_UpperCamelCase									=    xmod.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					_UpperCamelCase									=    xmod.encode(__snake_case      ).unsqueeze(0      )  # batch of size 1
					model.roberta.set_default_language(__snake_case      )

					_UpperCamelCase									=    model(__snake_case      )[0]
					if classification_head:
										_UpperCamelCase									=    xmod.model.classification_heads['''mnli'''](xmod.extract_features(__snake_case      )      )
					else:
										_UpperCamelCase									=    xmod.model(__snake_case,	lang_id=[SAMPLE_LANGUAGE]      )[0]
					print(our_output.shape,	their_output.shape      )
					_UpperCamelCase									=    torch.max(torch.abs(our_output - their_output      )      ).item()
					print(F'''max_absolute_diff = {max_absolute_diff}'''      )  # ~ 1e-7
					_UpperCamelCase									=    torch.allclose(__snake_case,	__snake_case,	atol=1e-3      )
					print('''Do both models output the same tensors?''',	'''🔥''' if success else '''💩'''      )
					if not success:
										raise Exception('''Something went wRoNg'''      )

					Path(__snake_case      ).mkdir(parents=__snake_case,	exist_ok=__snake_case      )
					print(F'''Saving model to {pytorch_dump_folder_path}'''      )
					model.save_pretrained(__snake_case      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				parser.add_argument(
				    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
				)
				_a								=						parser.parse_args()
				convert_xmod_checkpoint_to_pytorch(
				    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)








"""simple docstring"""





import inspect
import unittest

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

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


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
				from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=64							,						__a=2							,						__a=3							,						__a=True							,						__a=True							,						__a=32							,						__a=5							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=10							,						__a=0.02							,						__a=[1, 16, 4, 4]							,						__a=None							,						)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    image_size
							_UpperCamelCase									=    patch_size
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    type_sequence_label_size
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    scope
							_UpperCamelCase									=    backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							_UpperCamelCase									=    (self.image_size // 32) ** 2
							_UpperCamelCase									=    num_patches + 1



		def 		UpperCAmelCase				(   self)			->			int:






							'''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 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [4, 8, 16, 32],
							    '''num_groups''': 2,
							}

							return ViTHybridConfig(
							    image_size=self.image_size							,						patch_size=self.patch_size							,						num_channels=self.num_channels							,						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							,						is_decoder=__a							,						initializer_range=self.initializer_range							,						backbone_featmap_shape=self.backbone_featmap_shape							,						backbone_config=__a							,						)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))



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






							'''simple docstring'''
							_UpperCamelCase									=    self.type_sequence_label_size
							_UpperCamelCase									=    ViTHybridForImageClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size))




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






							'''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        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
		lowercase__         = (
		    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
		    if is_torch_available()
		    else {}
		)
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



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






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						has_text_modality=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViT does not use inputs_embeds''')
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							pass



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






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

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



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)
												_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]							,						__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase									=    _config_zero_init(__a)
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(config=__a)
												# Skip the check for the backbone
												for name, module in model.named_modules():
																	if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																						_UpperCamelCase									=    [F'''{name}.{key}''' for key in module.state_dict().keys()]
																						break

												for name, param in model.named_parameters():
																	if param.requires_grad:
																						if name in backbone_params:
																											continue
																						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'''							,						)




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






							'''simple docstring'''
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    ViTHybridModel.from_pretrained(__a)
												self.assertIsNotNone(__a)





def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image






@require_torch
@require_vision
class        _UpperCAmelCase(       unittest.TestCase      ):



		@cached_property
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
							    if is_vision_available()
							    else None
							)



		@slow
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
							    __a)

							_UpperCamelCase									=    self.default_image_processor
							_UpperCamelCase									=    prepare_img()
							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''').to(__a)

							# forward pass
							with torch.no_grad():
												_UpperCamelCase									=    model(**__a)

							# verify the logits
							_UpperCamelCase									=    torch.Size((1, 10_00))
							self.assertEqual(outputs.logits.shape							,						__a)

							_UpperCamelCase									=    torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)

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




		@slow
		@require_accelerate
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
							_UpperCamelCase									=    ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''							,						device_map='''auto''')

							_UpperCamelCase									=    prepare_img()

							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''')
							_UpperCamelCase									=    model(**__a)
							_UpperCamelCase									=    outputs.logits
							# model predicts one of the 1000 ImageNet classes
							_UpperCamelCase									=    logits.argmax(-1).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx]							,						'''tabby, tabby cat''')






