

"""simple docstring"""





import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
							raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
__UpperCamelCase  :      Optional[int]							      =		logging.get_logger(__name__)

__UpperCamelCase  :      Optional[Any]							      =		'''Hello world! cécé herlolip'''

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Optional[Any]  				=	FairseqRobertaModel.from_pretrained(A_ )
	roberta.eval()  # disable dropout
	lowerCAmelCase__	:						str  				=	roberta.model.encoder.sentence_encoder
	lowerCAmelCase__	:						Tuple  				=	XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings						,     hidden_size=roberta.cfg.model.encoder_embed_dim						,     num_hidden_layers=roberta.cfg.model.encoder_layers						,     num_attention_heads=roberta.cfg.model.encoder_attention_heads						,     intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim						,     max_position_embeddings=5_14						,     type_vocab_size=1						,     layer_norm_eps=1e-5						,     )
	if classification_head:
		lowerCAmelCase__	:						Optional[Any]  				=	roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our RoBERTa config:'''						,     A_ )

	lowerCAmelCase__	:						Optional[int]  				=	XLMRobertaXLForSequenceClassification(A_ ) if classification_head else XLMRobertaXLForMaskedLM(A_ )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCAmelCase__	:						Union[str, Any]  				=	roberta_sent_encoder.embed_tokens.weight
	lowerCAmelCase__	:						Dict  				=	roberta_sent_encoder.embed_positions.weight
	lowerCAmelCase__	:						int  				=	torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight )  # just zero them out b/c RoBERTa doesn't use them.

	lowerCAmelCase__	:						Union[str, Any]  				=	roberta_sent_encoder.layer_norm.weight
	lowerCAmelCase__	:						List[Any]  				=	roberta_sent_encoder.layer_norm.bias

	for i in range(config.num_hidden_layers ):
		# Encoder: start of layer
		lowerCAmelCase__	:						BertLayer  				=	model.roberta.encoder.layer[i]
		lowerCAmelCase__	:						TransformerSentenceEncoderLayer  				=	roberta_sent_encoder.layers[i]

		lowerCAmelCase__	:						RobertaAttention  				=	layer.attention
		lowerCAmelCase__	:						Tuple  				=	roberta_layer.self_attn_layer_norm.weight
		lowerCAmelCase__	:						int  				=	roberta_layer.self_attn_layer_norm.bias

		# self attention
		lowerCAmelCase__	:						BertSelfAttention  				=	layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size) )
		)

		lowerCAmelCase__	:						List[str]  				=	roberta_layer.self_attn.q_proj.weight
		lowerCAmelCase__	:						int  				=	roberta_layer.self_attn.q_proj.bias
		lowerCAmelCase__	:						Union[str, Any]  				=	roberta_layer.self_attn.k_proj.weight
		lowerCAmelCase__	:						Tuple  				=	roberta_layer.self_attn.k_proj.bias
		lowerCAmelCase__	:						Optional[Any]  				=	roberta_layer.self_attn.v_proj.weight
		lowerCAmelCase__	:						Any  				=	roberta_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCAmelCase__	:						BertSelfOutput  				=	layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowerCAmelCase__	:						List[Any]  				=	roberta_layer.self_attn.out_proj.weight
		lowerCAmelCase__	:						Dict  				=	roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		lowerCAmelCase__	:						Any  				=	roberta_layer.final_layer_norm.weight
		lowerCAmelCase__	:						str  				=	roberta_layer.final_layer_norm.bias

		# intermediate
		lowerCAmelCase__	:						BertIntermediate  				=	layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowerCAmelCase__	:						Dict  				=	roberta_layer.fca.weight
		lowerCAmelCase__	:						List[Any]  				=	roberta_layer.fca.bias

		# output
		lowerCAmelCase__	:						BertOutput  				=	layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		lowerCAmelCase__	:						Any  				=	roberta_layer.fca.weight
		lowerCAmelCase__	:						List[Any]  				=	roberta_layer.fca.bias
		# end of layer

	if classification_head:
		lowerCAmelCase__	:						Dict  				=	roberta.model.classification_heads['''mnli'''].dense.weight
		lowerCAmelCase__	:						Union[str, Any]  				=	roberta.model.classification_heads['''mnli'''].dense.bias
		lowerCAmelCase__	:						Any  				=	roberta.model.classification_heads['''mnli'''].out_proj.weight
		lowerCAmelCase__	:						Optional[Any]  				=	roberta.model.classification_heads['''mnli'''].out_proj.bias
	else:
		# LM Head
		lowerCAmelCase__	:						Optional[Any]  				=	roberta.model.encoder.lm_head.dense.weight
		lowerCAmelCase__	:						Union[str, Any]  				=	roberta.model.encoder.lm_head.dense.bias
		lowerCAmelCase__	:						List[str]  				=	roberta.model.encoder.lm_head.layer_norm.weight
		lowerCAmelCase__	:						Union[str, Any]  				=	roberta.model.encoder.lm_head.layer_norm.bias
		lowerCAmelCase__	:						int  				=	roberta.model.encoder.lm_head.weight
		lowerCAmelCase__	:						Optional[Any]  				=	roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowerCAmelCase__	:						torch.Tensor  				=	roberta.encode(A_ ).unsqueeze(0 )  # batch of size 1

	lowerCAmelCase__	:						str  				=	model(A_ )[0]
	if classification_head:
		lowerCAmelCase__	:						Optional[int]  				=	roberta.model.classification_heads['''mnli'''](roberta.extract_features(A_ ) )
	else:
		lowerCAmelCase__	:						Dict  				=	roberta.model(A_ )[0]
	print(our_output.shape						,     their_output.shape )
	lowerCAmelCase__	:						Any  				=	torch.max(torch.abs(our_output - their_output ) ).item()
	print(f'max_absolute_diff = {max_absolute_diff}' )  # ~ 1e-7
	lowerCAmelCase__	:						int  				=	torch.allclose(A_						,     A_						,     atol=1e-3 )
	print('''Do both models output the same tensors?'''						,     '''🔥''' if success else '''💩''' )
	if not success:
		raise Exception('''Something went wRoNg''' )

	pathlib.Path(A_ ).mkdir(parents=A_						,     exist_ok=A_ )
	print(f'Saving model to {pytorch_dump_folder_path}' )
	model.save_pretrained(A_ )


if __name__ == "__main__":
							__UpperCamelCase  :      Optional[int]							      =		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--roberta_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.'''
							)
							__UpperCamelCase  :      int							      =		parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)






"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel


class A_	(unittest.TestCase      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=13				,   lowercase_=30				,   lowercase_=2				,   lowercase_=3				,   lowercase_=True				,   lowercase_=True				,   lowercase_=32				,   lowercase_=5				,   lowercase_=4				,   lowercase_=37				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=10				,   lowercase_=0.02				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    parent
     UpperCAmelCase_					:					List[str]			     =    batch_size
     UpperCAmelCase_					:					Union[str, Any]			     =    image_size
     UpperCAmelCase_					:					List[str]			     =    patch_size
     UpperCAmelCase_					:					Union[str, Any]			     =    num_channels
     UpperCAmelCase_					:					Optional[int]			     =    is_training
     UpperCAmelCase_					:					Dict			     =    use_labels
     UpperCAmelCase_					:					Any			     =    hidden_size
     UpperCAmelCase_					:					Optional[Any]			     =    num_hidden_layers
     UpperCAmelCase_					:					Optional[Any]			     =    num_attention_heads
     UpperCAmelCase_					:					Dict			     =    intermediate_size
     UpperCAmelCase_					:					Optional[Any]			     =    hidden_act
     UpperCAmelCase_					:					Optional[Any]			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Tuple			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Dict			     =    type_sequence_label_size
     UpperCAmelCase_					:					Optional[Any]			     =    initializer_range

     # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     UpperCAmelCase_					:					Any			     =    (image_size // patch_size) ** 2
     UpperCAmelCase_					:					List[str]			     =    num_patches + 1







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

     UpperCAmelCase_					:					Dict			     =    ViTConfig(
         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=lowercase_				,   initializer_range=self.initializer_range				,   )

     return config, pixel_values







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    FlaxViTModel(config=lowercase_  )
     UpperCAmelCase_					:					int			     =    model(lowercase_  )
     # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
     UpperCAmelCase_					:					Optional[Any]			     =    (self.image_size, self.image_size)
     UpperCAmelCase_					:					List[Any]			     =    (self.patch_size, self.patch_size)
     UpperCAmelCase_					:					str			     =    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
     self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, num_patches + 1, self.hidden_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.type_sequence_label_size
     UpperCAmelCase_					:					Tuple			     =    FlaxViTForImageClassification(config=lowercase_  )
     UpperCAmelCase_					:					str			     =    model(lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)  )

     # test greyscale images
     UpperCAmelCase_					:					Any			     =    1
     UpperCAmelCase_					:					Optional[int]			     =    FlaxViTForImageClassification(lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
     UpperCAmelCase_					:					List[Any]			     =    model(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.prepare_config_and_inputs()
     (
         (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   
     )					:					Tuple			     =    config_and_inputs
     UpperCAmelCase_					:					Union[str, Any]			     =    {"pixel_values": pixel_values}
     return config, inputs_dict




@require_flax
class A_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Any       =		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    FlaxViTModelTester(self  )
     UpperCAmelCase_					:					Dict			     =    ConfigTester(self				,   config_class=lowercase_				,   has_text_modality=lowercase_				,   hidden_size=37  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					Optional[Any]			     =    model_class(lowercase_  )
        UpperCAmelCase_					:					Optional[int]			     =    inspect.signature(model.__call__  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCAmelCase_					:					List[str]			     =    [*signature.parameters.keys()]

        UpperCAmelCase_					:					List[str]			     =    ["pixel_values"]
        self.assertListEqual(arg_names[:1]				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
           UpperCAmelCase_					:					Union[str, Any]			     =    self._prepare_for_class(lowercase_				,   lowercase_  )
           UpperCAmelCase_					:					Tuple			     =    model_class(lowercase_  )

           @jax.jit
           def model_jitted(lowercase_				,   **lowercase_  ):
              return model(pixel_values=lowercase_				,   **lowercase_  )

           with self.subTest("JIT Enabled"  ):
              UpperCAmelCase_					:					Union[str, Any]			     =    model_jitted(**lowercase_  ).to_tuple()

           with self.subTest("JIT Disabled"  ):
              with jax.disable_jit():
                 UpperCAmelCase_					:					Tuple			     =    model_jitted(**lowercase_  ).to_tuple()

           self.assertEqual(len(lowercase_  )				,   len(lowercase_  )  )
           for jitted_output, output in zip(lowercase_				,   lowercase_  ):
              self.assertEqual(jitted_output.shape				,   output.shape  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_class_name in self.all_model_classes:
        UpperCAmelCase_					:					Union[str, Any]			     =    model_class_name.from_pretrained("google/vit-base-patch16-224"  )
        UpperCAmelCase_					:					List[str]			     =    model(np.ones((1, 3, 224, 224)  )  )
        self.assertIsNotNone(lowercase_  )


