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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
     raise Exception('''requires fairseq >= 0.9.0''')


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

__snake_case         =     '''Hello world! cécé herlolip'''





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Any:






      '''simple docstring'''





      UpperCAmelCase     :			int     	=FairseqRobertaModel.from_pretrained(__lowerCAmelCase    )
      roberta.eval()  # disable dropout
      UpperCAmelCase     :			int     	=roberta.model.encoder.sentence_encoder
      UpperCAmelCase     :			List[str]     	=RobertaConfig(
          vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings		,       hidden_size=roberta.args.encoder_embed_dim		,       num_hidden_layers=roberta.args.encoder_layers		,       num_attention_heads=roberta.args.encoder_attention_heads		,       intermediate_size=roberta.args.encoder_ffn_embed_dim		,       max_position_embeddings=5_14		,       type_vocab_size=1		,       layer_norm_eps=1e-5		,       )
      if classification_head:
            UpperCAmelCase     :			Optional[int]     	=roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
      print('''Our BERT config:'''		,       __lowerCAmelCase    )

      UpperCAmelCase     :			str     	=RobertaForSequenceClassification(__lowerCAmelCase    ) if classification_head else RobertaForMaskedLM(__lowerCAmelCase    )
      model.eval()

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

      for i in range(config.num_hidden_layers    ):
            # Encoder: start of layer
            UpperCAmelCase     :			List[Any]     	=model.roberta.encoder.layer[i]
            UpperCAmelCase     :			Dict     	=roberta_sent_encoder.layers[i]

            # self attention
            UpperCAmelCase     :			str     	=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)    )
            )

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

            # self-attention output
            UpperCAmelCase     :			Dict     	=layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            UpperCAmelCase     :			Dict     	=roberta_layer.self_attn.out_proj.weight
            UpperCAmelCase     :			List[str]     	=roberta_layer.self_attn.out_proj.bias
            UpperCAmelCase     :			Union[str, Any]     	=roberta_layer.self_attn_layer_norm.weight
            UpperCAmelCase     :			Optional[Any]     	=roberta_layer.self_attn_layer_norm.bias

            # intermediate
            UpperCAmelCase     :			Dict     	=layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            UpperCAmelCase     :			Tuple     	=roberta_layer.fca.weight
            UpperCAmelCase     :			Any     	=roberta_layer.fca.bias

            # output
            UpperCAmelCase     :			Any     	=layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            UpperCAmelCase     :			int     	=roberta_layer.fca.weight
            UpperCAmelCase     :			int     	=roberta_layer.fca.bias
            UpperCAmelCase     :			Optional[int]     	=roberta_layer.final_layer_norm.weight
            UpperCAmelCase     :			Optional[Any]     	=roberta_layer.final_layer_norm.bias
            # end of layer

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

      # Let's check that we get the same results.
      UpperCAmelCase     :			List[str]     	=roberta.encode(__lowerCAmelCase    ).unsqueeze(0    )  # batch of size 1

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

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


if __name__ == "__main__":
     __snake_case         =     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.'''
     )
     __snake_case         =     parser.parse_args()
     convert_roberta_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )







from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
						import tensorflow as tf

						from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=32						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=[10, 20, 30, 40]						,	_lowerCAmelCase=[2, 2, 3, 2]						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=37						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=10						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=3						,	_lowerCAmelCase=None						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							num_stages
										lowerCamelCase__      =							hidden_sizes
										lowerCamelCase__      =							depths
										lowerCamelCase__      =							is_training
										lowerCamelCase__      =							use_labels
										lowerCamelCase__      =							intermediate_size
										lowerCamelCase__      =							hidden_act
										lowerCamelCase__      =							type_sequence_label_size
										lowerCamelCase__      =							initializer_range
										lowerCamelCase__      =							scope





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

										lowerCamelCase__      =							self.get_config()

										return config, pixel_values, labels





					def        __magic_name__			(			self						):
										return ConvNextConfig(
										    num_channels=self.num_channels						,	hidden_sizes=self.hidden_sizes						,	depths=self.depths						,	num_stages=self.num_stages						,	hidden_act=self.hidden_act						,	is_decoder=_lowerCAmelCase						,	initializer_range=self.initializer_range						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFConvNextModel(config=_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	training=_lowerCAmelCase						)
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape						,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.type_sequence_label_size
										lowerCamelCase__      =							TFConvNextForImageClassification(_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	labels=_lowerCAmelCase						,	training=_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)						)






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

@require_tf
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					A__								=     (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModelTester(self						)
										lowerCamelCase__      =							ConfigTester(
										    self						,	config_class=_lowerCAmelCase						,	has_text_modality=_lowerCAmelCase						,	hidden_size=37						,	)





					@unittest.skip(reason="ConvNext does not use inputs_embeds"						)
					def        __magic_name__			(			self						):
										pass





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0						,	reason="TF does not support backprop for grouped convolutions on CPU."						,	)
					@slow
					def        __magic_name__			(			self						):
										super().test_keras_fit()





					@unittest.skip(reason="ConvNext does not support input and output embeddings"						)
					def        __magic_name__			(			self						):
										pass





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

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

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





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase						)





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0						,	reason="TF does not support backprop for grouped convolutions on CPU."						,	)
					def        __magic_name__			(			self						):
										super().test_dataset_conversion()





					def        __magic_name__			(			self						):
										def check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							model(**self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)						)
															lowerCamelCase__      =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															lowerCamelCase__      =							self.model_tester.num_stages
															self.assertEqual(len(_lowerCAmelCase						)						,	expected_num_stages + 1						)

															# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]						)						,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,	)

										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							True
															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

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

															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase={}						):
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						)
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						).to_tuple()

															def recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						):
																				if isinstance(_lowerCAmelCase						,	(List, Tuple)						):
																									for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase						,	_lowerCAmelCase						):
																														recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						)
																				elif tuple_object is None:
																									return
																				else:
																									self.assertTrue(
																									    all(tf.equal(_lowerCAmelCase						,	_lowerCAmelCase						)						)						,	msg=(
																									        "Tuple and dict output are not equal. Difference:"
																									        F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}"
																									    )						,	)

																				recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						)

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
										self.assertIsNotNone(_lowerCAmelCase						)





def  __UpperCamelCase   (     )			->List[str]:
					lowerCamelCase__      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
					return image







@require_tf
@require_vision
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@cached_property
					def        __magic_name__			(			self						):
										return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)

										lowerCamelCase__      =							self.default_image_processor
										lowerCamelCase__      =							prepare_img()
										lowerCamelCase__      =							image_processor(images=_lowerCAmelCase						,	return_tensors="tf"						)

										# forward pass
										lowerCamelCase__      =							model(**_lowerCAmelCase						)

										# verify the logits
										lowerCamelCase__      =							tf.TensorShape((1, 1000)						)
										self.assertEqual(outputs.logits.shape						,	_lowerCAmelCase						)

										lowerCamelCase__      =							tf.constant([-0.0_260, -0.4_739, 0.1_911]						)

										tf.debugging.assert_near(outputs.logits[0, :3]						,	_lowerCAmelCase						,	atol=1E-4						)

