





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()
_UpperCAmelCase       : Any			     =       logging.get_logger(__name__)

_UpperCAmelCase       : str			     =       """Hello, World!"""
_UpperCAmelCase       : Optional[int]			     =       """en_XX"""


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Union[str, Any]:
    lowerCamelCase__					:   Any      				=	Path('data_bin'				)
    lowerCamelCase__					:   Optional[int]      				=	FairseqXmodModel.from_pretrained(
        model_name_or_path=str(Path(_UpperCAmelCase				).parent				)		,			checkpoint_file=Path(_UpperCAmelCase				).name		,			_name='xmod_base'		,			arch='xmod_base'		,			task='multilingual_masked_lm'		,			data_name_or_path=str(_UpperCAmelCase				)		,			bpe='sentencepiece'		,			sentencepiece_model=str(Path(_UpperCAmelCase				).parent / 'sentencepiece.bpe.model'				)		,			src_dict=str(data_dir / 'dict.txt'				)		,			)
    xmod.eval()  # disable dropout
    print(_UpperCAmelCase				)

    lowerCamelCase__					:   Optional[int]      				=	xmod.model.encoder.sentence_encoder
    lowerCamelCase__					:   int      				=	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=514		,			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:
        lowerCamelCase__					:   int      				=	xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

    lowerCamelCase__					:   Union[str, Any]      				=	XmodForSequenceClassification(_UpperCAmelCase				) if classification_head else XmodForMaskedLM(_UpperCAmelCase				)
    model.eval()

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

    lowerCamelCase__					:   Dict      				=	xmod_sent_encoder.layernorm_embedding.weight
    lowerCamelCase__					:   Tuple      				=	xmod_sent_encoder.layernorm_embedding.bias

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

        # self attention
        lowerCamelCase__					:   Optional[Any]      				=	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.'				)

        lowerCamelCase__					:   Any      				=	xmod_layer.self_attn.q_proj.weight
        lowerCamelCase__					:   str      				=	xmod_layer.self_attn.q_proj.bias
        lowerCamelCase__					:   Union[str, Any]      				=	xmod_layer.self_attn.k_proj.weight
        lowerCamelCase__					:   Tuple      				=	xmod_layer.self_attn.k_proj.bias
        lowerCamelCase__					:   Dict      				=	xmod_layer.self_attn.v_proj.weight
        lowerCamelCase__					:   List[Any]      				=	xmod_layer.self_attn.v_proj.bias

        # self-attention output
        lowerCamelCase__					:   List[str]      				=	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.'				)
        lowerCamelCase__					:   str      				=	xmod_layer.self_attn.out_proj.weight
        lowerCamelCase__					:   Optional[int]      				=	xmod_layer.self_attn.out_proj.bias
        lowerCamelCase__					:   List[Any]      				=	xmod_layer.self_attn_layer_norm.weight
        lowerCamelCase__					:   Dict      				=	xmod_layer.self_attn_layer_norm.bias

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

        # output
        lowerCamelCase__					:   List[str]      				=	layer.output
        if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
            raise AssertionError('Dimensions of feed-forward weights do not match.'				)
        lowerCamelCase__					:   str      				=	xmod_layer.fca.weight
        lowerCamelCase__					:   Optional[Any]      				=	xmod_layer.fca.bias
        lowerCamelCase__					:   List[Any]      				=	xmod_layer.final_layer_norm.weight
        lowerCamelCase__					:   Optional[int]      				=	xmod_layer.final_layer_norm.bias
        if bert_output.adapter_layer_norm is not None:
            lowerCamelCase__					:   Optional[int]      				=	xmod_layer.adapter_layer_norm.weight
            lowerCamelCase__					:   Union[str, Any]      				=	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():
            lowerCamelCase__					:   Dict      				=	bert_output.adapter_modules[lang_code]
            lowerCamelCase__					:   str      				=	xmod_layer.adapter_modules[lang_code]
            lowerCamelCase__					:   List[Any]      				=	from_adapter.fca.weight
            lowerCamelCase__					:   Union[str, Any]      				=	from_adapter.fca.bias
            lowerCamelCase__					:   Optional[Any]      				=	from_adapter.fca.weight
            lowerCamelCase__					:   Any      				=	from_adapter.fca.bias

        # end of layer

    if xmod_sent_encoder.layer_norm is not None:
        lowerCamelCase__					:   List[Any]      				=	xmod_sent_encoder.layer_norm.weight
        lowerCamelCase__					:   List[str]      				=	xmod_sent_encoder.layer_norm.bias

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

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

    lowerCamelCase__					:   Union[str, Any]      				=	model(_UpperCAmelCase				)[0]
    if classification_head:
        lowerCamelCase__					:   Tuple      				=	xmod.model.classification_heads['mnli'](xmod.extract_features(_UpperCAmelCase				)				)
    else:
        lowerCamelCase__					:   Tuple      				=	xmod.model(_UpperCAmelCase		,			lang_id=[SAMPLE_LANGUAGE]				)[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__					:   Optional[Any]      				=	torch.allclose(_UpperCAmelCase		,			_UpperCAmelCase		,			atol=1e-3				)
    print('Do both models output the same tensors?'		,			'🔥' if success else '💩'				)
    if not success:
        raise Exception('Something went wRoNg'				)

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


if __name__ == "__main__":
       _UpperCAmelCase       : str			     =       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."""
       )
       _UpperCAmelCase       : List[str]			     =       parser.parse_args()
       convert_xmod_checkpoint_to_pytorch(
           args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )












import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class      lowerCAmelCase							(      unittest.TestCase    ):
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[Any]=3    ,  UpperCAmelCase						:   str=32    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Dict=10    ,  UpperCAmelCase						:   List[str]=[10, 20, 30, 40]    ,  UpperCAmelCase						:   Any=[1, 1, 2, 1]    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   Dict="relu"    ,  UpperCAmelCase						:   Tuple=3    ,  UpperCAmelCase						:   Dict=None    ,  )    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	parent
     lowerCamelCase__					:   Optional[Any]      				=	batch_size
     lowerCamelCase__					:   Dict      				=	image_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embeddings_size
     lowerCamelCase__					:   str      				=	hidden_sizes
     lowerCamelCase__					:   Any      				=	depths
     lowerCamelCase__					:   str      				=	is_training
     lowerCamelCase__					:   List[Any]      				=	use_labels
     lowerCamelCase__					:   Union[str, Any]      				=	hidden_act
     lowerCamelCase__					:   Dict      				=	num_labels
     lowerCamelCase__					:   Dict      				=	scope
     lowerCamelCase__					:   List[str]      				=	len(UpperCAmelCase			)
 def   A_			(   self						:   Optional[int]			)    ->			Optional[int]:
     lowerCamelCase__					:   int      				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

     lowerCamelCase__					:   str      				=	self.get_config()

     return config, pixel_values
 def   A_			(   self						:   Optional[int]			)    ->			Dict:
     return RegNetConfig(
         num_channels=self.num_channels    ,  embeddings_size=self.embeddings_size    ,  hidden_sizes=self.hidden_sizes    ,  depths=self.depths    ,  hidden_act=self.hidden_act    ,  num_labels=self.num_labels    ,  image_size=self.image_size    ,  )
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Any			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	FlaxRegNetModel(config=UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	model(UpperCAmelCase			)

     # Output shape (b, c, h, w)
     self.parent.assertEqual(
         result.last_hidden_state.shape    ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,  )
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Tuple			)    ->			Tuple:
     lowerCamelCase__					:   List[str]      				=	self.num_labels
     lowerCamelCase__					:   str      				=	FlaxRegNetForImageClassification(config=UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	model(UpperCAmelCase			)
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)			)







 def   A_			(   self						:   Dict			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	self.prepare_config_and_inputs()
     lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	config_and_inputs
     lowerCamelCase__					:   Dict      				=	{'pixel_values': pixel_values}
     return config, inputs_dict


@require_flax
class      lowerCAmelCase							(      __UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 def   A_			(   self						:   List[Any]			)    ->			None:
     lowerCamelCase__					:   List[Any]      				=	FlaxRegNetModelTester(self			)
     lowerCamelCase__					:   Union[str, Any]      				=	ConfigTester(self    ,  config_class=UpperCAmelCase    ,  has_text_modality=UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			Tuple:
     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   A_			(   self						:   Any			)    ->			str:
     return
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase			)
 def   A_			(   self						:   Dict			)    ->			Optional[Any]:
     lowerCamelCase__					:   Any      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase			)
 @unittest.skip(reason='RegNet does not use inputs_embeds'			)
 def   A_			(   self						:   Dict			)    ->			Dict:
     pass
 @unittest.skip(reason='RegNet does not support input and output embeddings'			)
 def   A_			(   self						:   Any			)    ->			Tuple:
     pass
 def   A_			(   self						:   Union[str, Any]			)    ->			Union[str, Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	self.model_tester.prepare_config_and_inputs_for_common()

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

         lowerCamelCase__					:   int      				=	['pixel_values']
         self.assertListEqual(arg_names[:1]    ,  UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			List[str]:
     def check_hidden_states_output(UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Optional[Any]			):
         lowerCamelCase__					:   Any      				=	model_class(UpperCAmelCase			)

         lowerCamelCase__					:   Optional[Any]      				=	model(**self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)			)

         lowerCamelCase__					:   Union[str, Any]      				=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         lowerCamelCase__					:   Optional[int]      				=	self.model_tester.num_stages
         self.assertEqual(len(UpperCAmelCase			)    ,  expected_num_stages + 1			)

     lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__					:   List[str]      				=	True
         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

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

         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Dict			)    ->			int:
     lowerCamelCase__				,							lowerCamelCase__					:   int      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
             lowerCamelCase__					:   int      				=	self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)
             lowerCamelCase__					:   Tuple      				=	model_class(UpperCAmelCase			)

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

             with self.subTest('JIT Enabled'			):
                 lowerCamelCase__					:   Dict      				=	model_jitted(**UpperCAmelCase			).to_tuple()

             with self.subTest('JIT Disabled'			):
                 with jax.disable_jit():
                     lowerCamelCase__					:   Optional[int]      				=	model_jitted(**UpperCAmelCase			).to_tuple()

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


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[int]      				=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image


@require_flax
class      lowerCAmelCase							(      unittest.TestCase    ):
 @cached_property
 def   A_			(   self						:   Any			)    ->			List[Any]:
     return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'			) if is_vision_available() else None







 @slow
 def   A_			(   self						:   Dict			)    ->			Tuple:
     lowerCamelCase__					:   Union[str, Any]      				=	FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'			)

     lowerCamelCase__					:   Dict      				=	self.default_image_processor
     lowerCamelCase__					:   List[Any]      				=	prepare_img()
     lowerCamelCase__					:   List[str]      				=	image_processor(images=UpperCAmelCase    ,  return_tensors='np'			)

     lowerCamelCase__					:   List[Any]      				=	model(**UpperCAmelCase			)

     # verify the logits
     lowerCamelCase__					:   Union[str, Any]      				=	(1, 1000)
     self.assertEqual(outputs.logits.shape    ,  UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]			)

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





