






'''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							:							Tuple  							=		logging.get_logger(__name__)

__A							:							Optional[Any]  							=		"""Hello, World!"""
__A							:							Any  							=		"""en_XX"""






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
	lowerCamelCase__  =     Path("data_bin")
	lowerCamelCase__  =     FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(lowercase__).parent)				,     checkpoint_file=Path(lowercase__).name				,     _name="xmod_base"				,     arch="xmod_base"				,     task="multilingual_masked_lm"				,     data_name_or_path=str(lowercase__)				,     bpe="sentencepiece"				,     sentencepiece_model=str(Path(lowercase__).parent / "sentencepiece.bpe.model")				,     src_dict=str(data_dir / "dict.txt")				,     )
	xmod.eval()  # disable dropout
	print(lowercase__)

	lowerCamelCase__  =     xmod.model.encoder.sentence_encoder
	lowerCamelCase__  =     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__  =     xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our X-MOD config:"				,     lowercase__)

	lowerCamelCase__  =     XmodForSequenceClassification(lowercase__) if classification_head else XmodForMaskedLM(lowercase__)
	model.eval()

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

	lowerCamelCase__  =     xmod_sent_encoder.layernorm_embedding.weight
	lowerCamelCase__  =     xmod_sent_encoder.layernorm_embedding.bias

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

		# self attention
		lowerCamelCase__  =     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__  =     xmod_layer.self_attn.q_proj.weight
		lowerCamelCase__  =     xmod_layer.self_attn.q_proj.bias
		lowerCamelCase__  =     xmod_layer.self_attn.k_proj.weight
		lowerCamelCase__  =     xmod_layer.self_attn.k_proj.bias
		lowerCamelCase__  =     xmod_layer.self_attn.v_proj.weight
		lowerCamelCase__  =     xmod_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCamelCase__  =     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__  =     xmod_layer.self_attn.out_proj.weight
		lowerCamelCase__  =     xmod_layer.self_attn.out_proj.bias
		lowerCamelCase__  =     xmod_layer.self_attn_layer_norm.weight
		lowerCamelCase__  =     xmod_layer.self_attn_layer_norm.bias

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

		# output
		lowerCamelCase__  =     layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError("Dimensions of feed-forward weights do not match.")
		lowerCamelCase__  =     xmod_layer.fca.weight
		lowerCamelCase__  =     xmod_layer.fca.bias
		lowerCamelCase__  =     xmod_layer.final_layer_norm.weight
		lowerCamelCase__  =     xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			lowerCamelCase__  =     xmod_layer.adapter_layer_norm.weight
			lowerCamelCase__  =     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__  =     bert_output.adapter_modules[lang_code]
			lowerCamelCase__  =     xmod_layer.adapter_modules[lang_code]
			lowerCamelCase__  =     from_adapter.fca.weight
			lowerCamelCase__  =     from_adapter.fca.bias
			lowerCamelCase__  =     from_adapter.fca.weight
			lowerCamelCase__  =     from_adapter.fca.bias

		# end of layer

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

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

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

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

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


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









'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							BlenderbotSmallConfig
    lowerCAmelCase__      				=							{}
    lowerCAmelCase__      				=							"gelu"

    def __init__(					self     :     List[Any]				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     str=13				,     __lowerCamelCase     :     List[str]=7				,     __lowerCamelCase     :     Dict=True				,     __lowerCamelCase     :     Any=False				,     __lowerCamelCase     :     Union[str, Any]=99				,     __lowerCamelCase     :     Optional[int]=32				,     __lowerCamelCase     :     Optional[int]=2				,     __lowerCamelCase     :     List[str]=4				,     __lowerCamelCase     :     Union[str, Any]=37				,     __lowerCamelCase     :     Tuple=0.1				,     __lowerCamelCase     :     List[str]=0.1				,     __lowerCamelCase     :     int=20				,     __lowerCamelCase     :     Optional[Any]=2				,     __lowerCamelCase     :     List[str]=1				,     __lowerCamelCase     :     Union[str, Any]=0				,     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     parent
     lowerCamelCase__  =     batch_size
     lowerCamelCase__  =     seq_length
     lowerCamelCase__  =     is_training
     lowerCamelCase__  =     use_labels
     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_hidden_layers
     lowerCamelCase__  =     num_attention_heads
     lowerCamelCase__  =     intermediate_size

     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     max_position_embeddings
     lowerCamelCase__  =     eos_token_id
     lowerCamelCase__  =     pad_token_id
     lowerCamelCase__  =     bos_token_id

    def    a__				(					self     :     str     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     ids_tensor([self.batch_size, self.seq_length - 1]				,     self.vocab_size     )
     lowerCamelCase__  =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )				,     1     )
     lowerCamelCase__  =     tf.concat([input_ids, eos_tensor]				,     axis=1     )

     lowerCamelCase__  =     ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size     )

     lowerCamelCase__  =     self.config_cls(
         vocab_size=self.vocab_size				,     d_model=self.hidden_size				,     encoder_layers=self.num_hidden_layers				,     decoder_layers=self.num_hidden_layers				,     encoder_attention_heads=self.num_attention_heads				,     decoder_attention_heads=self.num_attention_heads				,     encoder_ffn_dim=self.intermediate_size				,     decoder_ffn_dim=self.intermediate_size				,     dropout=self.hidden_dropout_prob				,     attention_dropout=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     eos_token_ids=[2]				,     bos_token_id=self.bos_token_id				,     pad_token_id=self.pad_token_id				,     decoder_start_token_id=self.pad_token_id				,     **self.config_updates				,     )
     lowerCamelCase__  =     prepare_blenderbot_small_inputs_dict(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )
     return config, inputs_dict




    def    a__				(					self     :     int				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Optional[Any]     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     TFBlenderbotSmallModel(config=__lowerCamelCase     ).get_decoder()
     lowerCamelCase__  =     inputs_dict["input_ids"]

     lowerCamelCase__  =     input_ids[:1, :]
     lowerCamelCase__  =     inputs_dict["attention_mask"][:1, :]
     lowerCamelCase__  =     inputs_dict["head_mask"]
     lowerCamelCase__  =     1

     # first forward pass
     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase				,     head_mask=__lowerCamelCase				,     use_cache=__lowerCamelCase     )

     lowerCamelCase__  ,	lowerCamelCase__  =     outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     lowerCamelCase__  =     ids_tensor((self.batch_size, 3)				,     config.vocab_size     )
     lowerCamelCase__  =     tf.cast(ids_tensor((self.batch_size, 3)				,     2     )				,     tf.inta     )

     # append to next input_ids and
     lowerCamelCase__  =     tf.concat([input_ids, next_tokens]				,     axis=-1     )
     lowerCamelCase__  =     tf.concat([attention_mask, next_attn_mask]				,     axis=-1     )

     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase     )[0]
     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase				,     past_key_values=__lowerCamelCase     )[0]

     self.parent.assertEqual(next_tokens.shape[1]				,     output_from_past.shape[1]     )

     # select random slice
     lowerCamelCase__  =     int(ids_tensor((1,)				,     output_from_past.shape[-1]     )     )
     lowerCamelCase__  =     output_from_no_past[:, -3:, random_slice_idx]
     lowerCamelCase__  =     output_from_past[:, :, random_slice_idx]

     # test that outputs are equal for slice
     tf.debugging.assert_near(__lowerCamelCase				,     __lowerCamelCase				,     rtol=1E-3     )






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     ):
 if attention_mask is None:
  lowerCamelCase__  =     tf.cast(tf.math.not_equal(lowercase__				,     config.pad_token_id)				,     tf.inta)
 if decoder_attention_mask is None:
  lowerCamelCase__  =     tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape				,     dtype=tf.inta),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,     config.pad_token_id)				,     tf.inta),
      ]				,     axis=-1				,     )
 if head_mask is None:
  lowerCamelCase__  =     tf.ones((config.encoder_layers, config.encoder_attention_heads))
 if decoder_head_mask is None:
  lowerCamelCase__  =     tf.ones((config.decoder_layers, config.decoder_attention_heads))
 if cross_attn_head_mask is None:
  lowerCamelCase__  =     tf.ones((config.decoder_layers, config.decoder_attention_heads))
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }
@require_tf
class 				lowercase       (							_lowerCamelCase ,       _lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							(
        (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
    )
    lowerCAmelCase__      				=							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
    lowerCAmelCase__      				=							(
        {
            "conversational": TFBlenderbotSmallForConditionalGeneration,
            "feature-extraction": TFBlenderbotSmallModel,
            "summarization": TFBlenderbotSmallForConditionalGeneration,
            "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
            "translation": TFBlenderbotSmallForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowerCAmelCase__      				=							True
    lowerCAmelCase__      				=							False
    lowerCAmelCase__      				=							False

    def    a__				(					self     :     List[Any]     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     TFBlenderbotSmallModelTester(self     )
     lowerCamelCase__  =     ConfigTester(self				,     config_class=__lowerCamelCase     )

    def    a__				(					self     :     Any     )							->							Tuple:

     '''simple docstring'''




     self.config_tester.run_common_tests()




    def    a__				(					self     :     List[Any]     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase     )
@require_tokenizers
@require_tf
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							[
        "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like "
        "  i'm going to throw up.\nand why is that?"
    ]
    lowerCAmelCase__      				=							"facebook/blenderbot_small-90M"

    @cached_property
    def    a__				(					self     :     Optional[Any]     )							->							Tuple:

     '''simple docstring'''




     return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )

    @cached_property
    def    a__				(					self     :     int     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
     return model




    @slow
    def    a__				(					self     :     Any     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.tokenizer(self.src_text				,     return_tensors="tf"     )
     lowerCamelCase__  =     self.model.generate(
         model_inputs.input_ids				,     attention_mask=model_inputs.attention_mask				,     num_beams=2				,     use_cache=__lowerCamelCase				,     )
     lowerCamelCase__  =     self.tokenizer.batch_decode(generated_ids.numpy()				,     skip_special_tokens=__lowerCamelCase     )[0]
     assert generated_words in (
         "i don't know. i just feel like i'm going to throw up. it's not fun.",
         "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
         "i'm not sure. i just feel like i've been in a bad situation.",
     )

