




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_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      bool      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path('data_bin'      )
				__lowerCamelCase		       =    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		       =    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:' ,     UpperCamelCase__      )

				__lowerCamelCase		       =    XmodForSequenceClassification(UpperCamelCase__      ) if classification_head else XmodForMaskedLM(UpperCamelCase__      )
				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(UpperCamelCase__      ).unsqueeze(0      )  # batch of size 1
				model.roberta.set_default_language(UpperCamelCase__      )

				__lowerCamelCase		       =    model(UpperCamelCase__      )[0]
				if classification_head:
								__lowerCamelCase		       =    xmod.model.classification_heads['mnli'](xmod.extract_features(UpperCamelCase__      )      )
				else:
								__lowerCamelCase		       =    xmod.model(UpperCamelCase__ ,     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(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__":
					__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'''




from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowerCamelCase							:Union[str, Any]				    =					logging.get_logger(__name__)







@add_end_docstrings(__UpperCAmelCase )
class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      **lowercase							):
				super().__init__(**lowercase							)

				if self.framework != "pt":
							raise ValueError(F'The {self.__class__} is only available in PyTorch.'							)
				# No specific FOR_XXX available yet


	def __call__(self       ,      lowercase       ,      **lowercase							):
				return super().__call__(lowercase       ,      **lowercase							)


	def     _a	(self       ,      **lowercase							):
				A_	:						Tuple	=     {}
				if "candidate_labels" in kwargs:
							A_	:						Dict	=     kwargs["""candidate_labels"""]
				if "hypothesis_template" in kwargs:
							A_	:						Optional[Any]	=     kwargs["""hypothesis_template"""]

				return preprocess_params, {}, {}


	def     _a	(self       ,      lowercase       ,      lowercase=None       ,      lowercase="This is a sound of {}."							):
				if isinstance(lowercase       ,      lowercase							):
							if audio.startswith("""http://"""							) or audio.startswith("""https://"""							):
										# We need to actually check for a real protocol, otherwise it's impossible to use a local file
										# like http_huggingface_co.png
										A_	:						Dict	=     requests.get(lowercase							).content
							else:
										with open(lowercase       ,      """rb"""							) as f:
													A_	:						List[str]	=     f.read()

				if isinstance(lowercase       ,      lowercase							):
							A_	:						List[Any]	=     ffmpeg_read(lowercase       ,      self.feature_extractor.sampling_rate							)

				if not isinstance(lowercase       ,      np.ndarray							):
							raise ValueError("""We expect a numpy ndarray as input"""							)
				if len(audio.shape							) != 1:
							raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline"""							)

				A_	:						int	=     self.feature_extractor(
				    [audio]       ,      sampling_rate=self.feature_extractor.sampling_rate       ,      return_tensors="""pt"""							)
				A_	:						List[Any]	=     candidate_labels
				A_	:						str	=     [hypothesis_template.format(lowercase							) for x in candidate_labels]
				A_	:						Optional[Any]	=     self.tokenizer(lowercase       ,      return_tensors=self.framework       ,      padding=lowercase							)
				A_	:						Optional[Any]	=     [text_inputs]
				return inputs


	def     _a	(self       ,      lowercase							):
				A_	:						Union[str, Any]	=     model_inputs.pop("""candidate_labels"""							)
				A_	:						List[Any]	=     model_inputs.pop("""text_inputs"""							)
				if isinstance(text_inputs[0]       ,      lowercase							):
							A_	:						Union[str, Any]	=     text_inputs[0]
				else:
							# Batching case.
							A_	:						Optional[int]	=     text_inputs[0][0]

				A_	:						str	=     self.model(**lowercase       ,      **lowercase							)

				A_	:						Union[str, Any]	=     {
				    """candidate_labels""": candidate_labels,
				    """logits""": outputs.logits_per_audio,
				}
				return model_outputs


	def     _a	(self       ,      lowercase							):
				A_	:						Union[str, Any]	=     model_outputs.pop("""candidate_labels"""							)
				A_	:						List[Any]	=     model_outputs["""logits"""][0]

				if self.framework == "pt":
							A_	:						Optional[Any]	=     logits.softmax(dim=0							)
							A_	:						str	=     probs.tolist()
				else:
							raise ValueError("""`tf` framework not supported."""							)

				A_	:						Optional[int]	=     [
				    {"""score""": score, """label""": candidate_label}
				    for score, candidate_label in sorted(zip(lowercase       ,      lowercase							)       ,      key=lambda lowercase							: -x[0]							)
				]
				return result