
from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


_UpperCAmelCase					: Dict      =       logging.get_logger(__name__)


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self				,      A_       )  ->					int:
				"""simple docstring"""
				if isinstance(A_				,      A_       ):
						UpperCamelCase		=     [label.strip() for label in labels.split(','       ) if label.strip()]
				return labels



		def __call__( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				if len(A_       ) == 0 or len(A_       ) == 0:
						raise ValueError('You must include at least one label and at least one sequence.'       )
				if hypothesis_template.format(labels[0]       ) == hypothesis_template:
						raise ValueError(
						    (
						        'The provided hypothesis_template "{}" was not able to be formatted with the target labels. '
						        'Make sure the passed template includes formatting syntax such as {{}} where the label should go.'
						    ).format(A_       )       )

				if isinstance(A_				,      A_       ):
						UpperCamelCase		=     [sequences]

				UpperCamelCase		=     []
				for sequence in sequences:
						sequence_pairs.extend([[sequence, hypothesis_template.format(A_       )] for label in labels]       )

				return sequence_pairs, sequences






@add_end_docstrings(_SCREAMING_SNAKE_CASE   )
class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_=ZeroShotClassificationArgumentHandler()				,      *A_				,      **A_       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     args_parser
				super().__init__(*A_				,      **A_       )
				if self.entailment_id == -1:
						logger.warning(
						    'Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '
						    '-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.'       )



		@property
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				for label, ind in self.model.config.labelaid.items():
						if label.lower().startswith('entail'       ):
								return ind
				return -1



		def __UpperCamelCase ( self				,      A_				,      A_=True				,      A_=True				,      A_=TruncationStrategy.ONLY_FIRST				,      **A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.framework
				if self.tokenizer.pad_token is None:
						# Override for tokenizers not supporting padding
						logger.error(
						    'Tokenizer was not supporting padding necessary for zero-shot, attempting to use '
						    ' `pad_token=eos_token`'       )
						UpperCamelCase		=     self.tokenizer.eos_token
				try:
						UpperCamelCase		=     self.tokenizer(
						    A_				,      add_special_tokens=A_				,      return_tensors=A_				,      padding=A_				,      truncation=A_				,      )
				except Exception as e:
						if "too short" in str(A_       ):
								# tokenizers might yell that we want to truncate
								# to a value that is not even reached by the input.
								# In that case we don't want to truncate.
								# It seems there's not a really better way to catch that
								# exception.

								UpperCamelCase		=     self.tokenizer(
								    A_				,      add_special_tokens=A_				,      return_tensors=A_				,      padding=A_				,      truncation=TruncationStrategy.DO_NOT_TRUNCATE				,      )
						else:
								raise e

				return inputs



		def __UpperCamelCase ( self				,      **A_       )  ->					Tuple:
				"""simple docstring"""
				if kwargs.get('multi_class'				,      A_       ) is not None:
						UpperCamelCase		=     kwargs['multi_class']
						logger.warning(
						    'The `multi_class` argument has been deprecated and renamed to `multi_label`. '
						    '`multi_class` will be removed in a future version of Transformers.'       )
				UpperCamelCase		=     {}
				if "candidate_labels" in kwargs:
						UpperCamelCase		=     self._args_parser._parse_labels(kwargs['candidate_labels']       )
				if "hypothesis_template" in kwargs:
						UpperCamelCase		=     kwargs['hypothesis_template']

				UpperCamelCase		=     {}
				if "multi_label" in kwargs:
						UpperCamelCase		=     kwargs['multi_label']
				return preprocess_params, {}, postprocess_params



		def __call__( self				,      A_				,      *A_				,      **A_				,      )  ->					Tuple:
				"""simple docstring"""
				if len(A_       ) == 0:
						pass
				elif len(A_       ) == 1 and "candidate_labels" not in kwargs:
						UpperCamelCase		=     args[0]
				else:
						raise ValueError(F'''Unable to understand extra arguments {args}'''       )

				return super().__call__(A_				,      **A_       )



		def __UpperCamelCase ( self				,      A_				,      A_=None				,      A_="This example is {}."       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     self._args_parser(A_				,      A_				,      A_       )

				for i, (candidate_label, sequence_pair) in enumerate(zip(A_				,      A_       )       ):
						UpperCamelCase		=     self._parse_and_tokenize([sequence_pair]       )

						yield {
						    "candidate_label": candidate_label,
						    "sequence": sequences[0],
						    "is_last": i == len(A_       ) - 1,
						    **model_input,
						}



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     inputs['candidate_label']
				UpperCamelCase		=     inputs['sequence']
				UpperCamelCase		=     {k: inputs[k] for k in self.tokenizer.model_input_names}
				UpperCamelCase		=     self.model(**A_       )

				UpperCamelCase		=     {
				    'candidate_label': candidate_label,
				    'sequence': sequence,
				    'is_last': inputs['is_last'],
				    **outputs,
				}
				return model_outputs



		def __UpperCamelCase ( self				,      A_				,      A_=False       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     [outputs['candidate_label'] for outputs in model_outputs]
				UpperCamelCase		=     [outputs['sequence'] for outputs in model_outputs]
				UpperCamelCase		=     np.concatenate([output['logits'].numpy() for output in model_outputs]       )
				UpperCamelCase		=     logits.shape[0]
				UpperCamelCase		=     len(A_       )
				UpperCamelCase		=     N // n
				UpperCamelCase		=     logits.reshape((num_sequences, n, -1)       )

				if multi_label or len(A_       ) == 1:
						# softmax over the entailment vs. contradiction dim for each label independently
						UpperCamelCase		=     self.entailment_id
						UpperCamelCase		=     -1 if entailment_id == 0 else 0
						UpperCamelCase		=     reshaped_outputs[..., [contradiction_id, entailment_id]]
						UpperCamelCase		=     np.exp(A_       ) / np.exp(A_       ).sum(-1				,      keepdims=A_       )
						UpperCamelCase		=     scores[..., 1]
				else:
						# softmax the "entailment" logits over all candidate labels
						UpperCamelCase		=     reshaped_outputs[..., self.entailment_id]
						UpperCamelCase		=     np.exp(A_       ) / np.exp(A_       ).sum(-1				,      keepdims=A_       )

				UpperCamelCase		=     list(reversed(scores[0].argsort()       )       )
				return {
				    "sequence": sequences[0],
				    "labels": [candidate_labels[i] for i in top_inds],
				    "scores": scores[0, top_inds].tolist(),
				}


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def       A       (					lowercase )   ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase		=     [
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    'decoder.output_projection.weight',
		    '_float_tensor',
		    'encoder.embed_positions._float_tensor',
		    'decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowercase    ,       lowercase )


def       A       (					lowercase )   ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase		=     list(s_dict.keys() )
		for key in keys:
				if "transformer_layers" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )
				elif "subsample" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )


def       A       (					lowercase )   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase	,  UpperCamelCase		=     emb.weight.shape
		UpperCamelCase		=     nn.Linear(lowercase    ,       lowercase    ,       bias=lowercase )
		UpperCamelCase		=     emb.weight.data
		return lin_layer


def       A       (					lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     torch.load(lowercase    ,       map_location='cpu' )
		UpperCamelCase		=     mam_aaa['args']
		UpperCamelCase		=     mam_aaa['model']
		UpperCamelCase		=     state_dict['decoder.output_projection.weight']

		remove_ignore_keys_(lowercase )
		rename_keys(lowercase )

		UpperCamelCase		=     state_dict['decoder.embed_tokens.weight'].shape[0]

		UpperCamelCase		=     args.share_decoder_input_output_embed

		UpperCamelCase		=     [int(lowercase ) for i in args.conv_kernel_sizes.split(',' )]
		UpperCamelCase		=     SpeechaTextConfig(
		    vocab_size=lowercase    ,       max_source_positions=args.max_source_positions    ,       max_target_positions=args.max_target_positions    ,       encoder_layers=args.encoder_layers    ,       decoder_layers=args.decoder_layers    ,       encoder_attention_heads=args.encoder_attention_heads    ,       decoder_attention_heads=args.decoder_attention_heads    ,       encoder_ffn_dim=args.encoder_ffn_embed_dim    ,       decoder_ffn_dim=args.decoder_ffn_embed_dim    ,       d_model=args.encoder_embed_dim    ,       dropout=args.dropout    ,       attention_dropout=args.attention_dropout    ,       activation_dropout=args.activation_dropout    ,       activation_function='relu'    ,       num_conv_layers=len(lowercase )    ,       conv_channels=args.conv_channels    ,       conv_kernel_sizes=lowercase    ,       input_feat_per_channel=args.input_feat_per_channel    ,       input_channels=args.input_channels    ,       tie_word_embeddings=lowercase    ,       num_beams=5    ,       max_length=200    ,       use_cache=lowercase    ,       decoder_start_token_id=2    ,       early_stopping=lowercase    ,       )

		UpperCamelCase		=     SpeechaTextForConditionalGeneration(lowercase )
		UpperCamelCase	,  UpperCamelCase		=     model.model.load_state_dict(lowercase    ,       strict=lowercase )
		if len(lowercase ) > 0 and not set(lowercase ) <= {
		    "encoder.embed_positions.weights",
		    "decoder.embed_positions.weights",
		}:
				raise ValueError(
				    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
				    f''' but all the following weights are missing {missing}''' )

		if tie_embeds:
				UpperCamelCase		=     make_linear_from_emb(model.model.decoder.embed_tokens )
		else:
				UpperCamelCase		=     lm_head_weights

		model.save_pretrained(lowercase )


if __name__ == "__main__":
					_UpperCAmelCase					: Optional[Any]      =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					_UpperCAmelCase					: Dict      =       parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
