



"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

snake_case_    					=	{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
snake_case_    					=	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						):
						for attribute in key.split('.'						):
												UpperCAmelCase					=			getattr(lowercase_		,     lowercase_						)

						if weight_type is not None:
												UpperCAmelCase					=			getattr(lowercase_		,     lowercase_						).shape
						else:
												UpperCAmelCase					=			hf_pointer.shape

						assert hf_shape == value.shape, (
						    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
						    F""" {value.shape} for {full_name}"""
						)

						if weight_type == "weight":
												UpperCAmelCase					=			value
						elif weight_type == "weight_g":
												UpperCAmelCase					=			value
						elif weight_type == "weight_v":
												UpperCAmelCase					=			value
						elif weight_type == "bias":
												UpperCAmelCase					=			value
						else:
												UpperCAmelCase					=			value

						logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""						)

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						UpperCAmelCase					=			[]
						UpperCAmelCase					=			fairseq_model.state_dict()

						UpperCAmelCase					=			hf_model.feature_extractor

						# if encoder has different dim to decoder -> use proj_weight
						UpperCAmelCase					=			None

						for name, value in fairseq_dict.items():
												UpperCAmelCase					=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     hf_model.config.feat_extract_norm == 'group'		,     )
																		UpperCAmelCase					=			True
												elif name.split('.'						)[0] == "proj":
																		UpperCAmelCase					=			fairseq_model.proj
																		UpperCAmelCase					=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
																														UpperCAmelCase					=			True
																														if "*" in mapped_key:
																																				UpperCAmelCase					=			name.split(lowercase_						)[0].split('.'						)[-2]
																																				UpperCAmelCase					=			mapped_key.replace('*'		,     lowercase_						)
																														if "weight_g" in name:
																																				UpperCAmelCase					=			'weight_g'
																														elif "weight_v" in name:
																																				UpperCAmelCase					=			'weight_v'
																														elif "bias" in name:
																																				UpperCAmelCase					=			'bias'
																														elif "weight" in name:
																																				UpperCAmelCase					=			'weight'
																														else:
																																				UpperCAmelCase					=			None
																														set_recursively(lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						)
																								continue
												if not is_used:
																		unused_weights.append(lowercase_						)

						logger.warning(F"""Unused weights: {unused_weights}"""						)

						return proj_weight

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			full_name.split('conv_layers.'						)[-1]
						UpperCAmelCase					=			name.split('.'						)
						UpperCAmelCase					=			int(items[0]						)
						UpperCAmelCase					=			int(items[1]						)

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		UpperCAmelCase					=			value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		UpperCAmelCase					=			value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		UpperCAmelCase					=			value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		UpperCAmelCase					=			value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
						else:
												unused_weights.append(lowercase_						)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase				,   UpperCAmelCase					=			emb.weight.shape
						UpperCAmelCase					=			nn.Linear(lowercase_		,     lowercase_		,     bias=lowercase_						)
						UpperCAmelCase					=			emb.weight.data
						return lin_layer

def 			_lowerCAmelCase				(       lowercase_						):
						with open(lowercase_		,     'r'		,     encoding='utf-8'						) as f:
												UpperCAmelCase					=			f.readlines()
												UpperCAmelCase					=			[line.split(' '						)[0] for line in lines]

						UpperCAmelCase					=			len(lowercase_						)

						UpperCAmelCase					=			{
						    '<s>': 0,
						    '<pad>': 1,
						    '</s>': 2,
						    '<unk>': 3,
						}

						vocab_dict.update(dict(zip(lowercase_		,     range(4		,     num_words + 4						)						)						)						)
						return vocab_dict

@torch.no_grad()
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     ):
						UpperCAmelCase					=			WavaVecaConfig.from_pretrained(lowercase_						)
						UpperCAmelCase					=			SpeechaTextaConfig.from_pretrained(
						    lowercase_		,     vocab_size=lowercase_		,     decoder_layers=lowercase_		,     do_stable_layer_norm=lowercase_						)

						UpperCAmelCase					=			WavaVecaFeatureExtractor(
						    feature_size=1		,     sampling_rate=16000		,     padding_value=0		,     do_normalize=lowercase_		,     return_attention_mask=lowercase_		,     )

						UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]		,     arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						)}						)
						UpperCAmelCase					=			model[0].eval()

						# set weights for wav2vec2 encoder
						UpperCAmelCase					=			WavaVecaModel(lowercase_						)
						UpperCAmelCase					=			recursively_load_weights_wavaveca(model.encoder		,     lowercase_						)

						UpperCAmelCase					=			SpeechaTextaForCausalLM(lowercase_						)
						UpperCAmelCase				,   UpperCAmelCase					=			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,     strict=lowercase_						)

						# set output linear layer
						unexpected_keys.remove('embed_out'						)
						UpperCAmelCase					=			nn.Parameter(model.decoder.embed_out.detach()						)

						# layer norm is init to identity matrix so leaving it is fine
						logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""						)
						logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""						)

						UpperCAmelCase					=			SpeechEncoderDecoderModel(encoder=lowercase_		,     decoder=lowercase_						)
						UpperCAmelCase					=			False

						# add projection layer
						UpperCAmelCase					=			nn.Parameter(projection_layer.weight						)
						UpperCAmelCase					=			nn.Parameter(projection_layer.bias						)

						UpperCAmelCase					=			create_vocab_dict(lowercase_						)

						with open(os.path.join(lowercase_		,     'vocab.json'						)		,     'w'						) as fp:
												json.dump(lowercase_		,     lowercase_						)

						UpperCAmelCase					=			SpeechaTextaTokenizer(os.path.join(lowercase_		,     'vocab.json'						)						)
						tokenizer.save_pretrained(lowercase_						)

						UpperCAmelCase					=			hf_wavavec.config.to_dict()
						UpperCAmelCase					=			tokenizer.pad_token_id
						UpperCAmelCase					=			tokenizer.bos_token_id
						UpperCAmelCase					=			tokenizer.eos_token_id
						UpperCAmelCase					=			'speech_to_text_2'
						UpperCAmelCase					=			'wav2vec2'

						UpperCAmelCase					=			SpeechEncoderDecoderConfig.from_dict(lowercase_						)

						hf_wavavec.save_pretrained(lowercase_						)
						feature_extractor.save_pretrained(lowercase_						)


if __name__ == "__main__":
				snake_case_    					=	argparse.ArgumentParser()
				parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
				parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
				parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
				parser.add_argument(
				    """--encoder_config_path""",
				    default="""facebook/wav2vec2-large-lv60""",
				    type=str,
				    help="""Path to hf encoder wav2vec2 checkpoint config""",
				)
				parser.add_argument(
				    """--decoder_config_path""",
				    default="""facebook/s2t-small-mustc-en-fr-st""",
				    type=str,
				    help="""Path to hf decoder s2t checkpoint config""",
				)
				parser.add_argument("""--vocab_size""", default=1_0224, type=int, help="""Vocab size of decoder""")
				parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

				snake_case_    					=	parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    vocab_size=args.vocab_size,
				    num_decoder_layers=args.num_decoder_layers,
				)










"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""philschmid/bart-large-cnn-samsum"""
						__UpperCamelCase		    =							(
						    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
						    """and returns a summary of the text."""
						)
						__UpperCamelCase		    =							"""summarizer"""
						__UpperCamelCase		    =							AutoTokenizer
						__UpperCamelCase		    =							AutoModelForSeqaSeqLM

						__UpperCamelCase		    =							["""text"""]
						__UpperCamelCase		    =							["""text"""]






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Tuple )    ->      int:
												return self.pre_processor(lowercase_       ,  return_tensors='pt'       ,  truncation=lowercase_ )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Dict )    ->      Optional[int]:
												return self.model.generate(**lowercase_ )[0]






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Tuple )    ->      List[str]:
												return self.pre_processor.decode(lowercase_       ,  skip_special_tokens=lowercase_       ,  clean_up_tokenization_spaces=lowercase_ )





