
"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_a														=		{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case     )							->		int:


					"""simple docstring"""





					for attribute in key.split('''.'''     ):
										_UpperCamelCase									=  getattr(__snake_case,					__snake_case     )

					if weight_type is not None:
										_UpperCamelCase									=  getattr(__snake_case,					__snake_case     ).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__		(      __snake_case,					__snake_case,					__snake_case     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase									=  []
					_UpperCamelCase									=  fairseq_model.state_dict()

					_UpperCamelCase									=  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										_UpperCamelCase									=  False
										if "conv_layers" in name:
															load_conv_layer(
															    __snake_case,					__snake_case,					__snake_case,					__snake_case,					hf_model.config.feat_extract_norm == '''group''',					)
															_UpperCamelCase									=  True
										else:
															for key, mapped_key in MAPPING.items():
																				_UpperCamelCase									=  '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																				if key in name or key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0]:
																									_UpperCamelCase									=  True
																									if "*" in mapped_key:
																														_UpperCamelCase									=  name.split(__snake_case     )[0].split('''.'''     )[-2]
																														_UpperCamelCase									=  mapped_key.replace('''*''',					__snake_case     )
																									if "weight_g" in name:
																														_UpperCamelCase									=  '''weight_g'''
																									elif "weight_v" in name:
																														_UpperCamelCase									=  '''weight_v'''
																									elif "weight" in name:
																														_UpperCamelCase									=  '''weight'''
																									elif "bias" in name:
																														_UpperCamelCase									=  '''bias'''
																									else:
																														_UpperCamelCase									=  None
																									set_recursively(__snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case     )
																				continue
										if not is_used:
															unused_weights.append(__snake_case     )

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



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case     )							->		List[Any]:


					"""simple docstring"""





					_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(__snake_case     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Tuple:


					"""simple docstring"""





					_UpperCamelCase									=  SEWConfig()
					if is_finetuned:
										_UpperCamelCase									=  model.wav_encoder.wav_model.cfg
					else:
										_UpperCamelCase									=  model.cfg

					_UpperCamelCase									=  fs_config.conv_bias
					_UpperCamelCase									=  eval(fs_config.conv_feature_layers     )
					_UpperCamelCase									=  [x[0] for x in conv_layers]
					_UpperCamelCase									=  [x[1] for x in conv_layers]
					_UpperCamelCase									=  [x[2] for x in conv_layers]
					_UpperCamelCase									=  '''gelu'''
					_UpperCamelCase									=  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
					_UpperCamelCase									=  0.0
					_UpperCamelCase									=  fs_config.activation_fn.name
					_UpperCamelCase									=  fs_config.encoder_embed_dim
					_UpperCamelCase									=  0.02
					_UpperCamelCase									=  fs_config.encoder_ffn_embed_dim
					_UpperCamelCase									=  1e-5
					_UpperCamelCase									=  fs_config.encoder_layerdrop
					_UpperCamelCase									=  fs_config.encoder_attention_heads
					_UpperCamelCase									=  fs_config.conv_pos_groups
					_UpperCamelCase									=  fs_config.conv_pos
					_UpperCamelCase									=  len(__snake_case     )
					_UpperCamelCase									=  fs_config.encoder_layers
					_UpperCamelCase									=  fs_config.squeeze_factor

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										_UpperCamelCase									=  model.cfg
										_UpperCamelCase									=  fs_config.final_dropout
										_UpperCamelCase									=  fs_config.layerdrop
					_UpperCamelCase									=  fs_config.activation_dropout
					_UpperCamelCase									=  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					_UpperCamelCase									=  fs_config.attention_dropout
					_UpperCamelCase									=  fs_config.dropout_input
					_UpperCamelCase									=  fs_config.dropout
					_UpperCamelCase									=  fs_config.mask_channel_length
					_UpperCamelCase									=  fs_config.mask_channel_prob
					_UpperCamelCase									=  fs_config.mask_length
					_UpperCamelCase									=  fs_config.mask_prob

					_UpperCamelCase									=  '''Wav2Vec2FeatureExtractor'''
					_UpperCamelCase									=  '''Wav2Vec2CTCTokenizer'''

					return config



@torch.no_grad()
def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case=None,					__snake_case=None,					__snake_case=True     )							->		Optional[Any]:


					"""simple docstring"""





					if is_finetuned:
										_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase									=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path],					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
					else:
										_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase									=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

					if config_path is not None:
										_UpperCamelCase									=  SEWConfig.from_pretrained(__snake_case     )
					else:
										_UpperCamelCase									=  convert_config(model[0],					__snake_case     )
					_UpperCamelCase									=  model[0].eval()

					_UpperCamelCase									=  True if config.feat_extract_norm == '''layer''' else False
					_UpperCamelCase									=  WavaVecaFeatureExtractor(
					    feature_size=1,					sampling_rate=1_60_00,					padding_value=0,					do_normalize=__snake_case,					return_attention_mask=__snake_case,					)

					if is_finetuned:
										if dict_path:
															_UpperCamelCase									=  Dictionary.load(__snake_case     )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_UpperCamelCase									=  target_dict.pad_index
															_UpperCamelCase									=  target_dict.bos_index
															_UpperCamelCase									=  target_dict.pad_index
															_UpperCamelCase									=  target_dict.bos_index
															_UpperCamelCase									=  target_dict.eos_index
															_UpperCamelCase									=  len(target_dict.symbols     )
															_UpperCamelCase									=  os.path.join(__snake_case,					'''vocab.json'''     )
															if not os.path.isdir(__snake_case     ):
																				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case     )     )
																				return
															os.makedirs(__snake_case,					exist_ok=__snake_case     )
															with open(__snake_case,					'''w''',					encoding='''utf-8'''     ) as vocab_handle:
																				json.dump(target_dict.indices,					__snake_case     )
															_UpperCamelCase									=  WavaVecaCTCTokenizer(
															    __snake_case,					unk_token=target_dict.unk_word,					pad_token=target_dict.pad_word,					bos_token=target_dict.bos_word,					eos_token=target_dict.eos_word,					word_delimiter_token='''|''',					do_lower_case=__snake_case,					)
															_UpperCamelCase									=  WavaVecaProcessor(feature_extractor=__snake_case,					tokenizer=__snake_case     )
															processor.save_pretrained(__snake_case     )

										_UpperCamelCase									=  SEWForCTC(__snake_case     )
					else:
										_UpperCamelCase									=  SEWModel(__snake_case     )
										feature_extractor.save_pretrained(__snake_case     )

					recursively_load_weights(__snake_case,					__snake_case,					__snake_case     )

					hf_model.save_pretrained(__snake_case     )


if __name__ == "__main__":
				_a														=		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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
				parser.add_argument(
				    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
				)
				_a														=		parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)








"""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 typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'dandelin/vilt-b32-finetuned-vqa'
		lowercase__      =				(
		    'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
		    'image containing the information, as well as a `question` which should be the question in English. It '
		    'returns a text that is the answer to the question.'
		)
		lowercase__      =				'image_qa'
		lowercase__      =				AutoProcessor
		lowercase__      =				AutoModelForVisualQuestionAnswering

		lowercase__      =				['image', 'text']
		lowercase__      =				['text']



		def __init__( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(__a    ,		__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												return self.model(**__a).logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.argmax(-1).item()
							return self.model.config.idalabel[idx]






