



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

UpperCAmelCase__       =	{
    "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",
}
UpperCAmelCase__       =	[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 							A     (					_UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       str   )    ->			str:







			'''simple docstring'''
			for attribute in key.split('.'   ):
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   )

			if weight_type is not None:
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   ).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 							A     (					_UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Optional[Any]   )    ->			Any:







			'''simple docstring'''
			_UpperCAmelCase			=       []
			_UpperCAmelCase			=       fairseq_model.state_dict()

			_UpperCAmelCase			=       hf_model.feature_extractor
			_UpperCAmelCase			=       hf_model.adapter

			for name, value in fairseq_dict.items():
						_UpperCAmelCase			=       False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       hf_model.config.feat_extract_norm == 'group'							,       )
									_UpperCAmelCase			=       True
						elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']   ):
									load_adapter(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
									_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(_UpperCAmelCase   )[0].split('.'   )[-2]
																		_UpperCAmelCase			=       mapped_key.replace('*'							,       _UpperCAmelCase   )
															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(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
												continue
						if not is_used:
									unused_weights.append(_UpperCAmelCase   )

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





def 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Optional[Any]   )    ->			Union[str, 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(_UpperCAmelCase   )





def 							A     (					_UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[Any]   )    ->			List[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       full_name.split('adaptor.'   )[-1]
			_UpperCAmelCase			=       name.split('.'   )

			if items[1].isdigit():
						_UpperCAmelCase			=       int(items[1]   )
			else:
						_UpperCAmelCase			=       None

			if "adaptor" not in full_name:
						if "proj_ln" in full_name:
									# has to be layer norm
									if "bias" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.bias.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
												_UpperCAmelCase			=       value
												logger.info(F"Adapter proj layer norm bias was initialized from {full_name}."   )
									if "weight" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.weight.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
												_UpperCAmelCase			=       value
						else:
									# has to be projection layer
									if "bias" in name:
												assert (
												    value.shape == adapter.proj.bias.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
												_UpperCAmelCase			=       value
												logger.info(F"Adapter proj layer bias was initialized from {full_name}."   )
									if "weight" in name:
												assert (
												    value.shape == adapter.proj.weight.data.shape
												), F"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
												_UpperCAmelCase			=       value
												logger.info(F"Adapter proj layer weight was initialized from {full_name}."   )
			elif isinstance(_UpperCAmelCase							,       _UpperCAmelCase   ):
						if "bias" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.bias.data.shape
									), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
									_UpperCAmelCase			=       value
									logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."   )
						elif "weight" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.weight.data.shape
									), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
									_UpperCAmelCase			=       value
									logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."   )
			else:
						unused_weights.append(_UpperCAmelCase   )





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	,					_UpperCAmelCase			=       emb.weight.shape
			_UpperCAmelCase			=       nn.Linear(_UpperCAmelCase							,       _UpperCAmelCase							,       bias=_UpperCAmelCase   )
			_UpperCAmelCase			=       emb.weight.data
			return lin_layer





@torch.no_grad()
def 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Dict							,       )    ->			Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase			=       WavaVecaConfig.from_pretrained(
			    _UpperCAmelCase							,       add_adapter=_UpperCAmelCase							,       adapter_stride=_UpperCAmelCase							,       adapter_kernel_size=_UpperCAmelCase							,       use_auth_token=_UpperCAmelCase							,       output_hidden_size=_UpperCAmelCase							,       )
			_UpperCAmelCase			=       MBartConfig.from_pretrained(_UpperCAmelCase   )

			# load model
			_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]							,       arg_overrides={
			        'config_yaml': config_yaml_path,
			        'data': '/'.join(dict_path.split('/'   )[:-1]   ),
			        'w2v_path': checkpoint_path,
			        'load_pretrained_decoder_from': None,
			    }							,       )
			_UpperCAmelCase			=       model[0].eval()

			# load feature extractor
			_UpperCAmelCase			=       WavaVecaFeatureExtractor.from_pretrained(_UpperCAmelCase							,       use_auth_token=_UpperCAmelCase   )

			# set weights for wav2vec2 encoder
			_UpperCAmelCase			=       WavaVecaModel(_UpperCAmelCase   )

			recursively_load_weights_wavaveca(model.encoder							,       _UpperCAmelCase   )

			# load decoder weights
			_UpperCAmelCase			=       MBartForCausalLM(_UpperCAmelCase   )
			_UpperCAmelCase	,					_UpperCAmelCase			=       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,       strict=_UpperCAmelCase   )
			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=_UpperCAmelCase							,       decoder=_UpperCAmelCase   )
			_UpperCAmelCase			=       False

			_UpperCAmelCase			=       MBartaaTokenizer(_UpperCAmelCase   )
			tokenizer.save_pretrained(_UpperCAmelCase   )

			_UpperCAmelCase			=       hf_wavavec.config.to_dict()
			_UpperCAmelCase			=       tokenizer.pad_token_id
			_UpperCAmelCase			=       tokenizer.bos_token_id
			_UpperCAmelCase			=       tokenizer.eos_token_id
			_UpperCAmelCase			=       'mbart50'
			_UpperCAmelCase			=       'wav2vec2'

			_UpperCAmelCase			=       tokenizer.eos_token_id
			_UpperCAmelCase			=       250_004
			_UpperCAmelCase			=       tokenizer.eos_token_id

			_UpperCAmelCase			=       SpeechEncoderDecoderConfig.from_dict(_UpperCAmelCase   )

			hf_wavavec.save_pretrained(_UpperCAmelCase   )
			feature_extractor.save_pretrained(_UpperCAmelCase   )


if __name__ == "__main__":
							UpperCAmelCase__       =	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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
							parser.add_argument(
							    "--encoder_config_path",
							    default="facebook/wav2vec2-xls-r-1b",
							    type=str,
							    help="Path to hf encoder wav2vec2 checkpoint config",
							)
							parser.add_argument(
							    "--decoder_config_path",
							    default="facebook/mbart-large-50-one-to-many-mmt",
							    type=str,
							    help="Path to hf decoder checkpoint config",
							)
							parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
							parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
							parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
							parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
							parser.add_argument("--start_token_id", default=25_0004, type=int, help="`decoder_start_token_id` of model config")

							UpperCAmelCase__       =	parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    args.config_yaml_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    add_adapter=args.add_adapter,
							    adapter_kernel_size=args.adapter_kernel_size,
							    adapter_stride=args.adapter_stride,
							    decoder_start_token_id=args.start_token_id,
							    encoder_output_dim=args.encoder_output_dim,
							)





import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__       =	"src/transformers"
UpperCAmelCase__       =	"docs/source/en/tasks"





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Any   )    ->			int:







			'''simple docstring'''
			with open(_UpperCAmelCase							,       'r'							,       encoding='utf-8'							,       newline='\n'   ) as f:
						_UpperCAmelCase			=       f.readlines()
			# Find the start prompt.
			_UpperCAmelCase			=       0
			while not lines[start_index].startswith(_UpperCAmelCase   ):
						start_index += 1
			start_index += 1

			_UpperCAmelCase			=       start_index
			while not lines[end_index].startswith(_UpperCAmelCase   ):
						end_index += 1
			end_index -= 1

			while len(lines[start_index]   ) <= 1:
						start_index += 1
			while len(lines[end_index]   ) <= 1:
						end_index -= 1
			end_index += 1
			return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase__       =	direct_transformers_import(TRANSFORMERS_PATH)

UpperCAmelCase__       =	{
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
UpperCAmelCase__       =	{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       TASK_GUIDE_TO_MODELS[task_guide]
			_UpperCAmelCase			=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCAmelCase							,       set()   )
			_UpperCAmelCase			=       {
			    code: name
			    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
			    if (code in model_maping_names or code in special_model_types)
			}
			return ", ".join([F"[{name}](../model_doc/{code})" for code, name in model_names.items()]   ) + "\n"





def 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Dict=False   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       _find_text_in_file(
			    filename=os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )							,       start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'							,       end_prompt='<!--End of the generated tip-->'							,       )

			_UpperCAmelCase			=       get_model_list_for_task(_UpperCAmelCase   )

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )							,       'w'							,       encoding='utf-8'							,       newline='\n'   ) as f:
												f.writelines(lines[:start_index] + [new_list] + lines[end_index:]   )
						else:
									raise ValueError(
									    F"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
									    ' to fix this.'   )


if __name__ == "__main__":
							UpperCAmelCase__       =	argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							UpperCAmelCase__       =	parser.parse_args()

							for task_guide in TASK_GUIDE_TO_MODELS.keys():
														check_model_list_for_task(task_guide, args.fix_and_overwrite)
