



"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

__SCREAMING_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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
__SCREAMING_SNAKE_CASE    							=      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]



def        lowerCAmelCase_(   lowercase_     :							List[str]							)	->       Tuple:
				_lowerCamelCase        =				{}
				with open(lowercase_						,   '''r'''							) as file:
								for line_number, line in enumerate(lowercase_							):
												_lowerCamelCase        =				line.strip()
												if line:
																_lowerCamelCase        =				line.split()
																_lowerCamelCase        =				line_number
																_lowerCamelCase        =				words[0]
																_lowerCamelCase        =				value
				return result



def        lowerCAmelCase_(   lowercase_     :							Dict						,   lowercase_     :							str						,   lowercase_     :							Tuple						,   lowercase_     :							List[Any]						,   lowercase_     :							Union[str, Any]							)	->       Optional[int]:
				for attribute in key.split('''.'''							):
								_lowerCamelCase        =				getattr(lowercase_						,   lowercase_							)

				_lowerCamelCase        =				None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(lowercase_							):
												_lowerCamelCase        =				PARAM_MAPPING[full_name.split('''.'''							)[-1]]
												_lowerCamelCase        =				'''param'''

				if weight_type is not None and weight_type != "param":
								_lowerCamelCase        =				getattr(lowercase_						,   lowercase_							).shape
				elif weight_type is not None and weight_type == "param":
								_lowerCamelCase        =				hf_pointer
								for attribute in hf_param_name.split('''.'''							):
												_lowerCamelCase        =				getattr(lowercase_						,   lowercase_							)
								_lowerCamelCase        =				shape_pointer.shape

								# let's reduce dimension
								_lowerCamelCase        =				value[0]
				else:
								_lowerCamelCase        =				hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    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":
								_lowerCamelCase        =				value
				elif weight_type == "weight_g":
								_lowerCamelCase        =				value
				elif weight_type == "weight_v":
								_lowerCamelCase        =				value
				elif weight_type == "bias":
								_lowerCamelCase        =				value
				elif weight_type == "param":
								for attribute in hf_param_name.split('''.'''							):
												_lowerCamelCase        =				getattr(lowercase_						,   lowercase_							)
								_lowerCamelCase        =				value
				else:
								_lowerCamelCase        =				value

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



def        lowerCAmelCase_(   lowercase_     :							Any						,   lowercase_     :							Optional[Any]						,   lowercase_     :							Optional[int]						,   lowercase_     :							Optional[int]						,   lowercase_     :							List[str]							)	->       Dict:
				_lowerCamelCase        =				None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(lowercase_							):
												_lowerCamelCase        =				PARAM_MAPPING[full_name.split('''.'''							)[-1]]
												_lowerCamelCase        =				'''param'''

				if weight_type is not None and weight_type != "param":
								_lowerCamelCase        =				'''.'''.join([key, weight_type]							)
				elif weight_type is not None and weight_type == "param":
								_lowerCamelCase        =				'''.'''.join([key, hf_param_name]							)
				else:
								_lowerCamelCase        =				key

				_lowerCamelCase        =				value if '''lm_head''' in full_key else value[0]


__SCREAMING_SNAKE_CASE    							=      {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}



def        lowerCAmelCase_(   lowercase_     :							List[Any]						,   lowercase_     :							Dict						,   lowercase_     :							Any=None						,   lowercase_     :							Optional[int]=None							)	->       Dict:
				_lowerCamelCase        =				False
				for key, mapped_key in MAPPING.items():
								_lowerCamelCase        =				'''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
												_lowerCamelCase        =				True
												if "*" in mapped_key:
																_lowerCamelCase        =				name.split(lowercase_							)[0].split('''.'''							)[-2]
																_lowerCamelCase        =				mapped_key.replace('''*'''						,   lowercase_							)
												if "weight_g" in name:
																_lowerCamelCase        =				'''weight_g'''
												elif "weight_v" in name:
																_lowerCamelCase        =				'''weight_v'''
												elif "bias" in name:
																_lowerCamelCase        =				'''bias'''
												elif "weight" in name:
																# TODO: don't match quantizer.weight_proj
																_lowerCamelCase        =				'''weight'''
												else:
																_lowerCamelCase        =				None
												if hf_dict is not None:
																rename_dict(lowercase_						,   lowercase_						,   lowercase_						,   lowercase_						,   lowercase_							)
												else:
																set_recursively(lowercase_						,   lowercase_						,   lowercase_						,   lowercase_						,   lowercase_							)
												return is_used
				return is_used



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]						,   lowercase_     :							Tuple						,   lowercase_     :							Tuple							)	->       Optional[int]:
				_lowerCamelCase        =				[]
				_lowerCamelCase        =				fairseq_model.state_dict()

				_lowerCamelCase        =				hf_model.wavaveca.feature_extractor

				for name, value in fairseq_dict.items():
								_lowerCamelCase        =				False
								if "conv_layers" in name:
												load_conv_layer(
												    lowercase_						,   lowercase_						,   lowercase_						,   lowercase_						,   hf_model.config.feat_extract_norm == '''group'''						,   )
												_lowerCamelCase        =				True
								else:
												_lowerCamelCase        =				load_wavaveca_layer(lowercase_						,   lowercase_						,   lowercase_							)
								if not is_used:
												unused_weights.append(lowercase_							)

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



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							str						,   lowercase_     :							List[Any]						,   lowercase_     :							List[str]							)	->       List[Any]:
				_lowerCamelCase        =				full_name.split('''conv_layers.'''							)[-1]
				_lowerCamelCase        =				name.split('''.'''							)
				_lowerCamelCase        =				int(items[0]							)
				_lowerCamelCase        =				int(items[1]							)

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    F"""{full_name} has size {value.shape}, but"""
																    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""							)
												_lowerCamelCase        =				value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    F"""{full_name} has size {value.shape}, but"""
																    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""							)
												_lowerCamelCase        =				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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    F"""{full_name} has size {value.shape}, but"""
																    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""							)
												_lowerCamelCase        =				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    F"""{full_name} has size {value.shape}, but"""
																    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""							)
												_lowerCamelCase        =				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
				else:
								unused_weights.append(lowercase_							)



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							Optional[int]						,   lowercase_     :							int=None						,   lowercase_     :							int=None						,   lowercase_     :							List[Any]=True						,   lowercase_     :							Optional[Any]=False							)	->       Any:
				if config_path is not None:
								_lowerCamelCase        =				WavaVecaConfig.from_pretrained(lowercase_							)
				else:
								_lowerCamelCase        =				WavaVecaConfig()

				if is_seq_class:
								_lowerCamelCase        =				read_txt_into_dict(lowercase_							)
								_lowerCamelCase        =				idalabel
								_lowerCamelCase        =				WavaVecaForSequenceClassification(lowercase_							)
								_lowerCamelCase        =				WavaVecaFeatureExtractor(
								    feature_size=1						,   sampling_rate=1_60_00						,   padding_value=0						,   do_normalize=lowercase_						,   return_attention_mask=lowercase_						,   )
								feature_extractor.save_pretrained(lowercase_							)

				elif is_finetuned:
								if dict_path:
												_lowerCamelCase        =				Dictionary.load(lowercase_							)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_lowerCamelCase        =				target_dict.pad_index
												_lowerCamelCase        =				target_dict.bos_index
												_lowerCamelCase        =				target_dict.eos_index
												_lowerCamelCase        =				len(target_dict.symbols							)
												_lowerCamelCase        =				os.path.join(lowercase_						,   '''vocab.json'''							)
												if not os.path.isdir(lowercase_							):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase_							)							)
																return
												os.makedirs(lowercase_						,   exist_ok=lowercase_							)
												_lowerCamelCase        =				target_dict.indices

												# fairseq has the <pad> and <s> switched
												_lowerCamelCase        =				0
												_lowerCamelCase        =				1
												with open(lowercase_						,   '''w'''						,   encoding='''utf-8'''							) as vocab_handle:
																json.dump(lowercase_						,   lowercase_							)
												_lowerCamelCase        =				WavaVecaCTCTokenizer(
												    lowercase_						,   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=lowercase_						,   )
												_lowerCamelCase        =				True if config.feat_extract_norm == '''layer''' else False
												_lowerCamelCase        =				WavaVecaFeatureExtractor(
												    feature_size=1						,   sampling_rate=1_60_00						,   padding_value=0						,   do_normalize=lowercase_						,   return_attention_mask=lowercase_						,   )
												_lowerCamelCase        =				WavaVecaProcessor(feature_extractor=lowercase_						,   tokenizer=lowercase_							)
												processor.save_pretrained(lowercase_							)

								_lowerCamelCase        =				WavaVecaForCTC(lowercase_							)
				else:
								_lowerCamelCase        =				WavaVecaForPreTraining(lowercase_							)

				if is_finetuned or is_seq_class:
								_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]						,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
				else:
								_lowerCamelCase        =				argparse.Namespace(task='''audio_pretraining'''							)
								_lowerCamelCase        =				fairseq.tasks.setup_task(lowercase_							)

								_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,   task=lowercase_							)

				_lowerCamelCase        =				model[0].eval()

				recursively_load_weights(lowercase_						,   lowercase_						,   not is_finetuned							)

				hf_wavavec.save_pretrained(lowercase_							)


if __name__ == "__main__":
			__SCREAMING_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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			parser.add_argument(
			    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
			)
			parser.add_argument(
			    '''--is_seq_class''',
			    action='''store_true''',
			    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
			)
			__SCREAMING_SNAKE_CASE    							=      parser.parse_args()

			__SCREAMING_SNAKE_CASE    							=      not args.not_finetuned and not args.is_seq_class
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.dict_path,
			    is_finetuned,
			    args.is_seq_class,
			)





"""simple docstring"""
import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM


@require_torch
class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=9_9				,       lowerCamelCase__=1_3				,       lowerCamelCase__=1_6				,       lowerCamelCase__=7				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__=2				,       lowerCamelCase__=3_2				,       lowerCamelCase__=4				,       lowerCamelCase__=4				,       lowerCamelCase__=3_0				,       lowerCamelCase__=0				,       lowerCamelCase__=1				,       lowerCamelCase__=2				,       lowerCamelCase__=None				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				decoder_seq_length
									# For common tests
									_lowerCamelCase        =				self.decoder_seq_length
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_attention_mask
									_lowerCamelCase        =				use_labels

									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				d_model
									_lowerCamelCase        =				d_model
									_lowerCamelCase        =				decoder_layers
									_lowerCamelCase        =				decoder_layers
									_lowerCamelCase        =				decoder_ffn_dim
									_lowerCamelCase        =				decoder_attention_heads
									_lowerCamelCase        =				decoder_attention_heads
									_lowerCamelCase        =				eos_token_id
									_lowerCamelCase        =				bos_token_id
									_lowerCamelCase        =				pad_token_id
									_lowerCamelCase        =				decoder_start_token_id
									_lowerCamelCase        =				use_cache
									_lowerCamelCase        =				max_position_embeddings

									_lowerCamelCase        =				None
									_lowerCamelCase        =				decoder_seq_length
									_lowerCamelCase        =				2
									_lowerCamelCase        =				1

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ids_tensor([self.batch_size, self.decoder_seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				None
									if self.use_attention_mask:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.decoder_seq_length]				,       vocab_size=2   )

									_lowerCamelCase        =				None
									if self.use_labels:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.decoder_seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				TrOCRConfig(
									    vocab_size=self.vocab_size				,       d_model=self.d_model				,       decoder_layers=self.decoder_layers				,       decoder_ffn_dim=self.decoder_ffn_dim				,       decoder_attention_heads=self.decoder_attention_heads				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.bos_token_id				,       use_cache=self.use_cache				,       pad_token_id=self.pad_token_id				,       decoder_start_token_id=self.decoder_start_token_id				,       max_position_embeddings=self.max_position_embeddings				,       )

									return (config, input_ids, attention_mask, lm_labels)

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       ):
									_lowerCamelCase        =				True
									_lowerCamelCase        =				TrOCRDecoder(config=lowerCamelCase__   ).to(lowerCamelCase__   ).eval()
									_lowerCamelCase        =				input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									_lowerCamelCase        =				model(lowerCamelCase__				,       use_cache=lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__				,       use_cache=lowerCamelCase__   )

									self.parent.assertTrue(len(lowerCamelCase__   ) == len(lowerCamelCase__   )   )
									self.parent.assertTrue(len(lowerCamelCase__   ) == len(lowerCamelCase__   ) + 1   )

									_lowerCamelCase        =				outputs['''past_key_values''']

									# create hypothetical next token and extent to next_input_ids
									_lowerCamelCase        =				ids_tensor((2, 1)				,       config.vocab_size - 1   ) + 1

									# append to next input_ids and
									_lowerCamelCase        =				torch.cat([input_ids, next_tokens]				,       dim=-1   )

									_lowerCamelCase        =				model(lowerCamelCase__   )['''last_hidden_state''']
									_lowerCamelCase        =				model(lowerCamelCase__				,       past_key_values=lowerCamelCase__   )['''last_hidden_state''']

									# select random slice
									_lowerCamelCase        =				ids_tensor((1,)				,       output_from_past.shape[-1]   ).item()
									_lowerCamelCase        =				output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									_lowerCamelCase        =				output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(lowerCamelCase__				,       lowerCamelCase__				,       atol=1e-3   )





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				config_and_inputs

									_lowerCamelCase        =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict


@require_torch
class 							lowerCamelCase_(   A__,				A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       int										= (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
					lowercase__   :       List[str]										= (TrOCRForCausalLM,) if is_torch_available() else ()
					lowercase__   :       Tuple										= {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
					lowercase__   :       Dict										= True
					lowercase__   :       Optional[Any]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TrOCRStandaloneDecoderModelTester(self				,       is_training=lowerCamelCase__   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									self.config_tester.run_common_tests()

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									return





					@unittest.skip('''The model doesn\'t support left padding'''   )  # and it's not used enough to be worth fixing :)
					def 			snake_case__      (    self   ):
									pass
