





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 					__A    (    lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						LongformerTokenizer
			lowerCAmelCase_							     =						True
			lowerCAmelCase_							     =						LongformerTokenizerFast
			lowerCAmelCase_							     =						True





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				lowerCamelCase__								=		[
				    '''l''',
				    '''o''',
				    '''w''',
				    '''e''',
				    '''r''',
				    '''s''',
				    '''t''',
				    '''i''',
				    '''d''',
				    '''n''',
				    '''\u0120''',
				    '''\u0120l''',
				    '''\u0120n''',
				    '''\u0120lo''',
				    '''\u0120low''',
				    '''er''',
				    '''\u0120lowest''',
				    '''\u0120newer''',
				    '''\u0120wider''',
				    '''<unk>''',
				]
				lowerCamelCase__								=		dict(zip(__lowerCAmelCase       ,  range(len(__lowerCAmelCase							)							)							)							)
				lowerCamelCase__								=		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
				lowerCamelCase__								=		{'''unk_token''': '''<unk>'''}

				lowerCamelCase__								=		os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['''vocab_file''']							)
				lowerCamelCase__								=		os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['''merges_file''']							)
				with open(self.vocab_file       ,  '''w'''       ,  encoding='''utf-8'''							) as fp:
					fp.write(json.dumps(__lowerCAmelCase							) + '''\n'''							)
				with open(self.merges_file       ,  '''w'''       ,  encoding='''utf-8'''							) as fp:
					fp.write('''\n'''.join(__lowerCAmelCase							)							)





			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				kwargs.update(self.special_tokens_map							)
				return self.tokenizer_class.from_pretrained(self.tmpdirname       ,  **__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				kwargs.update(self.special_tokens_map							)
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,  **__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		'''lower newer'''
				lowerCamelCase__								=		'''lower newer'''
				return input_text, output_text





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.tokenizer_class(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map							)
				lowerCamelCase__								=		'''lower newer'''
				lowerCamelCase__								=		['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
				lowerCamelCase__								=		tokenizer.tokenize(__lowerCAmelCase							)  # , add_prefix_space=True)
				self.assertListEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		tokens + [tokenizer.unk_token]
				lowerCamelCase__								=		[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase							)       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_tokenizer()

				self.assertListEqual(tokenizer.encode('''Hello world!'''       ,  add_special_tokens=__lowerCAmelCase							)       ,  [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]							)
				self.assertListEqual(
				    tokenizer.encode('''Hello world! cécé herlolip 418'''       ,  add_special_tokens=__lowerCAmelCase							)       ,  [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]       ,  )





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''							)

				lowerCamelCase__								=		tokenizer.encode('''sequence builders'''       ,  add_special_tokens=__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.encode('''multi-sequence build'''       ,  add_special_tokens=__lowerCAmelCase							)

				lowerCamelCase__								=		tokenizer.encode(
				    '''sequence builders'''       ,  add_special_tokens=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.encode(
				    '''sequence builders'''       ,  '''multi-sequence build'''       ,  add_special_tokens=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase							)

				lowerCamelCase__								=		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase       ,  __lowerCAmelCase							)

				assert encoded_sentence == encoded_text_from_decode
				assert encoded_pair == encoded_pair_from_decode





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_tokenizer()

				lowerCamelCase__								=		'''Encode this sequence.'''
				lowerCamelCase__								=		tokenizer.byte_encoder[''' '''.encode('''utf-8'''							)[0]]

				# Testing encoder arguments
				lowerCamelCase__								=		tokenizer.encode(__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
				self.assertNotEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		tokenizer.encode(__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
				self.assertEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)

				tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}							)
				lowerCamelCase__								=		tokenizer.encode(__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
				self.assertNotEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)

				# Testing spaces after special tokens
				lowerCamelCase__								=		'''<mask>'''
				tokenizer.add_special_tokens(
				    {'''mask_token''': AddedToken(__lowerCAmelCase       ,  lstrip=__lowerCAmelCase       ,  rstrip=__lowerCAmelCase							)}							)  # mask token has a left space
				lowerCamelCase__								=		tokenizer.convert_tokens_to_ids(__lowerCAmelCase							)

				lowerCamelCase__								=		'''Encode <mask> sequence'''
				lowerCamelCase__								=		'''Encode <mask>sequence'''

				lowerCamelCase__								=		tokenizer.encode(__lowerCAmelCase							)
				lowerCamelCase__								=		encoded.index(__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
				self.assertEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		tokenizer.encode(__lowerCAmelCase							)
				lowerCamelCase__								=		encoded.index(__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
				self.assertNotEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
						lowerCamelCase__								=		self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,  **__lowerCAmelCase							)
						lowerCamelCase__								=		self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,  **__lowerCAmelCase							)
						lowerCamelCase__								=		'''A, <mask> AllenNLP sentence.'''
						lowerCamelCase__								=		tokenizer_r.encode_plus(__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  return_token_type_ids=__lowerCAmelCase							)
						lowerCamelCase__								=		tokenizer_p.encode_plus(__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  return_token_type_ids=__lowerCAmelCase							)

						# token_type_ids should put 0 everywhere
						self.assertEqual(sum(tokens_r['''token_type_ids''']							)       ,  sum(tokens_p['''token_type_ids''']							)							)

						# attention_mask should put 1 everywhere, so sum over length should be 1
						self.assertEqual(
						    sum(tokens_r['''attention_mask''']							) / len(tokens_r['''attention_mask''']							)       ,  sum(tokens_p['''attention_mask''']							) / len(tokens_p['''attention_mask''']							)       ,  )

						lowerCamelCase__								=		tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
						lowerCamelCase__								=		tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']							)

						# Rust correctly handles the space before the mask while python doesnt
						self.assertSequenceEqual(tokens_p['''input_ids''']       ,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)
						self.assertSequenceEqual(tokens_r['''input_ids''']       ,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)

						self.assertSequenceEqual(
						    __lowerCAmelCase       ,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
						self.assertSequenceEqual(
						    __lowerCAmelCase       ,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for trim_offsets, add_prefix_space in itertools.product([True, False]       ,  repeat=2							):
					lowerCamelCase__								=		self.rust_tokenizer_class.from_pretrained(
					    self.tmpdirname       ,  use_fast=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase       ,  trim_offsets=__lowerCAmelCase							)

					lowerCamelCase__								=		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
					lowerCamelCase__								=		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

					self.assertEqual(pre_tokenizer_state['''add_prefix_space''']       ,  __lowerCAmelCase							)

					self.assertEqual(post_processor_state['''add_prefix_space''']       ,  __lowerCAmelCase							)
					self.assertEqual(post_processor_state['''trim_offsets''']       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
						lowerCamelCase__								=		'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
						lowerCamelCase__								=		F'{text_of_1_token} {text_of_1_token}'

						lowerCamelCase__								=		self.rust_tokenizer_class.from_pretrained(
						    __lowerCAmelCase       ,  use_fast=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase       ,  trim_offsets=__lowerCAmelCase							)
						lowerCamelCase__								=		tokenizer_r(__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase							)
						self.assertEqual(encoding.offset_mapping[0]       ,  (0, len(__lowerCAmelCase							))							)
						self.assertEqual(
						    encoding.offset_mapping[1]       ,  (len(__lowerCAmelCase							) + 1, len(__lowerCAmelCase							) + 1 + len(__lowerCAmelCase							))       ,  )

						lowerCamelCase__								=		self.rust_tokenizer_class.from_pretrained(
						    __lowerCAmelCase       ,  use_fast=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase       ,  trim_offsets=__lowerCAmelCase							)
						lowerCamelCase__								=		tokenizer_r(__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase							)
						self.assertEqual(encoding.offset_mapping[0]       ,  (0, len(__lowerCAmelCase							))							)
						self.assertEqual(
						    encoding.offset_mapping[1]       ,  (len(__lowerCAmelCase							) + 1, len(__lowerCAmelCase							) + 1 + len(__lowerCAmelCase							))       ,  )

						lowerCamelCase__								=		self.rust_tokenizer_class.from_pretrained(
						    __lowerCAmelCase       ,  use_fast=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase       ,  trim_offsets=__lowerCAmelCase							)
						lowerCamelCase__								=		tokenizer_r(__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase							)
						self.assertEqual(encoding.offset_mapping[0]       ,  (0, len(__lowerCAmelCase							))							)
						self.assertEqual(
						    encoding.offset_mapping[1]       ,  (len(__lowerCAmelCase							), len(__lowerCAmelCase							) + 1 + len(__lowerCAmelCase							))       ,  )

						lowerCamelCase__								=		self.rust_tokenizer_class.from_pretrained(
						    __lowerCAmelCase       ,  use_fast=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase       ,  trim_offsets=__lowerCAmelCase							)
						lowerCamelCase__								=		tokenizer_r(__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase							)
						self.assertEqual(encoding.offset_mapping[0]       ,  (0, len(__lowerCAmelCase							))							)
						self.assertEqual(
						    encoding.offset_mapping[1]       ,  (len(__lowerCAmelCase							), len(__lowerCAmelCase							) + 1 + len(__lowerCAmelCase							))       ,  )

						lowerCamelCase__								=		F' {text}'

						# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
						#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
						# )
						# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
						# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
						# self.assertEqual(
						#     encoding.offset_mapping[1],
						#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
						# )

						lowerCamelCase__								=		self.rust_tokenizer_class.from_pretrained(
						    __lowerCAmelCase       ,  use_fast=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase       ,  trim_offsets=__lowerCAmelCase							)
						lowerCamelCase__								=		tokenizer_r(__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase							)
						self.assertEqual(encoding.offset_mapping[0]       ,  (1, 1 + len(__lowerCAmelCase							))							)
						self.assertEqual(
						    encoding.offset_mapping[1]       ,  (1 + len(__lowerCAmelCase							) + 1, 1 + len(__lowerCAmelCase							) + 1 + len(__lowerCAmelCase							))       ,  )

						lowerCamelCase__								=		self.rust_tokenizer_class.from_pretrained(
						    __lowerCAmelCase       ,  use_fast=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase       ,  trim_offsets=__lowerCAmelCase							)
						lowerCamelCase__								=		tokenizer_r(__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase							)
						self.assertEqual(encoding.offset_mapping[0]       ,  (0, 1 + len(__lowerCAmelCase							))							)
						self.assertEqual(
						    encoding.offset_mapping[1]       ,  (1 + len(__lowerCAmelCase							), 1 + len(__lowerCAmelCase							) + 1 + len(__lowerCAmelCase							))       ,  )

						lowerCamelCase__								=		self.rust_tokenizer_class.from_pretrained(
						    __lowerCAmelCase       ,  use_fast=__lowerCAmelCase       ,  add_prefix_space=__lowerCAmelCase       ,  trim_offsets=__lowerCAmelCase							)
						lowerCamelCase__								=		tokenizer_r(__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase							)
						self.assertEqual(encoding.offset_mapping[0]       ,  (0, 1 + len(__lowerCAmelCase							))							)
						self.assertEqual(
						    encoding.offset_mapping[1]       ,  (1 + len(__lowerCAmelCase							), 1 + len(__lowerCAmelCase							) + 1 + len(__lowerCAmelCase							))       ,  )










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()
_a 						=      logging.get_logger(__name__)

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





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[Any]:







	'''simple docstring'''







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

	if weight_type is not None:
		lowerCamelCase__								=		getattr(__snake_case       ,__snake_case							).shape
	else:
		lowerCamelCase__								=		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":
		lowerCamelCase__								=		value
	elif weight_type == "weight_g":
		lowerCamelCase__								=		value
	elif weight_type == "weight_v":
		lowerCamelCase__								=		value
	elif weight_type == "bias":
		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__(__snake_case       ,__snake_case							)				->      int:







	'''simple docstring'''







	lowerCamelCase__								=		[]
	lowerCamelCase__								=		fairseq_model.state_dict()

	lowerCamelCase__								=		hf_model.feature_extractor

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

	for name, value in fairseq_dict.items():
		lowerCamelCase__								=		False
		if "conv_layers" in name:
			load_conv_layer(
			    __snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,hf_model.config.feat_extract_norm == '''group'''       ,)
			lowerCamelCase__								=		True
		elif name.split('''.'''							)[0] == "proj":
			lowerCamelCase__								=		fairseq_model.proj
			lowerCamelCase__								=		True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
					lowerCamelCase__								=		True
					if "*" in mapped_key:
						lowerCamelCase__								=		name.split(__snake_case							)[0].split('''.'''							)[-2]
						lowerCamelCase__								=		mapped_key.replace('''*'''       ,__snake_case							)
					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:
						lowerCamelCase__								=		'''weight'''
					else:
						lowerCamelCase__								=		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}'							)

	return proj_weight





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[int]:







	'''simple docstring'''







	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:
			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.'
			)
			lowerCamelCase__								=		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.'
			)
			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:
			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."
			)
			lowerCamelCase__								=		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.'
			)
			lowerCamelCase__								=		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							)				->      Any:







	'''simple docstring'''







	lowerCamelCase__				,	lowerCamelCase__								=		emb.weight.shape
	lowerCamelCase__								=		nn.Linear(__snake_case       ,__snake_case       ,bias=__snake_case							)
	lowerCamelCase__								=		emb.weight.data
	return lin_layer





def lowerCAmelCase__(__snake_case							)				->      Any:







	'''simple docstring'''







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

	lowerCamelCase__								=		len(__snake_case							)

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

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





@torch.no_grad()
def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,)				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		WavaVecaConfig.from_pretrained(__snake_case							)
	lowerCamelCase__								=		SpeechaTextaConfig.from_pretrained(
	    __snake_case       ,vocab_size=__snake_case       ,decoder_layers=__snake_case       ,do_stable_layer_norm=__snake_case							)

	lowerCamelCase__								=		WavaVecaFeatureExtractor(
	    feature_size=1       ,sampling_rate=16000       ,padding_value=0       ,do_normalize=__snake_case       ,return_attention_mask=__snake_case       ,)

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

	# set weights for wav2vec2 encoder
	lowerCamelCase__								=		WavaVecaModel(__snake_case							)
	lowerCamelCase__								=		recursively_load_weights_wavaveca(model.encoder       ,__snake_case							)

	lowerCamelCase__								=		SpeechaTextaForCausalLM(__snake_case							)
	lowerCamelCase__				,	lowerCamelCase__								=		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,strict=__snake_case							)

	# set output linear layer
	unexpected_keys.remove('''embed_out'''							)
	lowerCamelCase__								=		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}'							)

	lowerCamelCase__								=		SpeechEncoderDecoderModel(encoder=__snake_case       ,decoder=__snake_case							)
	lowerCamelCase__								=		False

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

	lowerCamelCase__								=		create_vocab_dict(__snake_case							)

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

	lowerCamelCase__								=		SpeechaTextaTokenizer(os.path.join(__snake_case       ,'''vocab.json'''							)							)
	tokenizer.save_pretrained(__snake_case							)

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

	lowerCamelCase__								=		SpeechEncoderDecoderConfig.from_dict(__snake_case							)

	hf_wavavec.save_pretrained(__snake_case							)
	feature_extractor.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(
			    "--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=10_224, type=int, help="Vocab size of decoder")
			parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

			_a 						=      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,
			)



