




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     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	int    		=   LongformerTokenizer
      UpperCAmelCase__      :	Tuple    		=   True
      UpperCAmelCase__      :	Optional[Any]    		=   LongformerTokenizerFast
      UpperCAmelCase__      :	Optional[int]    		=   True
      def 			lowerCAmelCase__						(				self: Tuple   ):
             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: Dict    ,       **UpperCamelCase_: Tuple   ):
             kwargs.update(self.special_tokens_map   )
             return self.tokenizer_class.from_pretrained(self.tmpdirname    ,       **__lowerCAmelCase   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       **UpperCamelCase_: Optional[int]   ):
             kwargs.update(self.special_tokens_map   )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,       **__lowerCAmelCase   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]   ):
             __lowerCamelCase   =						"""lower newer"""
             __lowerCamelCase   =						"""lower newer"""
             return input_text, output_text
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __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, 15, 10, 9, 3, 2, 15, 19]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase   )    ,       __lowerCAmelCase   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.get_tokenizer()

             self.assertListEqual(tokenizer.encode("""Hello world!"""    ,       add_special_tokens=__lowerCAmelCase   )    ,       [0, 3_14_14, 2_32, 3_28, 2]   )
             self.assertListEqual(
                 tokenizer.encode("""Hello world! cécé herlolip 418"""    ,       add_special_tokens=__lowerCAmelCase   )    ,       [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]    ,       )
      @slow
      def 			lowerCAmelCase__						(				self: Dict   ):
             __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: Tuple   ):
             __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: Any   ):
             pass
      def 			lowerCAmelCase__						(				self: List[str]   ):







             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_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
                           self.assertSequenceEqual(tokens_r["""input_ids"""]    ,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 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: Dict   ):







             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: int   ):







             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,
			)



