




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConditionalDetrImageProcessor





class     lowerCamelCase__(					unittest.TestCase):
      def __init__(				self: str    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: List[str]=7    ,       UpperCamelCase_: List[Any]=3    ,       UpperCamelCase_: Optional[Any]=30    ,       UpperCamelCase_: Optional[int]=4_00    ,       UpperCamelCase_: Optional[int]=True    ,       UpperCamelCase_: Optional[int]=None    ,       UpperCamelCase_: Dict=True    ,       UpperCamelCase_: Optional[int]=[0.5, 0.5, 0.5]    ,       UpperCamelCase_: Union[str, Any]=[0.5, 0.5, 0.5]    ,       UpperCamelCase_: int=True    ,       UpperCamelCase_: List[Any]=1 / 2_55    ,       UpperCamelCase_: Dict=True    ,       ):
             __lowerCamelCase   =						size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						num_channels
             __lowerCamelCase   =						min_resolution
             __lowerCamelCase   =						max_resolution
             __lowerCamelCase   =						do_resize
             __lowerCamelCase   =						size
             __lowerCamelCase   =						do_normalize
             __lowerCamelCase   =						image_mean
             __lowerCamelCase   =						image_std
             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_pad": self.do_pad,
             }




      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[int]=False   ):







             if not batched:
                    __lowerCamelCase   =						image_inputs[0]
                    if isinstance(__lowerCAmelCase    ,       Image.Image   ):
                           __lowerCamelCase,   __lowerCamelCase   =						image.size
                    else:
                           __lowerCamelCase,   __lowerCamelCase   =						image.shape[1], image.shape[2]
                    if w < h:
                           __lowerCamelCase   =						int(self.size["""shortest_edge"""] * h / w   )
                           __lowerCamelCase   =						self.size["""shortest_edge"""]
                    elif w > h:
                           __lowerCamelCase   =						self.size["""shortest_edge"""]
                           __lowerCamelCase   =						int(self.size["""shortest_edge"""] * w / h   )
                    else:
                           __lowerCamelCase   =						self.size["""shortest_edge"""]
                           __lowerCamelCase   =						self.size["""shortest_edge"""]

             else:
                    __lowerCamelCase   =						[]
                    for image in image_inputs:
                           __lowerCamelCase,   __lowerCamelCase   =						self.get_expected_values([image]   )
                           expected_values.append((expected_height, expected_width)   )
                    __lowerCamelCase   =						max(__lowerCAmelCase    ,       key=lambda UpperCamelCase_   : item[0]   )[0]
                    __lowerCamelCase   =						max(__lowerCAmelCase    ,       key=lambda UpperCamelCase_   : item[1]   )[1]

             return expected_height, expected_width





@require_torch
@require_vision
class     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	str    		=   ConditionalDetrImageProcessor if is_vision_available() else None
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						ConditionalDetrImageProcessingTester(self   )
      @property
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             return self.image_processor_tester.prepare_image_processor_dict()
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             self.assertTrue(hasattr(__lowerCAmelCase    ,       """image_mean"""   )   )
             self.assertTrue(hasattr(__lowerCAmelCase    ,       """image_std"""   )   )
             self.assertTrue(hasattr(__lowerCAmelCase    ,       """do_normalize"""   )   )
             self.assertTrue(hasattr(__lowerCAmelCase    ,       """do_resize"""   )   )
             self.assertTrue(hasattr(__lowerCAmelCase    ,       """size"""   )   )
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						self.image_processing_class.from_dict(self.image_processor_dict   )
             self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 18, """longest_edge""": 13_33}   )
             self.assertEqual(image_processor.do_pad    ,       __lowerCAmelCase   )

             __lowerCamelCase   =						self.image_processing_class.from_dict(
                 self.image_processor_dict    ,       size=42    ,       max_size=84    ,       pad_and_return_pixel_mask=__lowerCAmelCase   )
             self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 42, """longest_edge""": 84}   )
             self.assertEqual(image_processor.do_pad    ,       __lowerCAmelCase   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             pass
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             __lowerCamelCase   =						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCAmelCase   )
             for image in image_inputs:
                    self.assertIsInstance(__lowerCAmelCase    ,       Image.Image   )

             # Test not batched input
             __lowerCamelCase   =						image_processing(image_inputs[0]    ,       return_tensors="""pt"""   ).pixel_values

             __lowerCamelCase,   __lowerCamelCase   =						self.image_processor_tester.get_expected_values(__lowerCAmelCase   )

             self.assertEqual(
                 encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

             # Test batched
             __lowerCamelCase,   __lowerCamelCase   =						self.image_processor_tester.get_expected_values(__lowerCAmelCase    ,       batched=__lowerCAmelCase   )

             __lowerCamelCase   =						image_processing(__lowerCAmelCase    ,       return_tensors="""pt"""   ).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,       (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )    ,       )
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random numpy tensors
             __lowerCamelCase   =						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCAmelCase    ,       numpify=__lowerCAmelCase   )
             for image in image_inputs:
                    self.assertIsInstance(__lowerCAmelCase    ,       np.ndarray   )

             # Test not batched input
             __lowerCamelCase   =						image_processing(image_inputs[0]    ,       return_tensors="""pt"""   ).pixel_values

             __lowerCamelCase,   __lowerCamelCase   =						self.image_processor_tester.get_expected_values(__lowerCAmelCase   )

             self.assertEqual(
                 encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

             # Test batched
             __lowerCamelCase   =						image_processing(__lowerCAmelCase    ,       return_tensors="""pt"""   ).pixel_values

             __lowerCamelCase,   __lowerCamelCase   =						self.image_processor_tester.get_expected_values(__lowerCAmelCase    ,       batched=__lowerCAmelCase   )

             self.assertEqual(
                 encoded_images.shape    ,       (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )    ,       )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PyTorch tensors
             __lowerCamelCase   =						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCAmelCase    ,       torchify=__lowerCAmelCase   )
             for image in image_inputs:
                    self.assertIsInstance(__lowerCAmelCase    ,       torch.Tensor   )

             # Test not batched input
             __lowerCamelCase   =						image_processing(image_inputs[0]    ,       return_tensors="""pt"""   ).pixel_values

             __lowerCamelCase,   __lowerCamelCase   =						self.image_processor_tester.get_expected_values(__lowerCAmelCase   )

             self.assertEqual(
                 encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

             # Test batched
             __lowerCamelCase   =						image_processing(__lowerCAmelCase    ,       return_tensors="""pt"""   ).pixel_values

             __lowerCamelCase,   __lowerCamelCase   =						self.image_processor_tester.get_expected_values(__lowerCAmelCase    ,       batched=__lowerCAmelCase   )

             self.assertEqual(
                 encoded_images.shape    ,       (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )    ,       )
      @slow
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
             with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,       """r"""   ) as f:
                    __lowerCamelCase   =						json.loads(f.read()   )

             __lowerCamelCase   =						{"""image_id""": 3_97_69, """annotations""": target}

             # encode them
             __lowerCamelCase   =						ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""   )
             __lowerCamelCase   =						image_processing(images=__lowerCAmelCase    ,       annotations=__lowerCAmelCase    ,       return_tensors="""pt"""   )

             # verify pixel values
             __lowerCamelCase   =						torch.Size([1, 3, 8_00, 10_66]   )
             self.assertEqual(encoding["""pixel_values"""].shape    ,       __lowerCAmelCase   )

             __lowerCamelCase   =						torch.tensor([0.2796, 0.3138, 0.3481]   )
             self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,       __lowerCAmelCase    ,       atol=1E-4   )   )

             # verify area
             __lowerCamelCase   =						torch.tensor([5887.9600, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,       __lowerCAmelCase   )   )
             # verify boxes
             __lowerCamelCase   =						torch.Size([6, 4]   )
             self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,       __lowerCAmelCase   )
             __lowerCamelCase   =						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,       __lowerCAmelCase    ,       atol=1E-3   )   )
             # verify image_id
             __lowerCamelCase   =						torch.tensor([3_97_69]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,       __lowerCAmelCase   )   )
             # verify is_crowd
             __lowerCamelCase   =						torch.tensor([0, 0, 0, 0, 0, 0]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,       __lowerCAmelCase   )   )
             # verify class_labels
             __lowerCamelCase   =						torch.tensor([75, 75, 63, 65, 17, 17]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,       __lowerCAmelCase   )   )
             # verify orig_size
             __lowerCamelCase   =						torch.tensor([4_80, 6_40]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,       __lowerCAmelCase   )   )
             # verify size
             __lowerCamelCase   =						torch.tensor([8_00, 10_66]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,       __lowerCAmelCase   )   )




      @slow
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
             with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    ,       """r"""   ) as f:
                    __lowerCamelCase   =						json.loads(f.read()   )

             __lowerCamelCase   =						{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

             __lowerCamelCase   =						pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

             # encode them
             __lowerCamelCase   =						ConditionalDetrImageProcessor(format="""coco_panoptic"""   )
             __lowerCamelCase   =						image_processing(images=__lowerCAmelCase    ,       annotations=__lowerCAmelCase    ,       masks_path=__lowerCAmelCase    ,       return_tensors="""pt"""   )

             # verify pixel values
             __lowerCamelCase   =						torch.Size([1, 3, 8_00, 10_66]   )
             self.assertEqual(encoding["""pixel_values"""].shape    ,       __lowerCAmelCase   )

             __lowerCamelCase   =						torch.tensor([0.2796, 0.3138, 0.3481]   )
             self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,       __lowerCAmelCase    ,       atol=1E-4   )   )

             # verify area
             __lowerCamelCase   =						torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5879.6562, 7634.1147]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,       __lowerCAmelCase   )   )
             # verify boxes
             __lowerCamelCase   =						torch.Size([6, 4]   )
             self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,       __lowerCAmelCase   )
             __lowerCamelCase   =						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,       __lowerCAmelCase    ,       atol=1E-3   )   )
             # verify image_id
             __lowerCamelCase   =						torch.tensor([3_97_69]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,       __lowerCAmelCase   )   )
             # verify is_crowd
             __lowerCamelCase   =						torch.tensor([0, 0, 0, 0, 0, 0]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,       __lowerCAmelCase   )   )
             # verify class_labels
             __lowerCamelCase   =						torch.tensor([17, 17, 63, 75, 75, 93]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,       __lowerCAmelCase   )   )
             # verify masks
             __lowerCamelCase   =						82_28_73
             self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,       __lowerCAmelCase   )
             # verify orig_size
             __lowerCamelCase   =						torch.tensor([4_80, 6_40]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,       __lowerCAmelCase   )   )
             # verify size
             __lowerCamelCase   =						torch.tensor([8_00, 10_66]   )
             self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,       __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,
			)



