




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 YolosImageProcessor




class       _A				(		unittest.TestCase     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=7						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=30						,		_SCREAMING_SNAKE_CASE=400						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]						,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=1 / 255						,		_SCREAMING_SNAKE_CASE=True						,		):
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								_UpperCAmelCase      =			size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
								_UpperCAmelCase      =			parent
								_UpperCAmelCase      =			batch_size
								_UpperCAmelCase      =			num_channels
								_UpperCAmelCase      =			min_resolution
								_UpperCAmelCase      =			max_resolution
								_UpperCAmelCase      =			do_resize
								_UpperCAmelCase      =			size
								_UpperCAmelCase      =			do_normalize
								_UpperCAmelCase      =			image_mean
								_UpperCAmelCase      =			image_std
								_UpperCAmelCase      =			do_rescale
								_UpperCAmelCase      =			rescale_factor
								_UpperCAmelCase      =			do_pad






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

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

								return expected_height, expected_width




@require_torch
@require_vision
class       _A				(		__lowercase       ,     unittest.TestCase     ):
		__a					    =       YolosImageProcessor if is_vision_available() else None






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			YolosImageProcessingTester(self			)






		@property
		def 				UpperCAmelCase (    self			):
								return self.image_processor_tester.prepare_image_processor_dict()






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.image_processing_class(**self.image_processor_dict			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""image_mean"""			)			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""image_std"""			)			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""do_normalize"""			)			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""do_resize"""			)			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""size"""			)			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.image_processing_class.from_dict(self.image_processor_dict			)
								self.assertEqual(image_processor.size						,		{"""shortest_edge""": 18, """longest_edge""": 1333}			)
								self.assertEqual(image_processor.do_pad						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			self.image_processing_class.from_dict(
								    self.image_processor_dict						,		size=42						,		max_size=84						,		pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE			)
								self.assertEqual(image_processor.size						,		{"""shortest_edge""": 42, """longest_edge""": 84}			)
								self.assertEqual(image_processor.do_pad						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								pass






		def 				UpperCAmelCase (    self			):
								# Initialize image_processing
								_UpperCAmelCase      =			self.image_processing_class(**self.image_processor_dict			)
								# create random PIL images
								_UpperCAmelCase      =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_SCREAMING_SNAKE_CASE			)
								for image in image_inputs:
														self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		Image.Image			)

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

								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE			)

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

								# Test batched
								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						,		batched=_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			image_processing(_SCREAMING_SNAKE_CASE						,		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 				UpperCAmelCase (    self			):
								# Initialize image_processing
								_UpperCAmelCase      =			self.image_processing_class(**self.image_processor_dict			)
								# create random numpy tensors
								_UpperCAmelCase      =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_SCREAMING_SNAKE_CASE						,		numpify=_SCREAMING_SNAKE_CASE			)
								for image in image_inputs:
														self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		np.ndarray			)

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

								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE			)

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

								# Test batched
								_UpperCAmelCase      =			image_processing(_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			).pixel_values

								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						,		batched=_SCREAMING_SNAKE_CASE			)

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






		def 				UpperCAmelCase (    self			):
								# Initialize image_processing
								_UpperCAmelCase      =			self.image_processing_class(**self.image_processor_dict			)
								# create random PyTorch tensors
								_UpperCAmelCase      =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_SCREAMING_SNAKE_CASE						,		torchify=_SCREAMING_SNAKE_CASE			)
								for image in image_inputs:
														self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		torch.Tensor			)

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

								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE			)

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

								# Test batched
								_UpperCAmelCase      =			image_processing(_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			).pixel_values

								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						,		batched=_SCREAMING_SNAKE_CASE			)

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






		def 				UpperCAmelCase (    self			):
								# Initialize image_processings
								_UpperCAmelCase      =			self.image_processing_class(**self.image_processor_dict			)
								_UpperCAmelCase      =			self.image_processing_class(do_resize=_SCREAMING_SNAKE_CASE						,		do_normalize=_SCREAMING_SNAKE_CASE						,		do_rescale=_SCREAMING_SNAKE_CASE			)
								# create random PyTorch tensors
								_UpperCAmelCase      =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_SCREAMING_SNAKE_CASE						,		torchify=_SCREAMING_SNAKE_CASE			)
								for image in image_inputs:
														self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		torch.Tensor			)

								# Test whether the method "pad" and calling the image processor return the same tensors
								_UpperCAmelCase      =			image_processing_a.pad(_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			)
								_UpperCAmelCase      =			image_processing_a(_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			)

								self.assertTrue(
								    torch.allclose(encoded_images_with_method["""pixel_values"""]						,		encoded_images["""pixel_values"""]						,		atol=1e-4			)			)






		@slow
		def 				UpperCAmelCase (    self			):
								# prepare image and target
								_UpperCAmelCase      =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
								with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""						,		"""r"""			) as f:
														_UpperCAmelCase      =			json.loads(f.read()			)

								_UpperCAmelCase      =			{"""image_id""": 3_9769, """annotations""": target}

								# encode them
								_UpperCAmelCase      =			YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""			)
								_UpperCAmelCase      =			image_processing(images=_SCREAMING_SNAKE_CASE						,		annotations=_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			)

								# verify pixel values
								_UpperCAmelCase      =			torch.Size([1, 3, 800, 1066]			)
								self.assertEqual(encoding["""pixel_values"""].shape						,		_SCREAMING_SNAKE_CASE			)

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

								# verify area
								_UpperCAmelCase      =			torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify boxes
								_UpperCAmelCase      =			torch.Size([6, 4]			)
								self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,		_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,		_SCREAMING_SNAKE_CASE						,		atol=1e-3			)			)
								# verify image_id
								_UpperCAmelCase      =			torch.tensor([3_9769]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify is_crowd
								_UpperCAmelCase      =			torch.tensor([0, 0, 0, 0, 0, 0]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify class_labels
								_UpperCAmelCase      =			torch.tensor([75, 75, 63, 65, 17, 17]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify orig_size
								_UpperCAmelCase      =			torch.tensor([480, 640]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify size
								_UpperCAmelCase      =			torch.tensor([800, 1066]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,		_SCREAMING_SNAKE_CASE			)			)






		@slow
		def 				UpperCAmelCase (    self			):
								# prepare image, target and masks_path
								_UpperCAmelCase      =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
								with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""						,		"""r"""			) as f:
														_UpperCAmelCase      =			json.loads(f.read()			)

								_UpperCAmelCase      =			{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

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

								# encode them
								_UpperCAmelCase      =			YolosImageProcessor(format="""coco_panoptic"""			)
								_UpperCAmelCase      =			image_processing(images=_SCREAMING_SNAKE_CASE						,		annotations=_SCREAMING_SNAKE_CASE						,		masks_path=_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			)

								# verify pixel values
								_UpperCAmelCase      =			torch.Size([1, 3, 800, 1066]			)
								self.assertEqual(encoding["""pixel_values"""].shape						,		_SCREAMING_SNAKE_CASE			)

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

								# verify area
								_UpperCAmelCase      =			torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify boxes
								_UpperCAmelCase      =			torch.Size([6, 4]			)
								self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,		_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,		_SCREAMING_SNAKE_CASE						,		atol=1e-3			)			)
								# verify image_id
								_UpperCAmelCase      =			torch.tensor([3_9769]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify is_crowd
								_UpperCAmelCase      =			torch.tensor([0, 0, 0, 0, 0, 0]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify class_labels
								_UpperCAmelCase      =			torch.tensor([17, 17, 63, 75, 75, 93]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify masks
								_UpperCAmelCase      =			82_2873
								self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()						,		_SCREAMING_SNAKE_CASE			)
								# verify orig_size
								_UpperCAmelCase      =			torch.tensor([480, 640]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,		_SCREAMING_SNAKE_CASE			)			)
								# verify size
								_UpperCAmelCase      =			torch.tensor([800, 1066]			)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,		_SCREAMING_SNAKE_CASE			)			)





import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
a													=			logging.get_logger("transformers.models.speecht5")

a													=			{
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
a													=			{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
a													=			{
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
a													=			{
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
a													=			{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
a													=			{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
a													=			{
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
a													=			{
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
a													=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a													=			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a													=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a													=			[]
a													=			[
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
a													=			IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
a													=			IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
a													=			IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						for attribute in key.split("""."""       ):
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       )

						if weight_type is not None:
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       ).shape
						else:
												_UpperCAmelCase      =			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":
												_UpperCAmelCase      =			value
						elif weight_type == "weight_g":
												_UpperCAmelCase      =			value
						elif weight_type == "weight_v":
												_UpperCAmelCase      =			value
						elif weight_type == "bias":
												_UpperCAmelCase      =			value
						elif weight_type == "running_mean":
												_UpperCAmelCase      =			value
						elif weight_type == "running_var":
												_UpperCAmelCase      =			value
						elif weight_type == "num_batches_tracked":
												_UpperCAmelCase      =			value
						else:
												_UpperCAmelCase      =			value

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						for key in ignore_keys:
												if key.endswith(""".*"""       ):
																		if name.startswith(key[:-1]       ):
																								return True
												elif ".*." in key:
																		_UpperCAmelCase    ,					_UpperCAmelCase      =			key.split(""".*."""       )
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[int]:
						_UpperCAmelCase      =			[]

						if task == "s2t":
												_UpperCAmelCase      =			hf_model.speechta.encoder.prenet.feature_encoder
												_UpperCAmelCase      =			MAPPING_S2T
												_UpperCAmelCase      =			IGNORE_KEYS_S2T
						elif task == "t2s":
												_UpperCAmelCase      =			None
												_UpperCAmelCase      =			MAPPING_T2S
												_UpperCAmelCase      =			IGNORE_KEYS_T2S
						elif task == "s2s":
												_UpperCAmelCase      =			hf_model.speechta.encoder.prenet.feature_encoder
												_UpperCAmelCase      =			MAPPING_S2S
												_UpperCAmelCase      =			IGNORE_KEYS_S2S
						else:
												raise ValueError(f"Unsupported task: {task}"       )

						for name, value in fairseq_dict.items():
												if should_ignore(snake_case			,     snake_case       ):
																		logger.info(f"{name} was ignored"       )
																		continue

												_UpperCAmelCase      =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case			,     snake_case			,     snake_case			,     snake_case			,     hf_model.config.feat_extract_norm == """group"""			,     )
																		_UpperCAmelCase      =			True
												else:
																		for key, mapped_key in MAPPING.items():
																								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																								if "*" in key:
																														_UpperCAmelCase    ,					_UpperCAmelCase      =			key.split(""".*."""       )
																														if prefix in name and suffix in name:
																																				_UpperCAmelCase      =			suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																								if key in name:
																														_UpperCAmelCase      =			True
																														if "*" in mapped_key:
																																				_UpperCAmelCase      =			name.split(snake_case       )[0].split("""."""       )[-2]
																																				_UpperCAmelCase      =			mapped_key.replace("""*"""			,     snake_case       )
																														if "weight_g" in name:
																																				_UpperCAmelCase      =			"""weight_g"""
																														elif "weight_v" in name:
																																				_UpperCAmelCase      =			"""weight_v"""
																														elif "bias" in name:
																																				_UpperCAmelCase      =			"""bias"""
																														elif "weight" in name:
																																				_UpperCAmelCase      =			"""weight"""
																														elif "running_mean" in name:
																																				_UpperCAmelCase      =			"""running_mean"""
																														elif "running_var" in name:
																																				_UpperCAmelCase      =			"""running_var"""
																														elif "num_batches_tracked" in name:
																																				_UpperCAmelCase      =			"""num_batches_tracked"""
																														else:
																																				_UpperCAmelCase      =			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}"       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			full_name.split("""conv_layers."""       )[-1]
						_UpperCAmelCase      =			name.split("""."""       )
						_UpperCAmelCase      =			int(items[0]       )
						_UpperCAmelCase      =			int(items[1]       )

						if type_id == 0:
												if "bias" in name:
																		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."       )
																		_UpperCAmelCase      =			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."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		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."       )
																		_UpperCAmelCase      =			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."       )
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
						else:
												unused_weights.append(snake_case       )




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     snake_case=None			,     )						->				Union[str, Any]:
						if config_path is not None:
												_UpperCAmelCase      =			SpeechTaConfig.from_pretrained(snake_case       )
						else:
												_UpperCAmelCase      =			SpeechTaConfig()

						if task == "s2t":
												_UpperCAmelCase      =			config.max_text_positions
												_UpperCAmelCase      =			SpeechTaForSpeechToText(snake_case       )
						elif task == "t2s":
												_UpperCAmelCase      =			1_8_7_6
												_UpperCAmelCase      =			6_0_0
												_UpperCAmelCase      =			config.max_speech_positions
												_UpperCAmelCase      =			SpeechTaForTextToSpeech(snake_case       )
						elif task == "s2s":
												_UpperCAmelCase      =			1_8_7_6
												_UpperCAmelCase      =			config.max_speech_positions
												_UpperCAmelCase      =			SpeechTaForSpeechToSpeech(snake_case       )
						else:
												raise ValueError(f"Unknown task name: {task}"       )

						if vocab_path:
												_UpperCAmelCase      =			SpeechTaTokenizer(snake_case			,     model_max_length=config.max_text_positions       )

												# Mask token behaves like a normal word, i.e. include the space before it
												_UpperCAmelCase      =			AddedToken("""<mask>"""			,     lstrip=snake_case			,     rstrip=snake_case       )
												_UpperCAmelCase      =			mask_token
												tokenizer.add_special_tokens({"""mask_token""": mask_token}       )
												tokenizer.add_tokens(["""<ctc_blank>"""]       )

						_UpperCAmelCase      =			SpeechTaFeatureExtractor()
						_UpperCAmelCase      =			SpeechTaProcessor(tokenizer=snake_case			,     feature_extractor=snake_case       )
						processor.save_pretrained(snake_case       )

						_UpperCAmelCase      =			torch.load(snake_case       )
						recursively_load_weights(fairseq_checkpoint["""model"""]			,     snake_case			,     snake_case       )

						model.save_pretrained(snake_case       )

						if repo_id:
												print("""Pushing to the hub..."""       )
												processor.push_to_hub(snake_case       )
												model.push_to_hub(snake_case       )


if __name__ == "__main__":
				a													=			argparse.ArgumentParser()
				parser.add_argument(
				    "--task",
				    default="s2t",
				    type=str,
				    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
				)
				parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				parser.add_argument(
				    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
				)

				a													=			parser.parse_args()
				convert_speechta_checkpoint(
				    args.task,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.vocab_path,
				    args.push_to_hub,
				)