


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      UnCLIPImageVariationPipeline
     _lowerCamelCase   =      IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
     _lowerCamelCase   =      IMAGE_VARIATION_BATCH_PARAMS

     _lowerCamelCase   =      [
         """generator""",
         """return_dict""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     _lowerCamelCase   =      False


     @property
     def 	snake_case_  (	self    ):
        return 32


     @property
     def 	snake_case_  (	self    ):
        return 32


     @property
     def 	snake_case_  (	self    ):
        return self.time_input_dim


     @property
     def 	snake_case_  (	self    ):
        return self.time_input_dim * 4


     @property
     def 	snake_case_  (	self    ):
        return 100


     @property
     def 	snake_case_  (	self    ):
        __a					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )
        return tokenizer


     @property
     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  CLIPTextConfig(
            bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=self.text_embedder_hidden_size  ,       projection_dim=self.text_embedder_hidden_size  ,       intermediate_size=37  ,       layer_norm_eps=1E-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=1000  ,       )
        return CLIPTextModelWithProjection(__A    )


     @property
     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size  ,       projection_dim=self.text_embedder_hidden_size  ,       num_hidden_layers=5  ,       num_attention_heads=4  ,       image_size=32  ,       intermediate_size=37  ,       patch_size=1  ,       )
        return CLIPVisionModelWithProjection(__A    )


     @property
     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )

        __a					=  {
            """clip_embeddings_dim""": self.text_embedder_hidden_size,
            """time_embed_dim""": self.time_embed_dim,
            """cross_attention_dim""": self.cross_attention_dim,
        }

        __a					=  UnCLIPTextProjModel(**__A    )
        return model


     @property
     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )

        __a					=  {
            """sample_size""": 32,
            # RGB in channels
            """in_channels""": 3,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 6,
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": """identity""",
        }

        __a					=  UNetaDConditionModel(**__A    )
        return model


     @property
     def 	snake_case_  (	self    ):
        return {
            "sample_size": 64,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }


     @property
     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )

        __a					=  UNetaDModel(**self.dummy_super_res_kwargs    )
        return model


     @property
     def 	snake_case_  (	self    ):
        # seeded differently to get different unet than `self.dummy_super_res_first`
        torch.manual_seed(1    )

        __a					=  UNetaDModel(**self.dummy_super_res_kwargs    )
        return model


     def 	snake_case_  (	self    ):
        __a					=  self.dummy_decoder
        __a					=  self.dummy_text_proj
        __a					=  self.dummy_text_encoder
        __a					=  self.dummy_tokenizer
        __a					=  self.dummy_super_res_first
        __a					=  self.dummy_super_res_last

        __a					=  UnCLIPScheduler(
            variance_type="""learned_range"""  ,       prediction_type="""epsilon"""  ,       num_train_timesteps=1000  ,       )

        __a					=  UnCLIPScheduler(
            variance_type="""fixed_small_log"""  ,       prediction_type="""epsilon"""  ,       num_train_timesteps=1000  ,       )

        __a					=  CLIPImageProcessor(crop_size=32  ,       size=32    )

        __a					=  self.dummy_image_encoder

        return {
            "decoder": decoder,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_proj": text_proj,
            "feature_extractor": feature_extractor,
            "image_encoder": image_encoder,
            "super_res_first": super_res_first,
            "super_res_last": super_res_last,
            "decoder_scheduler": decoder_scheduler,
            "super_res_scheduler": super_res_scheduler,
        }


     def 	snake_case_  (	self  ,       __A  ,       __A=0  ,       __A=True    ):
        __a					=  floats_tensor((1, 3, 32, 32)  ,       rng=random.Random(__A    )    ).to(__A    )
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )

        if pil_image:
           __a					=  input_image * 0.5 + 0.5
           __a					=  input_image.clamp(0  ,       1    )
           __a					=  input_image.cpu().permute(0  ,       2  ,       3  ,       1    ).float().numpy()
           __a					=  DiffusionPipeline.numpy_to_pil(__A    )[0]

        return {
            "image": input_image,
            "generator": generator,
            "decoder_num_inference_steps": 2,
            "super_res_num_inference_steps": 2,
            "output_type": "np",
        }


     def 	snake_case_  (	self    ):
        __a					=  """cpu"""

        __a					=  self.get_dummy_components()

        __a					=  self.pipeline_class(**__A    )
        __a					=  pipe.to(__A    )

        pipe.set_progress_bar_config(disable=__A    )

        __a					=  self.get_dummy_inputs(__A  ,       pil_image=__A    )

        __a					=  pipe(**__A    )
        __a					=  output.images

        __a					=  self.get_dummy_inputs(__A  ,       pil_image=__A    )

        __a					=  pipe(
            **__A  ,       return_dict=__A  ,       )[0]

        __a					=  image[0, -3:, -3:, -1]
        __a					=  image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        __a					=  np.array(
            [
                0.9997,
                0.0002,
                0.9997,
                0.9997,
                0.9969,
                0.0023,
                0.9997,
                0.9969,
                0.9970,
            ]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2


     def 	snake_case_  (	self    ):
        __a					=  """cpu"""

        __a					=  self.get_dummy_components()

        __a					=  self.pipeline_class(**__A    )
        __a					=  pipe.to(__A    )

        pipe.set_progress_bar_config(disable=__A    )

        __a					=  self.get_dummy_inputs(__A  ,       pil_image=__A    )

        __a					=  pipe(**__A    )
        __a					=  output.images

        __a					=  self.get_dummy_inputs(__A  ,       pil_image=__A    )

        __a					=  pipe(
            **__A  ,       return_dict=__A  ,       )[0]

        __a					=  image[0, -3:, -3:, -1]
        __a					=  image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        __a					=  np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2


     def 	snake_case_  (	self    ):
        __a					=  """cpu"""

        __a					=  self.get_dummy_components()

        __a					=  self.pipeline_class(**__A    )
        __a					=  pipe.to(__A    )

        pipe.set_progress_bar_config(disable=__A    )

        __a					=  self.get_dummy_inputs(__A  ,       pil_image=__A    )
        __a					=  [
            pipeline_inputs["""image"""],
            pipeline_inputs["""image"""],
        ]

        __a					=  pipe(**__A    )
        __a					=  output.images

        __a					=  self.get_dummy_inputs(__A  ,       pil_image=__A    )
        __a					=  [
            tuple_pipeline_inputs["""image"""],
            tuple_pipeline_inputs["""image"""],
        ]

        __a					=  pipe(
            **__A  ,       return_dict=__A  ,       )[0]

        __a					=  image[0, -3:, -3:, -1]
        __a					=  image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)

        __a					=  np.array(
            [
                0.9997,
                0.9989,
                0.0008,
                0.0021,
                0.9960,
                0.0018,
                0.0014,
                0.0002,
                0.9933,
            ]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2


     def 	snake_case_  (	self    ):
        __a					=  torch.device("""cpu"""    )


        class     __UpperCAmelCase							:


             """simple docstring"""
             _lowerCamelCase   =      1







        __a					=  self.get_dummy_components()

        __a					=  self.pipeline_class(**__A    )
        __a					=  pipe.to(__A    )

        pipe.set_progress_bar_config(disable=__A    )

        __a					=  torch.Generator(device=__A    ).manual_seed(0    )
        __a					=  pipe.decoder.dtype
        __a					=  1

        __a					=  (
            batch_size,
            pipe.decoder.config.in_channels,
            pipe.decoder.config.sample_size,
            pipe.decoder.config.sample_size,
        )
        __a					=  pipe.prepare_latents(
            __A  ,       dtype=__A  ,       device=__A  ,       generator=__A  ,       latents=__A  ,       scheduler=DummyScheduler()    )

        __a					=  (
            batch_size,
            pipe.super_res_first.config.in_channels // 2,
            pipe.super_res_first.config.sample_size,
            pipe.super_res_first.config.sample_size,
        )
        __a					=  pipe.prepare_latents(
            __A  ,       dtype=__A  ,       device=__A  ,       generator=__A  ,       latents=__A  ,       scheduler=DummyScheduler()    )

        __a					=  self.get_dummy_inputs(__A  ,       pil_image=__A    )

        __a					=  pipe(
            **__A  ,       decoder_latents=__A  ,       super_res_latents=__A    ).images

        __a					=  self.get_dummy_inputs(__A  ,       pil_image=__A    )
        # Don't pass image, instead pass embedding
        __a					=  pipeline_inputs.pop("""image"""    )
        __a					=  pipe.image_encoder(__A    ).image_embeds

        __a					=  pipe(
            **__A  ,       decoder_latents=__A  ,       super_res_latents=__A  ,       image_embeddings=__A  ,       ).images

        # make sure passing text embeddings manually is identical
        assert np.abs(img_out_a - img_out_a    ).max() < 1E-4


     @skip_mps
     def 	snake_case_  (	self    ):
        __a					=  torch_device == """cpu"""

        # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
        __a					=  1E-2

        self._test_attention_slicing_forward_pass(
            test_max_difference=__A  ,       expected_max_diff=__A    )


     @skip_mps
     def 	snake_case_  (	self    ):
        __a					=  torch_device == """cpu"""
        __a					=  True
        __a					=  [
            """decoder_num_inference_steps""",
            """super_res_num_inference_steps""",
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=__A  ,       relax_max_difference=__A  ,       additional_params_copy_to_batched_inputs=__A  ,       )


     def 	snake_case_  (	self    ):
        __a					=  [
            """decoder_num_inference_steps""",
            """super_res_num_inference_steps""",
        ]

        if torch_device == "mps":
           # TODO: MPS errors with larger batch sizes
           __a					=  [2, 3]
           self._test_inference_batch_consistent(
               batch_sizes=__A  ,       additional_params_copy_to_batched_inputs=__A  ,       )
        else:
           self._test_inference_batch_consistent(
               additional_params_copy_to_batched_inputs=__A    )


     @skip_mps
     def 	snake_case_  (	self    ):
        return super().test_dict_tuple_outputs_equivalent()


     @skip_mps
     def 	snake_case_  (	self    ):
        return super().test_save_load_local()


     @skip_mps
     def 	snake_case_  (	self    ):
        return super().test_save_load_optional_components()







@slow
@require_torch_gpu
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


     def 	snake_case_  (	self    ):
        __a					=  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""    )
        __a					=  load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""    )

        __a					=  UnCLIPImageVariationPipeline.from_pretrained(
            """kakaobrain/karlo-v1-alpha-image-variations"""  ,       torch_dtype=torch.floataa    )
        __a					=  pipeline.to(__A    )
        pipeline.set_progress_bar_config(disable=__A    )

        __a					=  torch.Generator(device="""cpu"""    ).manual_seed(0    )
        __a					=  pipeline(
            __A  ,       generator=__A  ,       output_type="""np"""  ,       )

        __a					=  output.images[0]

        assert image.shape == (256, 256, 3)

        assert_mean_pixel_difference(__A  ,       __A  ,       15    )








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()
_lowerCAmelCase	:   str    =   logging.get_logger(__name__)

_lowerCAmelCase	:   str    =   {
    "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",
}
_lowerCAmelCase	:   List[str]    =   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     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     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     List[Any]:



				"""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     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     List[Any]:



				"""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     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""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     UpperCAmelCase_		(					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     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			)	->     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__":
	_lowerCAmelCase	:   int    =   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=1_0_2_2_4, type=int, help="Vocab size of decoder")
	parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

	_lowerCAmelCase	:   Optional[int]    =   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,
	)
