



"""simple docstring"""







import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
			from transformers import (
			    FlaxBertModel,
			    FlaxCLIPVisionModel,
			    FlaxVisionTextDualEncoderModel,
			    FlaxViTModel,
			    VisionTextDualEncoderConfig,
			    VisionTextDualEncoderProcessor,
			)
			from transformers.modeling_flax_pytorch_utils import (
			    convert_pytorch_state_dict_to_flax,
			    load_flax_weights_in_pytorch_model,
			)


if is_torch_available():
			import torch

			from transformers import VisionTextDualEncoderModel

if is_vision_available():
			from PIL import Image

def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> Dict:






	"""simple docstring"""





	if isinstance(UpperCAmelCase_,	collections.abc.Iterable     ):
		return x
	return (x, x)






@require_flax
class       UpperCamelCase__			:

			"""simple docstring"""


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				pass


			def   snake_case__	(      self		)      ->	Tuple:
				pass


			def   snake_case__	(      self		)      ->	Optional[int]:
				pass


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	str:
				A__											=					np.abs((a - b)		).max()
				self.assertLessEqual(a_						,	a_						,	f"""Difference between torch and flax is {diff} (>= {tol})."""		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				A__											=					VisionTextDualEncoderConfig.from_vision_text_configs(a_						,	a_		)

				A__											=					FlaxVisionTextDualEncoderModel(a_		)

				A__											=					model(input_ids=a_						,	pixel_values=a_						,	attention_mask=a_		)

				self.assertEqual(output["text_embeds"].shape						,	(input_ids.shape[0], config.projection_dim)		)
				self.assertEqual(output["image_embeds"].shape						,	(pixel_values.shape[0], config.projection_dim)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[Any]:
				A__											=					self.get_vision_text_model(a_						,	a_		)
				A__											=					{"vision_model": vision_model, "text_model": text_model}
				A__											=					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a_		)

				A__											=					model(input_ids=a_						,	pixel_values=a_						,	attention_mask=a_		)

				self.assertEqual(output["text_embeds"].shape						,	(input_ids.shape[0], model.config.projection_dim)		)
				self.assertEqual(output["image_embeds"].shape						,	(pixel_values.shape[0], model.config.projection_dim)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					self.get_vision_text_model(a_						,	a_		)
				A__											=					{"vision_model": vision_model, "text_model": text_model}
				A__											=					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a_		)

				A__											=					model(input_ids=a_						,	pixel_values=a_						,	attention_mask=a_		)
				A__											=					output[0]

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(a_		)
					A__											=					FlaxVisionTextDualEncoderModel.from_pretrained(a_		)

					A__											=					model(input_ids=a_						,	pixel_values=a_						,	attention_mask=a_		)
					A__											=					after_output[0]
					A__											=					np.amax(np.abs(out_a - out_a		)		)
					self.assertLessEqual(a_						,	1e-3		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					self.get_vision_text_model(a_						,	a_		)
				A__											=					{"vision_model": vision_model, "text_model": text_model}
				A__											=					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a_		)

				A__											=					model(
				    input_ids=a_						,	pixel_values=a_						,	attention_mask=a_						,	output_attentions=a_		)

				A__											=					output.vision_model_output.attentions
				self.assertEqual(len(a_		)						,	vision_config.num_hidden_layers		)

				# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
				A__											=					to_atuple(vision_model.config.image_size		)
				A__											=					to_atuple(vision_model.config.patch_size		)
				A__											=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
				A__											=					num_patches + 1
				self.assertEqual(vision_attentions[0].shape[-3:]						,	(vision_config.num_attention_heads, seq_len, seq_len)		)

				A__											=					output.text_model_output.attentions
				self.assertEqual(len(a_		)						,	text_config.num_hidden_layers		)

				self.assertEqual(
				    text_attentions[0].shape[-3:]						,	(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])						,	)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				pt_model.to(a_		)
				pt_model.eval()

				# prepare inputs
				A__											=					inputs_dict
				A__											=					{k: torch.tensor(v.tolist()		) for k, v in flax_inputs.items()}

				with torch.no_grad():
					A__											=					pt_model(**a_		).to_tuple()

				A__											=					fx_model(**a_		).to_tuple()
				self.assertEqual(len(a_		)						,	len(a_		)						,	"Output lengths differ between Flax and PyTorch"		)
				for fx_output, pt_output in zip(fx_outputs[:4]						,	pt_outputs[:4]		):
					self.assert_almost_equals(a_						,	pt_output.numpy()						,	4e-2		)

				# PT -> Flax
				with tempfile.TemporaryDirectory() as tmpdirname:
					pt_model.save_pretrained(a_		)
					A__											=					FlaxVisionTextDualEncoderModel.from_pretrained(a_						,	from_pt=a_		)

				A__											=					fx_model_loaded(**a_		).to_tuple()
				self.assertEqual(len(a_		)						,	len(a_		)						,	"Output lengths differ between Flax and PyTorch"		)
				for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]						,	pt_outputs[:4]		):
					self.assert_almost_equals(a_						,	pt_output.numpy()						,	4e-2		)

				# Flax -> PT
				with tempfile.TemporaryDirectory() as tmpdirname:
					fx_model.save_pretrained(a_		)
					A__											=					VisionTextDualEncoderModel.from_pretrained(a_						,	from_flax=a_		)

				pt_model_loaded.to(a_		)
				pt_model_loaded.eval()

				with torch.no_grad():
					A__											=					pt_model_loaded(**a_		).to_tuple()

				self.assertEqual(len(a_		)						,	len(a_		)						,	"Output lengths differ between Flax and PyTorch"		)
				for fx_output, pt_output_loaded in zip(fx_outputs[:4]						,	pt_outputs_loaded[:4]		):
					self.assert_almost_equals(a_						,	pt_output_loaded.numpy()						,	4e-2		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				A__											=					VisionTextDualEncoderConfig.from_vision_text_configs(a_						,	a_		)

				A__											=					VisionTextDualEncoderModel(a_		)
				A__											=					FlaxVisionTextDualEncoderModel(a_		)

				A__											=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,	a_		)
				A__											=					fx_state

				self.check_pt_flax_equivalence(a_						,	a_						,	a_		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				A__											=					VisionTextDualEncoderConfig.from_vision_text_configs(a_						,	a_		)

				A__											=					VisionTextDualEncoderModel(a_		)
				A__											=					FlaxVisionTextDualEncoderModel(a_		)

				A__											=					load_flax_weights_in_pytorch_model(a_						,	fx_model.params		)

				self.check_pt_flax_equivalence(a_						,	a_						,	a_		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.prepare_config_and_inputs()
				self.check_model_from_pretrained_configs(**a_		)


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.prepare_config_and_inputs()
				self.check_vision_text_dual_encoder_from_pretrained(**a_		)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.prepare_config_and_inputs()
				self.check_save_load(**a_		)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.prepare_config_and_inputs()
				self.check_vision_text_output_attention(**a_		)


			@is_pt_flax_cross_test
			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self.prepare_config_and_inputs()
				A__											=					config_inputs_dict.pop("vision_config"		)
				A__											=					config_inputs_dict.pop("text_config"		)

				A__											=					config_inputs_dict

				self.check_equivalence_pt_to_flax(a_						,	a_						,	a_		)
				self.check_equivalence_flax_to_pt(a_						,	a_						,	a_		)







			@slow
			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_pretrained_model_and_inputs()

				A__											=					model_a(**a_		)
				A__											=					outputs[0]

				with tempfile.TemporaryDirectory() as tmp_dirname:
					model_a.save_pretrained(a_		)
					A__											=					FlaxVisionTextDualEncoderModel.from_pretrained(a_		)

					A__											=					model_a(**a_		)
					A__											=					after_outputs[0]
					A__											=					np.amax(np.abs(out_a - out_a		)		)
					self.assertLessEqual(a_						,	1e-5		)






@require_flax
class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
				    "hf-internal-testing/tiny-random-vit"						,	"hf-internal-testing/tiny-bert"						,	vision_from_pt=a_						,	text_from_pt=a_						,	)
				A__											=					13
				A__											=					floats_tensor(
				    [
				        batch_size,
				        model.config.vision_config.num_channels,
				        model.config.vision_config.image_size,
				        model.config.vision_config.image_size,
				    ]		)
				A__											=					ids_tensor([batch_size, 4]						,	model.config.text_config.vocab_size		)
				A__											=					random_attention_mask([batch_size, 4]		)
				A__											=					{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

				return model, inputs


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					FlaxViTModel(a_		)
				A__											=					FlaxBertModel(a_		)
				return vision_model, text_model







			def   snake_case__	(      self		)      ->	Dict:
				A__											=					FlaxViTModelTester(self		)
				A__											=					FlaxBertModelTester(self		)
				A__											=					vit_model_tester.prepare_config_and_inputs()
				A__											=					bert_model_tester.prepare_config_and_inputs()

				A__											=					vision_config_and_inputs

				A__											=					text_config_and_inputs

				# make sure that cross attention layers are added
				return {
				    "text_config": text_config,
				    "vision_config": vision_config,
				    "pixel_values": pixel_values,
				    "attention_mask": attention_mask,
				    "input_ids": input_ids,
				    "token_type_ids": token_type_ids,
				}






@require_torch
class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
				    "hf-internal-testing/tiny-random-clip"						,	"hf-internal-testing/tiny-bert"						,	vision_from_pt=a_						,	text_from_pt=a_						,	)
				A__											=					13
				A__											=					floats_tensor(
				    [
				        batch_size,
				        model.config.vision_config.num_channels,
				        model.config.vision_config.image_size,
				        model.config.vision_config.image_size,
				    ]		)
				A__											=					ids_tensor([batch_size, 4]						,	model.config.text_config.vocab_size		)
				A__											=					random_attention_mask([batch_size, 4]		)
				A__											=					{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

				return model, inputs


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					FlaxCLIPVisionModel(a_		)
				A__											=					FlaxBertModel(a_		)
				return vision_model, text_model







			def   snake_case__	(      self		)      ->	Optional[Any]:
				A__											=					FlaxCLIPVisionModelTester(self		)
				A__											=					FlaxBertModelTester(self		)
				A__											=					clip_model_tester.prepare_config_and_inputs()
				A__											=					bert_model_tester.prepare_config_and_inputs()

				A__											=					vision_config_and_inputs

				A__											=					text_config_and_inputs

				# make sure that cross attention layers are added
				return {
				    "text_config": text_config,
				    "vision_config": vision_config,
				    "pixel_values": pixel_values,
				    "attention_mask": attention_mask,
				    "input_ids": input_ids,
				    "token_type_ids": token_type_ids,
				}






@require_flax
@require_vision
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			@slow
			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"						,	logit_scale_init_value=1.0		)
				A__											=					VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"		)

				A__											=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				A__											=					processor(
				    text=["una foto di un gatto", "una foto di un cane"]						,	images=a_						,	padding=a_						,	return_tensors="np"		)

				A__											=					model(**a_		)

				# verify the logits
				self.assertEqual(outputs.logits_per_image.shape						,	(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])		)
				self.assertEqual(
				    outputs.logits_per_text.shape						,	(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])						,	)

				A__											=					np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]		)

				self.assertTrue(np.allclose(outputs.logits_per_image						,	a_						,	atol=1e-3		)		)








'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase   : int             =				logging.get_logger(__name__)

lowerCAmelCase   : Dict             =				{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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   : Optional[Any]             =				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def 	_A  (       A							,A							,A							,A							,A							)  ->				Tuple:
	for attribute in key.split("."							):
		lowercase						:		Dict   					=		getattr(A							,A							)

	if weight_type is not None:
		lowercase						:		List[str]   					=		getattr(A							,A							).shape
	else:
		lowercase						:		Any   					=		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":
		lowercase						:		List[Any]   					=		value
	elif weight_type == "weight_g":
		lowercase						:		List[Any]   					=		value
	elif weight_type == "weight_v":
		lowercase						:		int   					=		value
	elif weight_type == "bias":
		lowercase						:		Any   					=		value
	elif weight_type == "running_mean":
		lowercase						:		Tuple   					=		value
	elif weight_type == "running_var":
		lowercase						:		Dict   					=		value
	elif weight_type == "num_batches_tracked":
		lowercase						:		Optional[int]   					=		value
	elif weight_type == "inv_freq":
		lowercase						:		List[Any]   					=		value
	else:
		lowercase						:		Dict   					=		value

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




def 	_A  (       A							,A							,A							)  ->				int:
	lowercase						:		Optional[int]   					=		[]
	lowercase						:		Tuple   					=		fairseq_model.state_dict()

	lowercase						:		Optional[Any]   					=		hf_model.wavaveca_conformer.feature_extractor

	for name, value in fairseq_dict.items():
		lowercase						:		str   					=		False
		if "conv_layers" in name:
			load_conv_layer(
			    A							,A							,A							,A							,hf_model.config.feat_extract_norm == "group"							,)
			lowercase						:		Dict   					=		True
		else:
			for key, mapped_key in MAPPING.items():
				lowercase						:		List[str]   					=		"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split("w2v_model."							)[-1] == name.split("."							)[0]:
					lowercase						:		List[str]   					=		True
					if "*" in mapped_key:
						lowercase						:		int   					=		name.split(A							)[0].split("."							)[-2]
						lowercase						:		Optional[Any]   					=		mapped_key.replace("*"							,A							)
					if "pos_bias_u" in name:
						lowercase						:		str   					=		None
					elif "pos_bias_v" in name:
						lowercase						:		Optional[int]   					=		None
					elif "weight_g" in name:
						lowercase						:		int   					=		"weight_g"
					elif "weight_v" in name:
						lowercase						:		Dict   					=		"weight_v"
					elif "bias" in name:
						lowercase						:		List[str]   					=		"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						lowercase						:		Optional[int]   					=		"weight"
					elif "running_mean" in name:
						lowercase						:		Optional[int]   					=		"running_mean"
					elif "inv_freq" in name:
						lowercase						:		Dict   					=		"inv_freq"
					elif "running_var" in name:
						lowercase						:		int   					=		"running_var"
					elif "num_batches_tracked" in name:
						lowercase						:		Optional[Any]   					=		"num_batches_tracked"
					else:
						lowercase						:		int   					=		None
					set_recursively(A							,A							,A							,A							,A							)
				continue
		if not is_used:
			unused_weights.append(A							)

	logger.warning(F'''Unused weights: {unused_weights}'''							)




def 	_A  (       A							,A							,A							,A							,A							)  ->				List[str]:
	lowercase						:		Tuple   					=		full_name.split("conv_layers."							)[-1]
	lowercase						:		Optional[Any]   					=		name.split("."							)
	lowercase						:		str   					=		int(items[0]							)
	lowercase						:		List[Any]   					=		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.'''							)
			lowercase						:		Union[str, Any]   					=		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.'''							)
			lowercase						:		str   					=		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.'''							)
			lowercase						:		int   					=		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.'''							)
			lowercase						:		str   					=		value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
	else:
		unused_weights.append(A							)




@torch.no_grad()
def 	_A  (       A							,A							,A=None							,A=None							,A=True							)  ->				Optional[Any]:
	if config_path is not None:
		lowercase						:		Tuple   					=		WavaVecaConformerConfig.from_pretrained(A							,hidden_act="swish"							)
	else:
		lowercase						:		Any   					=		WavaVecaConformerConfig()

	if "rope" in checkpoint_path:
		lowercase						:		str   					=		"rotary"

	if is_finetuned:
		if dict_path:
			lowercase						:		List[str]   					=		Dictionary.load(A							)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			lowercase						:		Optional[int]   					=		target_dict.pad_index
			lowercase						:		Optional[int]   					=		target_dict.bos_index
			lowercase						:		Optional[Any]   					=		target_dict.eos_index
			lowercase						:		str   					=		len(target_dict.symbols							)
			lowercase						:		List[Any]   					=		os.path.join(A							,"vocab.json"							)
			if not os.path.isdir(A							):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A							)							)
				return
			os.makedirs(A							,exist_ok=A							)
			lowercase						:		Any   					=		target_dict.indices

			# fairseq has the <pad> and <s> switched
			lowercase						:		Any   					=		0
			lowercase						:		Tuple   					=		1
			with open(A							,"w"							,encoding="utf-8"							) as vocab_handle:
				json.dump(A							,A							)
			lowercase						:		Tuple   					=		WavaVecaCTCTokenizer(
			    A							,unk_token=target_dict.unk_word							,pad_token=target_dict.pad_word							,bos_token=target_dict.bos_word							,eos_token=target_dict.eos_word							,word_delimiter_token="|"							,do_lower_case=A							,)
			lowercase						:		Dict   					=		True if config.feat_extract_norm == "layer" else False
			lowercase						:		List[str]   					=		WavaVecaFeatureExtractor(
			    feature_size=1							,sampling_rate=1_6_0_0_0							,padding_value=0							,do_normalize=A							,return_attention_mask=A							,)
			lowercase						:		List[str]   					=		WavaVecaProcessor(feature_extractor=A							,tokenizer=A							)
			processor.save_pretrained(A							)

		lowercase						:		Any   					=		WavaVecaConformerForCTC(A							)
	else:
		lowercase						:		str   					=		WavaVecaConformerForPreTraining(A							)

	if is_finetuned:
		lowercase					,		lowercase					,		lowercase						:		List[str]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]							,arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
	else:
		lowercase						:		List[str]   					=		argparse.Namespace(task="audio_pretraining"							)
		lowercase						:		Union[str, Any]   					=		fairseq.tasks.setup_task(A							)

		lowercase					,		lowercase					,		lowercase						:		Optional[Any]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,task=A							)

	lowercase						:		List[str]   					=		model[0].eval()

	recursively_load_weights(A							,A							,not is_finetuned							)

	hf_wavavec.save_pretrained(A							)


if __name__ == "__main__":
					lowerCAmelCase   : str             =				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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					lowerCAmelCase   : int             =				parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)


