



from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
  from transformers import (
      TFBertModel,
      TFCLIPVisionModel,
      TFDeiTModel,
      TFRobertaModel,
      TFVisionTextDualEncoderModel,
      TFViTModel,
      VisionTextDualEncoderConfig,
  )

if is_vision_available():
  from PIL import Image

  from transformers import VisionTextDualEncoderProcessor



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Optional[int]:
 if isinstance(SCREAMING_SNAKE_CASE__				,       collections.abc.Iterable				):
  return x
 return (x, x)







@require_tf
class 		snake_case						:



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			List[str]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			List[str])				->				Any:

    """simple docstring"""







    pass

   def      UpperCamelCase_  (					self							:			Dict)				->				Optional[Any]:

    """simple docstring"""







    pass

   def      UpperCamelCase_  (					self							:			str)				->				Union[str, Any]:

    """simple docstring"""







    pass

   def      UpperCamelCase_  (					self							:			Dict				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			str=None				,		**lowerCAmelCase							:			List[Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase				,		lowerCAmelCase)

    _snake_case    :  Optional[int]					=					TFVisionTextDualEncoderModel(lowerCAmelCase)

    _snake_case    :  Dict					=					model(input_ids=lowerCAmelCase				,		pixel_values=lowerCAmelCase				,		attention_mask=lowerCAmelCase)

    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      UpperCamelCase_  (					self							:			List[str]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Any=None				,		**lowerCAmelCase							:			Any)				->				Any:

    """simple docstring"""







    _snake_case	,    _snake_case    :  Dict					=					self.get_vision_text_model(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase				,		text_model=lowerCAmelCase)

    _snake_case    :  List[str]					=					model(input_ids=lowerCAmelCase				,		pixel_values=lowerCAmelCase				,		attention_mask=lowerCAmelCase)

    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      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			List[Any]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			List[Any]				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			List[Any])				->				Tuple:

    """simple docstring"""







    _snake_case	,    _snake_case    :  List[Any]					=					self.get_vision_text_model(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					{"""vision_model""": vision_model, """text_model""": text_model}
    _snake_case    :  str					=					TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase)

    _snake_case    :  List[Any]					=					model(input_ids=lowerCAmelCase				,		pixel_values=lowerCAmelCase				,		attention_mask=lowerCAmelCase)

    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      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			List[Any]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Union[str, Any]=None				,		**lowerCAmelCase							:			int)				->				Tuple:

    """simple docstring"""







    _snake_case	,    _snake_case    :  int					=					self.get_vision_text_model(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase				,		text_model=lowerCAmelCase)

    _snake_case    :  str					=					model(input_ids=lowerCAmelCase				,		pixel_values=lowerCAmelCase				,		attention_mask=lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					output[0].numpy()

    with tempfile.TemporaryDirectory() as tmpdirname:
     model.save_pretrained(lowerCAmelCase)
     _snake_case    :  Optional[Any]					=					TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase)

     _snake_case    :  Tuple					=					model(input_ids=lowerCAmelCase				,		pixel_values=lowerCAmelCase				,		attention_mask=lowerCAmelCase)
     _snake_case    :  Optional[int]					=					after_output[0].numpy()
     _snake_case    :  List[Any]					=					np.amax(np.abs(out_a - out_a))
     self.assertLessEqual(lowerCAmelCase				,		1E-5)

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Optional[int]=None				,		**lowerCAmelCase							:			Dict)				->				Any:

    """simple docstring"""







    _snake_case	,    _snake_case    :  List[str]					=					self.get_vision_text_model(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Any					=					TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase				,		text_model=lowerCAmelCase)

    _snake_case    :  Tuple					=					model(
        input_ids=lowerCAmelCase				,		pixel_values=lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		output_attentions=lowerCAmelCase)

    _snake_case    :  List[Any]					=					output.vision_model_output.attentions
    self.assertEqual(len(lowerCAmelCase)				,		vision_config.num_hidden_layers)

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

    _snake_case    :  int					=					output.text_model_output.attentions
    self.assertEqual(len(lowerCAmelCase)				,		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      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			np.ndarray				,		lowerCAmelCase							:			float)				->				Dict:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					np.abs((a - b)).max()
    self.assertLessEqual(lowerCAmelCase				,		lowerCAmelCase				,		F'''Difference between torch and flax is {diff} (>= {tol}).''')

   def      UpperCamelCase_  (					self							:			Any)				->				int:

    """simple docstring"""







    _snake_case    :  Any					=					self.prepare_config_and_inputs()
    self.check_vision_text_dual_encoder_model(**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					self.prepare_config_and_inputs()
    self.check_model_from_pretrained_configs(**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			str)				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  List[Any]					=					self.prepare_config_and_inputs()
    self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			str)				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					self.prepare_config_and_inputs()
    self.check_save_load(**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Tuple					=					self.prepare_config_and_inputs()
    self.check_vision_text_output_attention(**lowerCAmelCase)


   @slow
   def      UpperCamelCase_  (					self							:			str)				->				int:

    """simple docstring"""







    _snake_case	,    _snake_case    :  Dict					=					self.get_pretrained_model_and_inputs()

    _snake_case    :  Optional[Any]					=					model_a(**lowerCAmelCase)
    _snake_case    :  int					=					outputs[0].numpy()

    with tempfile.TemporaryDirectory() as tmp_dirname:
     model_a.save_pretrained(lowerCAmelCase)
     _snake_case    :  Union[str, Any]					=					TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase)

     _snake_case    :  Dict					=					model_a(**lowerCAmelCase)
     _snake_case    :  int					=					after_outputs[0].numpy()
     _snake_case    :  str					=					np.amax(np.abs(out_a - out_a))
     self.assertLessEqual(lowerCAmelCase				,		1E-5)







@require_tf
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			List[Any])				->				Tuple:

    """simple docstring"""







    _snake_case    :  List[Any]					=					TFVisionTextDualEncoderModel.from_vision_text_pretrained(
        """hf-internal-testing/tiny-random-vit"""				,		"""hf-internal-testing/tiny-random-bert""")
    _snake_case    :  Union[str, Any]					=					13
    _snake_case    :  int					=					floats_tensor(
        [
            batch_size,
            model.vision_model.config.num_channels,
            model.vision_model.config.image_size,
            model.vision_model.config.image_size,
        ])
    _snake_case    :  Any					=					ids_tensor([batch_size, 4]				,		model.text_model.config.vocab_size)
    _snake_case    :  Tuple					=					random_attention_mask([batch_size, 4])
    _snake_case    :  Tuple					=					{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

    return model, inputs

   def      UpperCamelCase_  (					self							:			List[str]				,		lowerCAmelCase							:			List[Any]				,		lowerCAmelCase							:			List[Any])				->				Dict:

    """simple docstring"""







    _snake_case    :  List[str]					=					TFViTModel(lowerCAmelCase				,		name="""vision_model""")
    _snake_case    :  List[Any]					=					TFBertModel(lowerCAmelCase				,		name="""text_model""")
    return vision_model, text_model


   def      UpperCamelCase_  (					self							:			List[str])				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					TFViTModelTester(self)
    _snake_case    :  Optional[Any]					=					TFBertModelTester(self)
    _snake_case    :  Optional[int]					=					vit_model_tester.prepare_config_and_inputs()
    _snake_case    :  int					=					bert_model_tester.prepare_config_and_inputs()

    _snake_case	,    _snake_case	,    _snake_case    :  Dict					=					vision_config_and_inputs

    (
        (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    
    )    :  List[str]					=					text_config_and_inputs

    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": input_mask,
        "input_ids": input_ids,
        "text_token_type_ids": token_type_ids,
        "text_sequence_labels": sequence_labels,
        "text_token_labels": token_labels,
        "text_choice_labels": choice_labels,
    }







@require_tf
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			str)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					TFVisionTextDualEncoderModel.from_vision_text_pretrained(
        """Rocketknight1/tiny-random-deit-tf"""				,		"""hf-internal-testing/tiny-random-roberta""")
    _snake_case    :  int					=					13
    _snake_case    :  List[str]					=					floats_tensor(
        [
            batch_size,
            model.vision_model.config.num_channels,
            model.vision_model.config.image_size,
            model.vision_model.config.image_size,
        ])
    _snake_case    :  Union[str, Any]					=					ids_tensor([batch_size, 4]				,		model.text_model.config.vocab_size)
    _snake_case    :  int					=					random_attention_mask([batch_size, 4])
    _snake_case    :  List[str]					=					{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

    return model, inputs

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			int=None				,		**lowerCAmelCase							:			Dict)				->				Optional[int]:

    """simple docstring"""







    _snake_case	,    _snake_case    :  Any					=					self.get_vision_text_model(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  int					=					TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase				,		text_model=lowerCAmelCase)

    _snake_case    :  Optional[Any]					=					model(
        input_ids=lowerCAmelCase				,		pixel_values=lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		output_attentions=lowerCAmelCase)

    _snake_case    :  Dict					=					output.vision_model_output.attentions
    self.assertEqual(len(lowerCAmelCase)				,		vision_config.num_hidden_layers)

    # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
    _snake_case    :  List[Any]					=					to_atuple(vision_model.config.image_size)
    _snake_case    :  str					=					to_atuple(vision_model.config.patch_size)
    _snake_case    :  str					=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    _snake_case    :  List[Any]					=					num_patches + 2
    self.assertEqual(vision_attentions[0].shape[-3:]				,		(vision_config.num_attention_heads, seq_len, seq_len))

    _snake_case    :  Dict					=					output.text_model_output.attentions
    self.assertEqual(len(lowerCAmelCase)				,		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      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Optional[Any])				->				Any:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					TFDeiTModel(lowerCAmelCase				,		name="""vision_model""")
    _snake_case    :  Optional[int]					=					TFRobertaModel(lowerCAmelCase				,		name="""text_model""")
    return vision_model, text_model


   def      UpperCamelCase_  (					self							:			List[str])				->				int:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					TFDeiTModelTester(self)
    _snake_case    :  Tuple					=					TFRobertaModelTester(self)
    _snake_case    :  Optional[int]					=					vit_model_tester.prepare_config_and_inputs()
    _snake_case    :  Optional[Any]					=					bert_model_tester.prepare_config_and_inputs()

    _snake_case	,    _snake_case	,    _snake_case    :  List[Any]					=					vision_config_and_inputs

    (
        (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    
    )    :  Dict					=					text_config_and_inputs

    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": input_mask,
        "input_ids": input_ids,
        "text_token_type_ids": token_type_ids,
        "text_sequence_labels": sequence_labels,
        "text_token_labels": token_labels,
        "text_choice_labels": choice_labels,
    }







@require_tf
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  str					=					TFVisionTextDualEncoderModel.from_vision_text_pretrained(
        """Rocketknight1/tiny-random-clip-tf"""				,		"""hf-internal-testing/tiny-random-bert""")
    _snake_case    :  str					=					13
    _snake_case    :  Dict					=					floats_tensor(
        [
            batch_size,
            model.vision_model.config.num_channels,
            model.vision_model.config.image_size,
            model.vision_model.config.image_size,
        ])
    _snake_case    :  Optional[int]					=					ids_tensor([batch_size, 4]				,		model.text_model.config.vocab_size)
    _snake_case    :  str					=					random_attention_mask([batch_size, 4])
    _snake_case    :  Optional[int]					=					{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

    return model, inputs

   def      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					TFCLIPVisionModel(lowerCAmelCase				,		name="""vision_model""")
    _snake_case    :  List[Any]					=					TFBertModel(lowerCAmelCase				,		name="""text_model""")
    return vision_model, text_model


   def      UpperCamelCase_  (					self							:			str)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					TFCLIPVisionModelTester(self)
    _snake_case    :  int					=					TFBertModelTester(self)
    _snake_case    :  Any					=					clip_model_tester.prepare_config_and_inputs()
    _snake_case    :  str					=					bert_model_tester.prepare_config_and_inputs()

    _snake_case	,    _snake_case    :  Optional[Any]					=					vision_config_and_inputs

    (
        (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    
    )    :  Union[str, Any]					=					text_config_and_inputs

    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": input_mask,
        "input_ids": input_ids,
        "text_token_type_ids": token_type_ids,
        "text_sequence_labels": sequence_labels,
        "text_token_labels": token_labels,
        "text_choice_labels": choice_labels,
    }







@require_vision
@require_tf
class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   @slow
   def      UpperCamelCase_  (					self							:			Dict)				->				str:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					TFVisionTextDualEncoderModel.from_pretrained(
        """clip-italian/clip-italian"""				,		logit_scale_init_value=1.0				,		from_pt=lowerCAmelCase)
    _snake_case    :  str					=					VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""")

    _snake_case    :  List[str]					=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
    _snake_case    :  List[Any]					=					processor(
        text=["""una foto di un gatto""", """una foto di un cane"""]				,		images=lowerCAmelCase				,		padding=lowerCAmelCase				,		return_tensors="""np""")

    _snake_case    :  Optional[int]					=					model(**lowerCAmelCase)

    # 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])				,		)

    _snake_case    :  int					=					np.array([[1.2_284_727, 0.3_104_122]])

    self.assertTrue(np.allclose(outputs.logits_per_image.numpy()				,		lowerCAmelCase				,		atol=1E-3))





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
a__    =	logging.get_logger(__name__)

a__    =	{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
a__    =	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				)  ->     Any:
 _snake_case    :  Union[str, Any]					=					{}
 with open(SCREAMING_SNAKE_CASE__				,       """r"""				) as file:
  for line_number, line in enumerate(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  Union[str, Any]					=					line.strip()
   if line:
    _snake_case    :  List[str]					=					line.split()
    _snake_case    :  List[Any]					=					line_number
    _snake_case    :  int					=					words[0]
    _snake_case    :  str					=					value
 return result



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Any				)  ->     Tuple:
 for attribute in key.split("""."""				):
  _snake_case    :  List[str]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  List[Any]					=					None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  Union[str, Any]					=					PARAM_MAPPING[full_name.split("""."""				)[-1]]
   _snake_case    :  Any					=					"""param"""

 if weight_type is not None and weight_type != "param":
  _snake_case    :  int					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				).shape
 elif weight_type is not None and weight_type == "param":
  _snake_case    :  Dict					=					hf_pointer
  for attribute in hf_param_name.split("""."""				):
   _snake_case    :  Tuple					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Any					=					shape_pointer.shape

  # let's reduce dimension
  _snake_case    :  str					=					value[0]
 else:
  _snake_case    :  List[str]					=					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":
  _snake_case    :  Dict					=					value
 elif weight_type == "weight_g":
  _snake_case    :  Union[str, Any]					=					value
 elif weight_type == "weight_v":
  _snake_case    :  str					=					value
 elif weight_type == "bias":
  _snake_case    :  Dict					=					value
 elif weight_type == "param":
  for attribute in hf_param_name.split("""."""				):
   _snake_case    :  Union[str, Any]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  _snake_case    :  int					=					value
 else:
  _snake_case    :  Dict					=					value

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     str:
 _snake_case    :  List[str]					=					None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  List[str]					=					PARAM_MAPPING[full_name.split("""."""				)[-1]]
   _snake_case    :  Optional[Any]					=					"""param"""

 if weight_type is not None and weight_type != "param":
  _snake_case    :  int					=					""".""".join([key, weight_type]				)
 elif weight_type is not None and weight_type == "param":
  _snake_case    :  List[str]					=					""".""".join([key, hf_param_name]				)
 else:
  _snake_case    :  Optional[int]					=					key

 _snake_case    :  Any					=					value if """lm_head""" in full_key else value[0]


a__    =	{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	int=None				,       SCREAMING_SNAKE_CASE__       :	Any=None				)  ->     Optional[Any]:
 _snake_case    :  Any					=					False
 for key, mapped_key in MAPPING.items():
  _snake_case    :  List[str]					=					"""wav2vec2.""" + 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]:
   _snake_case    :  Optional[int]					=					True
   if "*" in mapped_key:
    _snake_case    :  List[str]					=					name.split(SCREAMING_SNAKE_CASE__				)[0].split("""."""				)[-2]
    _snake_case    :  List[Any]					=					mapped_key.replace("""*"""				,       SCREAMING_SNAKE_CASE__				)
   if "weight_g" in name:
    _snake_case    :  Tuple					=					"""weight_g"""
   elif "weight_v" in name:
    _snake_case    :  Dict					=					"""weight_v"""
   elif "bias" in name:
    _snake_case    :  Optional[Any]					=					"""bias"""
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    _snake_case    :  List[Any]					=					"""weight"""
   else:
    _snake_case    :  Dict					=					None
   if hf_dict is not None:
    rename_dict(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   else:
    set_recursively(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   return is_used
 return is_used



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     Tuple:
 _snake_case    :  Optional[Any]					=					[]
 _snake_case    :  Tuple					=					fairseq_model.state_dict()

 _snake_case    :  List[str]					=					hf_model.wavaveca.feature_extractor

 for name, value in fairseq_dict.items():
  _snake_case    :  Tuple					=					False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       hf_model.config.feat_extract_norm == """group"""				,       )
   _snake_case    :  List[str]					=					True
  else:
   _snake_case    :  Dict					=					load_wavaveca_layer(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE__				)

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



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



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	List[str]=None				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=None				,       SCREAMING_SNAKE_CASE__       :	int=True				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]=False				)  ->     Optional[Any]:
 if config_path is not None:
  _snake_case    :  List[Any]					=					WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  Union[str, Any]					=					WavaVecaConfig()

 if is_seq_class:
  _snake_case    :  Union[str, Any]					=					read_txt_into_dict(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  str					=					idalabel
  _snake_case    :  Union[str, Any]					=					WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  List[str]					=					WavaVecaFeatureExtractor(
      feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=SCREAMING_SNAKE_CASE__				,       return_attention_mask=SCREAMING_SNAKE_CASE__				,       )
  feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__				)

 elif is_finetuned:
  if dict_path:
   _snake_case    :  Optional[Any]					=					Dictionary.load(SCREAMING_SNAKE_CASE__				)

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

   # fairseq has the <pad> and <s> switched
   _snake_case    :  Optional[int]					=					0
   _snake_case    :  Tuple					=					1
   with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as vocab_handle:
    json.dump(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   _snake_case    :  Dict					=					WavaVecaCTCTokenizer(
       SCREAMING_SNAKE_CASE__				,       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=SCREAMING_SNAKE_CASE__				,       )
   _snake_case    :  Any					=					True if config.feat_extract_norm == """layer""" else False
   _snake_case    :  Optional[int]					=					WavaVecaFeatureExtractor(
       feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=SCREAMING_SNAKE_CASE__				,       return_attention_mask=SCREAMING_SNAKE_CASE__				,       )
   _snake_case    :  List[Any]					=					WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__				,       tokenizer=SCREAMING_SNAKE_CASE__				)
   processor.save_pretrained(SCREAMING_SNAKE_CASE__				)

  _snake_case    :  Tuple					=					WavaVecaForCTC(SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  List[str]					=					WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__				)

 if is_finetuned or is_seq_class:
  _snake_case	,    _snake_case	,    _snake_case    :  List[Any]					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]				,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				)}				)
 else:
  _snake_case    :  Optional[Any]					=					argparse.Namespace(task="""audio_pretraining"""				)
  _snake_case    :  Dict					=					fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__				)

  _snake_case	,    _snake_case	,    _snake_case    :  str					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,       task=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[Any]					=					model[0].eval()

 recursively_load_weights(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       not is_finetuned				)

 hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
  parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
  parser.add_argument("""--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"""
  )
  parser.add_argument(
      """--is_seq_class""",
      action="""store_true""",
      help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
  )
  a__    =	parser.parse_args()

  a__    =	not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )
