


"""simple docstring"""






from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       TFLayoutLMvaForQuestionAnswering,
       TFLayoutLMvaForSequenceClassification,
       TFLayoutLMvaForTokenClassification,
       TFLayoutLMvaModel,
   )

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor


class 			__lowerCamelCase     :

     '''simple docstring'''


     def __init__(					self			, __UpperCAmelCase			, __UpperCAmelCase=2			, __UpperCAmelCase=3			, __UpperCAmelCase=4			, __UpperCAmelCase=2			, __UpperCAmelCase=7			, __UpperCAmelCase=True			, __UpperCAmelCase=True			, __UpperCAmelCase=True			, __UpperCAmelCase=True			, __UpperCAmelCase=99			, __UpperCAmelCase=36			, __UpperCAmelCase=2			, __UpperCAmelCase=4			, __UpperCAmelCase=37			, __UpperCAmelCase="gelu"			, __UpperCAmelCase=0.1			, __UpperCAmelCase=0.1			, __UpperCAmelCase=512			, __UpperCAmelCase=16			, __UpperCAmelCase=2			, __UpperCAmelCase=0.02			, __UpperCAmelCase=6			, __UpperCAmelCase=6			, __UpperCAmelCase=3			, __UpperCAmelCase=4			, __UpperCAmelCase=None			, __UpperCAmelCase=1000			, )				->							Union[str, Any]:
          _a					= parent
          _a					= batch_size
          _a					= num_channels
          _a					= image_size
          _a					= patch_size
          _a					= is_training
          _a					= use_input_mask
          _a					= use_token_type_ids
          _a					= use_labels
          _a					= vocab_size
          _a					= hidden_size
          _a					= num_hidden_layers
          _a					= num_attention_heads
          _a					= intermediate_size
          _a					= hidden_act
          _a					= hidden_dropout_prob
          _a					= attention_probs_dropout_prob
          _a					= max_position_embeddings
          _a					= type_vocab_size
          _a					= type_sequence_label_size
          _a					= initializer_range
          _a					= coordinate_size
          _a					= shape_size
          _a					= num_labels
          _a					= num_choices
          _a					= scope
          _a					= range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          _a					= text_seq_length
          _a					= (image_size // patch_size) ** 2 + 1
          _a					= self.text_seq_length + self.image_seq_length


     def 							_UpperCAmelCase					(					self							)				->							Any:
          _a					= ids_tensor([self.batch_size, self.text_seq_length]			, self.vocab_size							)

          _a					= ids_tensor([self.batch_size, self.text_seq_length, 4]			, self.range_bbox							)
          _a					= bbox.numpy()
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]							):
               for j in range(bbox.shape[1]							):
                    if bbox[i, j, 3] < bbox[i, j, 1]:
                         _a					= bbox[i, j, 3]
                         _a					= bbox[i, j, 1]
                         _a					= tmp_coordinate
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         _a					= bbox[i, j, 2]
                         _a					= bbox[i, j, 0]
                         _a					= tmp_coordinate
          _a					= tf.constant(__a							)

          _a					= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          _a					= None
          if self.use_input_mask:
               _a					= random_attention_mask([self.batch_size, self.text_seq_length]							)

          _a					= None
          if self.use_token_type_ids:
               _a					= ids_tensor([self.batch_size, self.text_seq_length]			, self.type_vocab_size							)

          _a					= None
          _a					= None
          if self.use_labels:
               _a					= ids_tensor([self.batch_size]			, self.type_sequence_label_size							)
               _a					= ids_tensor([self.batch_size, self.text_seq_length]			, self.num_labels							)

          _a					= LayoutLMvaConfig(
              vocab_size=self.vocab_size			, hidden_size=self.hidden_size			, num_hidden_layers=self.num_hidden_layers			, num_attention_heads=self.num_attention_heads			, intermediate_size=self.intermediate_size			, hidden_act=self.hidden_act			, hidden_dropout_prob=self.hidden_dropout_prob			, attention_probs_dropout_prob=self.attention_probs_dropout_prob			, max_position_embeddings=self.max_position_embeddings			, type_vocab_size=self.type_vocab_size			, initializer_range=self.initializer_range			, coordinate_size=self.coordinate_size			, shape_size=self.shape_size			, input_size=self.image_size			, patch_size=self.patch_size			, )

          return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							int:
          _a					= TFLayoutLMvaModel(config=__a							)

          # text + image
          _a					= model(__a			, pixel_values=__a			, training=__a							)
          _a					= model(
              __a			, bbox=__a			, pixel_values=__a			, attention_mask=__a			, token_type_ids=__a			, training=__a			, )
          _a					= model(__a			, bbox=__a			, pixel_values=__a			, training=__a							)

          self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)							)

          # text only
          _a					= model(__a			, training=__a							)
          self.parent.assertEqual(
              result.last_hidden_state.shape			, (self.batch_size, self.text_seq_length, self.hidden_size)							)

          # image only
          _a					= model({'''pixel_values''': pixel_values}			, training=__a							)
          self.parent.assertEqual(
              result.last_hidden_state.shape			, (self.batch_size, self.image_seq_length, self.hidden_size)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							str:
          _a					= self.num_labels
          _a					= TFLayoutLMvaForSequenceClassification(config=__a							)
          _a					= model(
              __a			, bbox=__a			, pixel_values=__a			, attention_mask=__a			, token_type_ids=__a			, labels=__a			, training=__a			, )
          self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Tuple:
          _a					= self.num_labels
          _a					= TFLayoutLMvaForTokenClassification(config=__a							)
          _a					= model(
              __a			, bbox=__a			, pixel_values=__a			, attention_mask=__a			, token_type_ids=__a			, labels=__a			, training=__a			, )
          self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.text_seq_length, self.num_labels)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							List[Any]:
          _a					= 2
          _a					= TFLayoutLMvaForQuestionAnswering(config=__a							)
          _a					= model(
              __a			, bbox=__a			, pixel_values=__a			, attention_mask=__a			, token_type_ids=__a			, start_positions=__a			, end_positions=__a			, training=__a			, )
          self.parent.assertEqual(result.start_logits.shape			, (self.batch_size, self.seq_length)							)
          self.parent.assertEqual(result.end_logits.shape			, (self.batch_size, self.seq_length)							)





     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          _a					= self.prepare_config_and_inputs()
          (_a)					= config_and_inputs
          _a					= {
              'input_ids': input_ids,
              'bbox': bbox,
              'pixel_values': pixel_values,
              'token_type_ids': token_type_ids,
              'attention_mask': input_mask,
          }
          return config, inputs_dict







@require_tf
class 			__lowerCamelCase     (    lowerCAmelCase_     ,    lowerCAmelCase_     ,    unittest.TestCase ):

     '''simple docstring'''
     A_				:		Any			=						(
         (
             TFLayoutLMvaModel,
             TFLayoutLMvaForQuestionAnswering,
             TFLayoutLMvaForSequenceClassification,
             TFLayoutLMvaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     A_				:		Tuple			=						(
         {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
         if is_tf_available()
         else {}
     )

     A_				:		List[str]			=						False
     A_				:		List[str]			=						False
     A_				:		Tuple			=						False


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Union[str, Any]:
          return True


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase=False							)				->							List[str]:
          _a					= copy.deepcopy(__a							)

          if model_class in get_values(__a							):
               _a					= {
                   k: tf.tile(tf.expand_dims(__a			, 1							)			, (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
                   if isinstance(__a			, tf.Tensor							) and v.ndim > 0
                   else v
                   for k, v in inputs_dict.items()
               }

          if return_labels:
               if model_class in get_values(__a							):
                    _a					= tf.ones(self.model_tester.batch_size			, dtype=tf.intaa							)
               elif model_class in get_values(__a							):
                    _a					= tf.zeros(self.model_tester.batch_size			, dtype=tf.intaa							)
                    _a					= tf.zeros(self.model_tester.batch_size			, dtype=tf.intaa							)
               elif model_class in get_values(__a							):
                    _a					= tf.zeros(self.model_tester.batch_size			, dtype=tf.intaa							)
               elif model_class in get_values(__a							):
                    _a					= tf.zeros(
                        (self.model_tester.batch_size, self.model_tester.text_seq_length)			, dtype=tf.intaa							)

          return inputs_dict


     def 							_UpperCAmelCase					(					self							)				->							Optional[int]:
          _a					= TFLayoutLMvaModelTester(self							)
          _a					= ConfigTester(self			, config_class=__a			, hidden_size=37							)


     def 							_UpperCAmelCase					(					self							)				->							Union[str, Any]:
          self.config_tester.run_common_tests()


     def 							_UpperCAmelCase					(					self							)				->							int:
          _a					= self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
               _a					= model_class(__a							)
               if getattr(__a			, '''hf_compute_loss'''			, __a							):
                    # The number of elements in the loss should be the same as the number of elements in the label
                    _a					= self._prepare_for_class(inputs_dict.copy()			, __a			, return_labels=__a							)
                    _a					= prepared_for_class[
                        sorted(prepared_for_class.keys() - inputs_dict.keys()			, reverse=__a							)[0]
                    ]
                    _a					= added_label.shape.as_list()[:1]

                    # Test that model correctly compute the loss with kwargs
                    _a					= self._prepare_for_class(inputs_dict.copy()			, __a			, return_labels=__a							)
                    _a					= prepared_for_class.pop('''input_ids'''							)

                    _a					= model(__a			, **__a							)[0]
                    self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)

                    # Test that model correctly compute the loss when we mask some positions
                    _a					= self._prepare_for_class(inputs_dict.copy()			, __a			, return_labels=__a							)
                    _a					= prepared_for_class.pop('''input_ids'''							)
                    if "labels" in prepared_for_class:
                         _a					= prepared_for_class['labels'].numpy()
                         if len(labels.shape							) > 1 and labels.shape[1] != 1:
                              _a					= -100
                              _a					= tf.convert_to_tensor(__a							)
                              _a					= model(__a			, **__a							)[0]
                              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)
                              self.assertTrue(not np.any(np.isnan(loss.numpy()							)							)							)

                # Test that model correctly compute the loss with a dict
                    _a					= self._prepare_for_class(inputs_dict.copy()			, __a			, return_labels=__a							)
                    _a					= model(__a							)[0]
                    self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)

                    # Test that model correctly compute the loss with a tuple
                    _a					= self._prepare_for_class(inputs_dict.copy()			, __a			, return_labels=__a							)

                    # Get keys that were added with the _prepare_for_class function
                    _a					= prepared_for_class.keys() - inputs_dict.keys()
                    _a					= inspect.signature(model.call							).parameters
                    _a					= list(signature.keys()							)

                    # Create a dictionary holding the location of the tensors in the tuple
                    _a					= {0: 'input_ids'}
                    for label_key in label_keys:
                         _a					= signature_names.index(__a							)
                         _a					= label_key
                    _a					= sorted(tuple_index_mapping.items()							)
                    # Initialize a list with their default values, update the values and convert to a tuple
                    _a					= []

                    for name in signature_names:
                         if name != "kwargs":
                              list_input.append(signature[name].default							)

                    for index, value in sorted_tuple_index_mapping:
                         _a					= prepared_for_class[value]

                    _a					= tuple(__a							)

                    # Send to model
                    _a					= model(tuple_input[:-1]							)[0]

                    self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)


     def 							_UpperCAmelCase					(					self							)				->							Any:
          (
              _a				
          )					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(__a			, __a			, __a			, __a			, __a			, __a							)


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          (
              _a				
          )					= self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               _a					= type
               self.model_tester.create_and_check_model(__a			, __a			, __a			, __a			, __a			, __a							)


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          (
              _a				
          )					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(
              __a			, __a			, __a			, __a			, __a			, __a			, __a							)


     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          (
              _a				
          )					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(
              __a			, __a			, __a			, __a			, __a			, __a			, __a							)


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          (
              _a				
          )					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(
              __a			, __a			, __a			, __a			, __a			, __a			, __a							)





     @slow
     def 							_UpperCAmelCase					(					self							)				->							Union[str, Any]:





          for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a					= TFLayoutLMvaModel.from_pretrained(__a							)
               self.assertIsNotNone(__a							)







def 					A_      (      ):






     """simple docstring"""

     _a					= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     return image


@require_tf
class 			__lowerCamelCase     (    unittest.TestCase ):

     '''simple docstring'''


     @cached_property
     def 							_UpperCAmelCase					(					self							)				->							Any:
          return LayoutLMvaImageProcessor(apply_ocr=__a							) if is_vision_available() else None





     @slow
     def 							_UpperCAmelCase					(					self							)				->							Optional[Any]:
          _a					= TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''							)

          _a					= self.default_image_processor
          _a					= prepare_img()
          _a					= image_processor(images=__a			, return_tensors='''tf'''							).pixel_values

          _a					= tf.constant([[1, 2]]							)
          _a					= tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]							)			, axis=0							)

          # forward pass
          _a					= model(input_ids=__a			, bbox=__a			, pixel_values=__a			, training=__a							)

          # verify the logits
          _a					= (1, 199, 768)
          self.assertEqual(outputs.last_hidden_state.shape			, __a							)

          _a					= tf.constant(
              [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							)

          self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]			, __a			, atol=1e-4							)							)



'''simple docstring'''



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()
__lowercase	:						Union[str, Any]       =	logging.get_logger(__name__)

__lowercase	:						Optional[int]       =	{
    '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',
}
__lowercase	:						Optional[Any]       =	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Dict   ):
					for attribute in key.split('.'   ):
										__a			:       Any					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					if weight_type is not None:
										__a			:       List[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ).shape
					else:
										__a			:       Any					    =  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":
										__a			:       Tuple					    =  value
					elif weight_type == "weight_g":
										__a			:       str					    =  value
					elif weight_type == "weight_v":
										__a			:       Optional[Any]					    =  value
					elif weight_type == "bias":
										__a			:       Union[str, Any]					    =  value
					else:
										__a			:       List[Any]					    =  value

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       int					    =  []
					__a			:       List[str]					    =  fairseq_model.state_dict()

					__a			:       Tuple					    =  hf_model.feature_extractor

					# if encoder has different dim to decoder -> use proj_weight
					__a			:       int					    =  None

					for name, value in fairseq_dict.items():
										__a			:       List[str]					    =  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'						,      )
															__a			:       List[str]					    =  True
										elif name.split('.'   )[0] == "proj":
															__a			:       Tuple					    =  fairseq_model.proj
															__a			:       str					    =  True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
																									__a			:       List[Any]					    =  True
																									if "*" in mapped_key:
																														__a			:       str					    =  name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
																														__a			:       int					    =  mapped_key.replace('*'						,      _SCREAMING_SNAKE_CASE   )
																									if "weight_g" in name:
																														__a			:       List[Any]					    =  'weight_g'
																									elif "weight_v" in name:
																														__a			:       List[Any]					    =  'weight_v'
																									elif "bias" in name:
																														__a			:       Optional[Any]					    =  'bias'
																									elif "weight" in name:
																														__a			:       Tuple					    =  'weight'
																									else:
																														__a			:       Optional[Any]					    =  None
																									set_recursively(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																				continue
										if not is_used:
															unused_weights.append(_SCREAMING_SNAKE_CASE   )

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

					return proj_weight






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       List[str]					    =  full_name.split('conv_layers.'   )[-1]
					__a			:       Any					    =  name.split('.'   )
					__a			:       List[str]					    =  int(items[0]   )
					__a			:       List[Any]					    =  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."""
															)
															__a			:       List[str]					    =  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."""
															)
															__a			:       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:
															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."
															)
															__a			:       Tuple					    =  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."""
															)
															__a			:       List[Any]					    =  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   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a    ,				__a			:       List[str]					    =  emb.weight.shape
					__a			:       str					    =  nn.Linear(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      bias=_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  emb.weight.data
					return lin_layer






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'						,      encoding='utf-8'   ) as f:
										__a			:       Union[str, Any]					    =  f.readlines()
										__a			:       Tuple					    =  [line.split(' '   )[0] for line in lines]

					__a			:       int					    =  len(_SCREAMING_SNAKE_CASE   )

					__a			:       List[Any]					    =  {
					    '<s>': 0,
					    '<pad>': 1,
					    '</s>': 2,
					    '<unk>': 3,
					}

					vocab_dict.update(dict(zip(_SCREAMING_SNAKE_CASE						,      range(4						,      num_words + 4   )   )   )   )
					return vocab_dict






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      ):
					__a			:       Optional[int]					    =  WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  SpeechaTextaConfig.from_pretrained(
					    _SCREAMING_SNAKE_CASE						,      vocab_size=_SCREAMING_SNAKE_CASE						,      decoder_layers=_SCREAMING_SNAKE_CASE						,      do_stable_layer_norm=_SCREAMING_SNAKE_CASE   )

					__a			:       Dict					    =  WavaVecaFeatureExtractor(
					    feature_size=1						,      sampling_rate=16_000						,      padding_value=0						,      do_normalize=_SCREAMING_SNAKE_CASE						,      return_attention_mask=_SCREAMING_SNAKE_CASE						,      )

					__a    ,				__a    ,				__a			:       List[Any]					    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]						,      arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
					__a			:       Optional[int]					    =  model[0].eval()

					# set weights for wav2vec2 encoder
					__a			:       Tuple					    =  WavaVecaModel(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  recursively_load_weights_wavaveca(model.encoder						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Dict					    =  SpeechaTextaForCausalLM(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       Optional[int]					    =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,      strict=_SCREAMING_SNAKE_CASE   )

					# set output linear layer
					unexpected_keys.remove('embed_out'   )
					__a			:       Optional[Any]					    =  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}"""   )

					__a			:       Tuple					    =  SpeechEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE						,      decoder=_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  False

					# add projection layer
					__a			:       str					    =  nn.Parameter(projection_layer.weight   )
					__a			:       Any					    =  nn.Parameter(projection_layer.bias   )

					__a			:       str					    =  create_vocab_dict(_SCREAMING_SNAKE_CASE   )

					with open(os.path.join(_SCREAMING_SNAKE_CASE						,      'vocab.json'   )						,      'w'   ) as fp:
										json.dump(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Optional[Any]					    =  SpeechaTextaTokenizer(os.path.join(_SCREAMING_SNAKE_CASE						,      'vocab.json'   )   )
					tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )

					__a			:       Union[str, Any]					    =  hf_wavavec.config.to_dict()
					__a			:       Tuple					    =  tokenizer.pad_token_id
					__a			:       Optional[int]					    =  tokenizer.bos_token_id
					__a			:       Union[str, Any]					    =  tokenizer.eos_token_id
					__a			:       Tuple					    =  'speech_to_text_2'
					__a			:       Tuple					    =  'wav2vec2'

					__a			:       List[str]					    =  SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE   )

					hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE   )
					feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	__lowercase	:						Dict       =	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_02_24, type=int, help='Vocab size of decoder')
	parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

	__lowercase	:						Tuple       =	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,
	)



