




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 a_ 		=  "platform"

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None   )							->						Tuple:

    """simple docstring"""


    if attention_mask is None:
        __UpperCamelCase			=    np.where(input_ids != config.pad_token_id       ,  1       ,  0   )
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
    }

@require_flax
class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Tuple			,							snake_case       :			Optional[Any]			,							snake_case       :			Union[str, Any]=13			,							snake_case       :			int=7			,							snake_case       :			Union[str, Any]=True			,							snake_case       :			Any=False			,							snake_case       :			List[str]=99			,							snake_case       :			Union[str, Any]=16			,							snake_case       :			Union[str, Any]=2			,							snake_case       :			Any=4			,							snake_case       :			int=4			,							snake_case       :			str="gelu"			,							snake_case       :			Optional[int]=0.1			,							snake_case       :			int=0.1			,							snake_case       :			Any=20			,							snake_case       :			int=2			,							snake_case       :			Optional[Any]=1			,							snake_case       :			int=0			,							snake_case       :			str=16			,							snake_case       :			Any=16			,							snake_case       :			Optional[Any]=0.02			,							):
          __UpperCamelCase			=    parent
          __UpperCamelCase			=    batch_size
          __UpperCamelCase			=    seq_length
          __UpperCamelCase			=    is_training
          __UpperCamelCase			=    use_labels
          __UpperCamelCase			=    vocab_size
          __UpperCamelCase			=    hidden_size
          __UpperCamelCase			=    num_hidden_layers
          __UpperCamelCase			=    num_attention_heads
          __UpperCamelCase			=    intermediate_size
          __UpperCamelCase			=    hidden_act
          __UpperCamelCase			=    hidden_dropout_prob
          __UpperCamelCase			=    attention_probs_dropout_prob
          __UpperCamelCase			=    max_position_embeddings
          __UpperCamelCase			=    eos_token_id
          __UpperCamelCase			=    pad_token_id
          __UpperCamelCase			=    bos_token_id
          __UpperCamelCase			=    embed_dim
          __UpperCamelCase			=    word_embed_proj_dim
          __UpperCamelCase			=    initializer_range
          __UpperCamelCase			=    False







      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,							self.vocab_size					)			,							3			,							self.vocab_size					)
          __UpperCamelCase			=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,							dtype=np.intaa					))			,							-1					)

          __UpperCamelCase			=    OPTConfig(
              vocab_size=self.vocab_size			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							ffn_dim=self.intermediate_size			,							dropout=self.hidden_dropout_prob			,							attention_dropout=self.attention_probs_dropout_prob			,							max_position_embeddings=self.max_position_embeddings			,							eos_token_id=self.eos_token_id			,							bos_token_id=self.bos_token_id			,							pad_token_id=self.pad_token_id			,							embed_dim=self.embed_dim			,							is_encoder_decoder=snake_case			,							word_embed_proj_dim=self.word_embed_proj_dim			,							initializer_range=self.initializer_range			,							use_cache=snake_case			,							)
          __UpperCamelCase			=    prepare_opt_inputs_dict(snake_case			,							snake_case					)
          return config, inputs_dict







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_config_and_inputs()
          return config, inputs_dict







      def    snake_case						(  self       :			Tuple			,							snake_case       :			int			,							snake_case       :			int			,							snake_case       :			Any					):
          __UpperCamelCase			=    20
          __UpperCamelCase			=    model_class_name(snake_case					)

          __UpperCamelCase			=    inputs_dict['''input_ids''']
          __UpperCamelCase			=    inputs_dict['''attention_mask''']

          __UpperCamelCase			=    model.init_cache(input_ids.shape[0]			,							snake_case					)
          __UpperCamelCase			=    jnp.ones((input_ids.shape[0], max_length)			,							dtype='''i4'''					)

          __UpperCamelCase			=    jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,							(input_ids.shape[0], input_ids.shape[-1] - 1)			,							)
          __UpperCamelCase			=    model(
              input_ids[:, :-1]			,							attention_mask=snake_case			,							past_key_values=snake_case			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,							dtype='''i4'''					)
          __UpperCamelCase			=    model(
              input_ids[:, -1:]			,							attention_mask=snake_case			,							past_key_values=outputs_cache.past_key_values			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    model(snake_case					)

          __UpperCamelCase			=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
          self.parent.assertTrue(diff < 1E-3			,							msg=F"Max diff is {diff}"					)







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			Optional[Any]			,							snake_case       :			Any			,							snake_case       :			int					):
          __UpperCamelCase			=    20
          __UpperCamelCase			=    model_class_name(snake_case					)

          __UpperCamelCase		,   __UpperCamelCase			=    (
              inputs_dict['''input_ids'''],
              inputs_dict['''attention_mask'''],
          )

          __UpperCamelCase			=    jnp.concatenate(
              [
                  attention_mask,
                  jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
              ]			,							axis=-1			,							)

          __UpperCamelCase			=    model.init_cache(input_ids.shape[0]			,							snake_case					)
          __UpperCamelCase			=    jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,							(input_ids.shape[0], input_ids.shape[-1] - 1)			,							)

          __UpperCamelCase			=    model(
              input_ids[:, :-1]			,							attention_mask=snake_case			,							past_key_values=snake_case			,							position_ids=snake_case			,							)
          __UpperCamelCase			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,							dtype='''i4'''					)
          __UpperCamelCase			=    model(
              input_ids[:, -1:]			,							past_key_values=outputs_cache.past_key_values			,							attention_mask=snake_case			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    model(snake_case			,							attention_mask=snake_case					)

          __UpperCamelCase			=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
          self.parent.assertTrue(diff < 1E-3			,							msg=F"Max diff is {diff}"					)







@require_flax
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      unittest.TestCase				,      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Any     =       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__      :			Optional[int]     =       () if is_flax_available() else ()







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    FlaxOPTModelTester(self					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(snake_case			,							snake_case			,							snake_case					)







      def    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(snake_case			,							snake_case			,							snake_case					)







      @slow
      def    snake_case						(  self       :			Union[str, Any]					):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase			=    model_class_name.from_pretrained('''facebook/opt-125m'''					)
              __UpperCamelCase			=    np.ones((1, 1)					) * model.config.eos_token_id
              __UpperCamelCase			=    model(snake_case					)
              self.assertIsNotNone(snake_case					)







@require_sentencepiece
@require_flax
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      @slow
      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
          __UpperCamelCase			=    jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
          __UpperCamelCase			=    model(input_ids=snake_case					).last_hidden_state
          __UpperCamelCase			=    (1, 11, 512)
          self.assertEqual(output.shape			,							snake_case					)
          __UpperCamelCase			=    jnp.array(
              [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]					)
          self.assertTrue(jnp.allclose(output[:, :3, :3]			,							snake_case			,							atol=4E-2					)					)







@require_flax
@slow
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			Optional[Any]					):
          super().setUp()
          __UpperCamelCase			=    '''facebook/opt-350m'''







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(self.path_model					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(self.path_model					)

          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to''',
              '''In the city of''',
              '''Paris is the capital of France and''',
              '''Computers and mobile phones have taken''',
          ]
          # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
          __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''			,							padding=snake_case			,							add_special_tokens=snake_case					)
          __UpperCamelCase			=    model(inputs.input_ids			,							attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
          __UpperCamelCase			=    jnp.array(
              [
                  [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                  [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                  [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                  [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
              ]					)
          self.assertTrue(jnp.allclose(snake_case			,							snake_case			,							atol=4E-2					)					)

          __UpperCamelCase			=    jax.jit(snake_case					)
          __UpperCamelCase			=    model(inputs.input_ids			,							attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
          self.assertTrue(jnp.allclose(snake_case			,							snake_case			,							atol=4E-2					)					)







@require_flax
@slow
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      @property
      def    snake_case						(  self       :			Any					):
          return [
              "Today is a beautiful day and I want",
              "In the city of",
              "Paris is the capital of France and",
              "Computers and mobile phones have taken",
          ]







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    '''facebook/opt-125m'''

          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to''',
              '''In the city of New York, the city''',
              '''Paris is the capital of France and the capital''',
              '''Computers and mobile phones have taken over the''',
          ]

          __UpperCamelCase			=    []

          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)

          for prompt in self.prompts:
              __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''					).input_ids

              __UpperCamelCase			=    model.generate(snake_case			,							max_length=10					)
              __UpperCamelCase			=    generated_ids[0]

              __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)
              predicted_outputs += generated_string

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    '''facebook/opt-350m'''

          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to''',
              '''In the city of San Francisco, the city''',
              '''Paris is the capital of France and the capital''',
              '''Computers and mobile phones have taken over the''',
          ]

          __UpperCamelCase			=    []
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)

          for prompt in self.prompts:
              __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''					).input_ids

              __UpperCamelCase			=    model.generate(snake_case			,							max_length=10					)
              __UpperCamelCase			=    generated_ids[0]

              __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)
              predicted_outputs += generated_string

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    '''facebook/opt-125m'''
          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to thank''',
              '''In the city of Rome Canaver Canaver Canaver Canaver''',
          ]
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)
          __UpperCamelCase			=    tokenizer(
              [
                  '''Today is a beautiful day and I want to''',
                  '''In the city of''',
              ]			,							return_tensors='''jax'''			,							padding=snake_case			,							)

          __UpperCamelCase			=    jax.jit(model.generate					)

          __UpperCamelCase			=    jit_generate(inputs['''input_ids''']			,							attention_mask=inputs['''attention_mask''']					).sequences

          __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)

          self.assertIsNotNone(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    '''facebook/opt-350m'''

          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)

          __UpperCamelCase			=    '''left'''

          # use different length sentences to test batching
          __UpperCamelCase			=    [
              '''Hello, my dog is a little''',
              '''Today, I''',
          ]

          __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''			,							padding=snake_case					)
          __UpperCamelCase			=    inputs['''input_ids''']

          __UpperCamelCase			=    model.generate(input_ids=snake_case			,							attention_mask=inputs['''attention_mask''']			,							trace=snake_case					)

          __UpperCamelCase			=    tokenizer(sentences[0]			,							return_tensors='''jax'''					).input_ids
          __UpperCamelCase			=    model.generate(input_ids=snake_case					)

          __UpperCamelCase			=    inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
          __UpperCamelCase			=    tokenizer(sentences[1]			,							return_tensors='''jax'''					).input_ids
          __UpperCamelCase			=    model.generate(input_ids=snake_case			,							max_length=model.config.max_length - num_paddings					)

          __UpperCamelCase			=    tokenizer.batch_decode(outputs[0]			,							skip_special_tokens=snake_case					)
          __UpperCamelCase			=    tokenizer.decode(output_non_padded[0][0]			,							skip_special_tokens=snake_case					)
          __UpperCamelCase			=    tokenizer.decode(output_padded[0][0]			,							skip_special_tokens=snake_case					)

          __UpperCamelCase			=    [
              '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
              '''Today, I was in the middle of a conversation with a friend about the''',
          ]
          self.assertListEqual(snake_case			,							snake_case					)
          self.assertListEqual(snake_case			,							[non_padded_sentence, padded_sentence]					)














import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

lowerCamelCase					    =	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCamelCase					    =	[
    '''lm_head''',
]
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    for attribute in key.split('.'   ):
        _lowercase     			=						getattr(_lowercase,   _lowercase   )

    if weight_type is not None:
        _lowercase     			=						getattr(_lowercase,   _lowercase   ).shape
    else:
        _lowercase     			=						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     			=						value
    elif weight_type == "weight_g":
        _lowercase     			=						value
    elif weight_type == "weight_v":
        _lowercase     			=						value
    elif weight_type == "bias":
        _lowercase     			=						value
    else:
        _lowercase     			=						value

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


    '''simple docstring'''



    _lowercase     			=						[]
    _lowercase     			=						fairseq_model.state_dict()

    if not is_headless:
        _lowercase     			=						hf_model.dataavec_audio.feature_extractor
        _lowercase     			=						hf_model.dataavec_audio.encoder.pos_conv_embed

    else:
        _lowercase     			=						hf_model.feature_extractor
        _lowercase     			=						hf_model.encoder.pos_conv_embed

    for name, value in fairseq_dict.items():
        _lowercase     			=						False
        if "conv_layers" in name:
            load_conv_layer(
                _lowercase,   _lowercase,   _lowercase,   _lowercase,   )
            _lowercase     			=						True
        elif "pos_conv" in name:
            load_pos_conv_layer(
                _lowercase,   _lowercase,   _lowercase,   _lowercase,   )
            _lowercase     			=						True
        else:
            for key, mapped_key in MAPPING.items():
                if not is_headless:
                    _lowercase     			=						'data2vec_audio.' + 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     			=						True
                    if "*" in mapped_key:
                        _lowercase     			=						name.split(_lowercase   )[0].split('.'   )[-2]
                        _lowercase     			=						mapped_key.replace('*',   _lowercase   )
                    if "weight_g" in name:
                        _lowercase     			=						'weight_g'
                    elif "weight_v" in name:
                        _lowercase     			=						'weight_v'
                    elif "bias" in name:
                        _lowercase     			=						'bias'
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _lowercase     			=						'weight'
                    else:
                        _lowercase     			=						None
                    set_recursively(_lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase   )
                continue
        if not is_used:
            unused_weights.append(_lowercase   )

    logger.warning(f'''Unused weights: {unused_weights}'''   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						path.split('.'   )
    return reduce(_lowercase,   _lowercase,   _lowercase   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						access_by_string(_lowercase,   _lowercase   )
    _lowercase     			=						hf_weight.data

    if fsq_value.shape != hf_value.shape:
        raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''   )
    _lowercase     			=						fsq_value
    logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						full_name.split('conv_layers.'   )[-1]
    _lowercase     			=						name.split('.'   )
    _lowercase     			=						int(items[0]   )
    _lowercase     			=						int(items[1]   )

    _lowercase     			=						name.split('.'   )[-1]
    if type_id == 0:
        _lowercase     			=						'conv'
    elif type_id == 2:
        _lowercase     			=						'layer_norm'
    else:
        unused_weights.append(_lowercase   )
        return

    set_weights(_lowercase,   _lowercase,   _lowercase,   f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						full_name.split('pos_conv.'   )[-1]
    _lowercase     			=						name.split('.'   )
    _lowercase     			=						int(items[0]   )
    _lowercase     			=						int(items[1]   )

    _lowercase     			=						name.split('.'   )[-1]
    if type_id != 0:
        unused_weights.append(_lowercase   )
        return
    else:
        _lowercase     			=						'conv'

    set_weights(_lowercase,   _lowercase,   _lowercase,   f'''layers.{layer_id}.{layer_type}.{weight_type}'''   )
@torch.no_grad()
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase=None,   _lowercase=None,   _lowercase=True   ):


    '''simple docstring'''



    if config_path is not None:
        _lowercase     			=						DataaVecAudioConfig.from_pretrained(_lowercase   )
    else:
        _lowercase     			=						DataaVecAudioConfig()

    if not is_finetuned:
        # Modify final_proj layer name
        _lowercase     			=						DataaVecAudioModel(_lowercase   )
        _lowercase     			=						os.path.dirname(_lowercase   )

        _lowercase     			=						torch.load(_lowercase   )
        _lowercase     			=						state_dict['model'].pop('final_proj.0.weight'   )
        _lowercase     			=						state_dict['model'].pop('final_proj.0.bias'   )
        _lowercase     			=						os.path.join(_lowercase,   'converted.pt'   )
        torch.save(_lowercase,   _lowercase   )
    else:
        _lowercase     			=						DataaVecAudioForCTC(_lowercase   )
        _lowercase     			=						checkpoint_path

    def load_dataavec(_lowercase   ):
        _lowercase			,     _lowercase			,     _lowercase     			=						fairseq.checkpoint_utils.load_model_ensemble_and_task([path]   )
        return model[0].eval()

    _lowercase     			=						load_dataavec(_lowercase   )

    recursively_load_weights(_lowercase,   _lowercase,   not is_finetuned   )

    _lowercase     			=						WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'   )

    _lowercase     			=						load_dataset('patrickvonplaten/librispeech_asr_dummy',   'clean',   split='validation'   )
    _lowercase     			=						[x['array'] for x in ds[:4]['audio']]

    _lowercase     			=						processor(_lowercase,   return_tensors='pt',   padding=_lowercase   )

    _lowercase     			=						inputs.input_values
    _lowercase     			=						inputs.attention_mask
    #    input_values = inputs.input_values[:, :-1]
    #    attention_mask = inputs.attention_mask[:, :-1]

    hf_wavavec.eval()
    model.eval()
    if is_finetuned:
        _lowercase     			=						model(source=_lowercase,   padding_mask=(1 - attention_mask),   mask=_lowercase,   features_only=_lowercase   )[
            'encoder_out'
        ].transpose(0,   1   )
        _lowercase     			=						hf_wavavec(_lowercase,   attention_mask=_lowercase   )['logits']

        _lowercase     			=						torch.argmax(_lowercase,   dim=-1   )
        _lowercase     			=						processor.batch_decode(_lowercase   )

        print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''   )
    else:
        _lowercase     			=						model(source=_lowercase,   padding_mask=(1 - attention_mask),   mask=_lowercase,   features_only=_lowercase   )[
            'layer_results'
        ][-1][0].transpose(0,   1   )
        _lowercase     			=						hf_wavavec(_lowercase,   attention_mask=_lowercase   )['last_hidden_state']

    print(our_output.shape,   their_output.shape   )
    _lowercase     			=						torch.max(torch.abs(our_output - their_output   )   ).item()
    print(f'''max_absolute_diff = {max_absolute_diff}'''   )  # ~ 1e-7
    _lowercase     			=						torch.allclose(_lowercase,   _lowercase,   atol=1e-3   )
    print('Do both models output the same tensors?',   '🔥' if success else '💩'   )
    if not success:
        raise Exception('Something went wRoNg'   )

    hf_wavavec.save_pretrained(_lowercase   )

    if is_finetuned:
        processor.save_pretrained(_lowercase   )
    else:
        processor.feature_extractor.save_pretrained(_lowercase   )


if __name__ == "__main__":
   lowerCamelCase					    =	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					    =	parser.parse_args()
   convert_wavaveca_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )
