




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()
a_      						=   logging.get_logger(__name__)

a_      						=   {
    "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",
}
a_      						=   [
    "lm_head",
]




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)				->		Tuple:

    """simple docstring"""


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

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

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




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  []
    __UpperCamelCase     						=  fairseq_model.state_dict()

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

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

    for name, value in fairseq_dict.items():
        __UpperCamelCase     						=  False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   )
            __UpperCamelCase     						=  True
        elif "pos_conv" in name:
            load_pos_conv_layer(
                lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   )
            __UpperCamelCase     						=  True
        else:
            for key, mapped_key in MAPPING.items():
                if not is_headless:
                    __UpperCamelCase     						=  '''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]:
                    __UpperCamelCase     						=  True
                    if "*" in mapped_key:
                        __UpperCamelCase     						=  name.split(lowercase_					)[0].split('''.'''					)[-2]
                        __UpperCamelCase     						=  mapped_key.replace('''*'''    ,   lowercase_					)
                    if "weight_g" in name:
                        __UpperCamelCase     						=  '''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase     						=  '''weight_v'''
                    elif "bias" in name:
                        __UpperCamelCase     						=  '''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        __UpperCamelCase     						=  '''weight'''
                    else:
                        __UpperCamelCase     						=  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     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Tuple:

    """simple docstring"""


    __UpperCamelCase     						=  path.split('''.'''					)
    return reduce(lowercase_    ,   lowercase_    ,   lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  access_by_string(lowercase_    ,   lowercase_					)
    __UpperCamelCase     						=  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."					)
    __UpperCamelCase     						=  fsq_value
    logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)				->		Optional[Any]:

    """simple docstring"""


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

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

    set_weights(lowercase_    ,   lowercase_    ,   lowercase_    ,   F"conv_layers.{layer_id}.{layer_type}.{weight_type}"					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


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

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

    set_weights(lowercase_    ,   lowercase_    ,   lowercase_    ,   F"layers.{layer_id}.{layer_type}.{weight_type}"					)




@torch.no_grad()
def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_=None    ,   lowercase_=None    ,   lowercase_=True					)				->		List[Any]:

    """simple docstring"""


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

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

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

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

    __UpperCamelCase     						=  load_dataavec(lowercase_					)

    recursively_load_weights(lowercase_    ,   lowercase_    ,   not is_finetuned					)

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

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

    __UpperCamelCase     						=  processor(lowercase_    ,   return_tensors='''pt'''    ,   padding=lowercase_					)

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

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

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

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

    print(our_output.shape    ,   their_output.shape					)
    __UpperCamelCase     						=  torch.max(torch.abs(our_output - their_output					)					).item()
    print(F"max_absolute_diff = {max_absolute_diff}"					)  # ~ 1e-7
    __UpperCamelCase     						=  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__":
 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"
 )
 a_      						=   parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )












import random

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       Any       ,				snake_case    :       float       ,				snake_case    :       float ):
          __UpperCamelCase     						=  x
          __UpperCamelCase     						=  y







      def 					snake_case		(   self    :       List[Any] ):
          return (self.x**2 + self.y**2) <= 1







      @classmethod
      def 					snake_case		(   cls    :       List[str] ):
          return cls(x=random.random()       ,				y=random.random() )







def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		float:

    """simple docstring"""


    if number_of_simulations < 1:
        raise ValueError('''At least one simulation is necessary to estimate PI.'''					)

    __UpperCamelCase     						=  0
    for _ in range(lowercase_					):
        __UpperCamelCase     						=  Point.random_unit_square()

        if random_point.is_in_unit_circle():
            number_in_unit_circle += 1

    return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
 # import doctest

 # doctest.testmod()
 from math import pi

 a_      						=   "Please enter the desired number of Monte Carlo simulations: "
 a_      						=   estimate_pi(int(input(prompt).strip()))
 print(f"""An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}""")






