





'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[int]			):
    A__							=     [
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "decoder.output_projection.weight",
        "_float_tensor",
        "encoder.embed_positions._float_tensor",
        "decoder.embed_positions._float_tensor",
    ]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase		,			_lowerCamelCase			)





def 		UpperCamelCase							(				_lowerCamelCase   :   List[str]			):
    A__							=     list(s_dict.keys()			)
    for key in keys:
        if "transformer_layers" in key:
            A__							=     s_dict.pop(_lowerCamelCase			)
        elif "subsample" in key:
            A__							=     s_dict.pop(_lowerCamelCase			)





def 		UpperCamelCase							(				_lowerCamelCase   :   int			):
    A__,      A__							=     emb.weight.shape
    A__							=     nn.Linear(_lowerCamelCase		,			_lowerCamelCase		,			bias=_lowerCamelCase			)
    A__							=     emb.weight.data
    return lin_layer





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Optional[Any]			):
    A__							=     torch.load(_lowerCamelCase		,			map_location="cpu"			)
    A__							=     mam_aaa["args"]
    A__							=     mam_aaa["model"]
    A__							=     state_dict["decoder.output_projection.weight"]

    remove_ignore_keys_(_lowerCamelCase			)
    rename_keys(_lowerCamelCase			)

    A__							=     state_dict["decoder.embed_tokens.weight"].shape[0]

    A__							=     args.share_decoder_input_output_embed

    A__							=     [int(_lowerCamelCase			) for i in args.conv_kernel_sizes.split(","			)]
    A__							=     SpeechaTextConfig(
        vocab_size=_lowerCamelCase		,			max_source_positions=args.max_source_positions		,			max_target_positions=args.max_target_positions		,			encoder_layers=args.encoder_layers		,			decoder_layers=args.decoder_layers		,			encoder_attention_heads=args.encoder_attention_heads		,			decoder_attention_heads=args.decoder_attention_heads		,			encoder_ffn_dim=args.encoder_ffn_embed_dim		,			decoder_ffn_dim=args.decoder_ffn_embed_dim		,			d_model=args.encoder_embed_dim		,			dropout=args.dropout		,			attention_dropout=args.attention_dropout		,			activation_dropout=args.activation_dropout		,			activation_function="relu"		,			num_conv_layers=len(_lowerCamelCase			)		,			conv_channels=args.conv_channels		,			conv_kernel_sizes=_lowerCamelCase		,			input_feat_per_channel=args.input_feat_per_channel		,			input_channels=args.input_channels		,			tie_word_embeddings=_lowerCamelCase		,			num_beams=5		,			max_length=2_00		,			use_cache=_lowerCamelCase		,			decoder_start_token_id=2		,			early_stopping=_lowerCamelCase		,			)

    A__							=     SpeechaTextForConditionalGeneration(_lowerCamelCase			)
    A__,      A__							=     model.model.load_state_dict(_lowerCamelCase		,			strict=_lowerCamelCase			)
    if len(_lowerCamelCase			) > 0 and not set(_lowerCamelCase			) <= {
        "encoder.embed_positions.weights",
        "decoder.embed_positions.weights",
    }:
        raise ValueError(
            "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
            F" but all the following weights are missing {missing}"			)

    if tie_embeds:
        A__							=     make_linear_from_emb(model.model.decoder.embed_tokens			)
    else:
        A__							=     lm_head_weights

    model.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
  __lowerCAmelCase				:      Any						      =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  __lowerCAmelCase				:      Optional[int]						      =parser.parse_args()
  convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)










'''simple docstring'''

import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase				:      int						      =5_0000
__lowerCAmelCase				:      Union[str, Any]						      =5000

__lowerCAmelCase      ,	__lowerCAmelCase				:      Tuple						      =os.path.split(__file__)
__lowerCAmelCase				:      int						      =os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   int			):
    for i in range(_lowerCamelCase			):
        A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Any			):
    for i in range(0		,			len(_lowerCamelCase			)		,			_lowerCamelCase			):
        A__							=     dataset[i : i + batch_size]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   List[str]			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(_lowerCamelCase			):
            A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Dict			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(0		,			_lowerCamelCase		,			_lowerCamelCase			):
            A__							=     dataset[i : i + batch_size]





def 		UpperCamelCase							(				):
    A__							=     {"num examples": SPEED_TEST_N_EXAMPLES}
    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
        (read_formatted, {"type": "torch", "length": SMALL_TEST}),
        (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]

    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"			)
        A__							=     datasets.Features(
            {"list": datasets.Sequence(datasets.Value("float32"			)			), "numbers": datasets.Value("float32"			)}			)
        A__							=     generate_example_dataset(
            os.path.join(_lowerCamelCase		,			"dataset.arrow"			)		,			_lowerCamelCase		,			num_examples=_lowerCamelCase		,			seq_shapes={"list": (1_00,)}		,			)
        print("first set of iterations"			)
        for func, kwargs in functions:
            print(func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(_lowerCamelCase		,			**_lowerCamelCase			)

        print("shuffling dataset"			)
        A__							=     dataset.shuffle()
        print("Second set of iterations (after shuffling"			)
        for func, kwargs in functions_shuffled:
            print("shuffled "		,			func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(
                _lowerCamelCase		,			**_lowerCamelCase			)

    with open(_lowerCamelCase		,			"wb"			) as f:
        f.write(json.dumps(_lowerCamelCase			).encode("utf-8"			)			)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_iterating()



