






from typing import Dict, Optional, Tuple

import torch



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     torch.Tensor      )  -> torch.Tensor:





    """simple docstring"""

    __A             =		boundaries[1] - boundaries[0]
    __A             =		boundaries + step / 2
    __A             =		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )]     ,			dim=0      )
    return bin_centers



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     torch.Tensor     ,			__lowercase				:     torch.Tensor     ,			)  -> Tuple[torch.Tensor, torch.Tensor]:





    """simple docstring"""

    __A             =		_calculate_bin_centers(__lowercase      )
    return (
        torch.sum(aligned_distance_error_probs * bin_centers     ,			dim=-1      ),
        bin_centers[-1],
    )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     torch.Tensor     ,			__lowercase				:     int = 3_1     ,			__lowercase				:     int = 6_4     ,			**__lowercase				:     Tuple     ,			)  -> Dict[str, torch.Tensor]:





    """simple docstring"""

    __A             =		torch.linspace(0     ,			__lowercase     ,			steps=(no_bins - 1)     ,			device=logits.device      )

    __A             =		torch.nn.functional.softmax(__lowercase     ,			dim=-1      )
    __A      ,		__A             =		_calculate_expected_aligned_error(
        alignment_confidence_breaks=__lowercase     ,			aligned_distance_error_probs=__lowercase     ,			)

    return {
        "aligned_confidence_probs": aligned_confidence_probs,
        "predicted_aligned_error": predicted_aligned_error,
        "max_predicted_aligned_error": max_predicted_aligned_error,
    }






def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     torch.Tensor     ,			__lowercase				:     Optional[torch.Tensor] = None     ,			__lowercase				:     int = 3_1     ,			__lowercase				:     int = 6_4     ,			__lowercase				:     float = 1E-8     ,			**__lowercase				:     List[Any]     ,			)  -> torch.Tensor:





    """simple docstring"""

    if residue_weights is None:
        __A             =		logits.new_ones(logits.shape[-2]      )

    __A             =		torch.linspace(0     ,			__lowercase     ,			steps=(no_bins - 1)     ,			device=logits.device      )

    __A             =		_calculate_bin_centers(__lowercase      )
    torch.sum(__lowercase      )
    __A             =		logits.shape[-2]
    __A             =		max(__lowercase     ,			1_9      )

    __A             =		1.24 * (clipped_n - 1_5) ** (1.0 / 3) - 1.8

    __A             =		torch.nn.functional.softmax(__lowercase     ,			dim=-1      )

    __A             =		1.0 / (1 + (bin_centers**2) / (da**2))
    __A             =		torch.sum(probs * tm_per_bin     ,			dim=-1      )

    __A             =		residue_weights / (eps + residue_weights.sum())
    __A             =		torch.sum(predicted_tm_term * normed_residue_mask     ,			dim=-1      )

    __A             =		per_alignment * residue_weights

    __A             =		(weighted == torch.max(__lowercase      )).nonzero()[0]
    return per_alignment[tuple(__lowercase      )]












# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]     ,			__lowercase				:     str      )  -> Optional[int]:





    """simple docstring"""

    if name != "training_function" and name != "main":
        raise ValueError(f"Incorrect function name passed: {name}, choose either 'main' or 'training_function'"      )
    __A      ,		__A             =		[], False
    for line in lines:
        if not found_start and f"def {name}" in line:
            __A             =		True
            good_lines.append(__lowercase      )
            continue
        if found_start:
            if name == "training_function" and "def main" in line:
                return good_lines
            if name == "main" and "if __name__" in line:
                return good_lines
            good_lines.append(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]      )  -> Optional[int]:





    """simple docstring"""

    return [line for line in lines if not line.lstrip().startswith("""#"""      ) and line != "\n"]






def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     str     ,			__lowercase				:     str     ,			__lowercase				:     bool     ,			__lowercase				:     str = None      )  -> List[Any]:





    """simple docstring"""

    with open(__lowercase     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    with open(os.path.abspath(os.path.join("""examples"""     ,			"""nlp_example.py"""      )      )     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    with open(__lowercase     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    if secondary_filename is not None:
        with open(__lowercase     ,			"""r"""      ) as f:
            __A             =		f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
    if parser_only:
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        if secondary_filename is not None:
            __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
    else:
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        if secondary_filename is not None:
            __A             =		clean_lines(
                get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )

    __A             =		"""train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

    # Specific code in our script that differs from the full version, aka what is new
    __A             =		[]
    __A             =		[]  # We keep track of the idxs just in case it's a repeated statement
    __A             =		iter(__lowercase      )
    for i in range(len(__lowercase      ) - 1      ):
        if i not in passed_idxs:
            __A             =		next(__lowercase      )
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_feature_code.append(__lowercase      )
                    passed_idxs.append(__lowercase      )
                else:
                    # Skip over the `config['num_epochs'] = 2` statement
                    __A             =		next(__lowercase      )

    # Extract out just the new parts from the full_file_training_func
    __A             =		[]
    __A             =		[]  # We keep track of the idxs just in case it's a repeated statement
    for i, line in enumerate(__lowercase      ):
        if i not in passed_idxs:
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_full_example_parts.append(__lowercase      )
                    passed_idxs.append(__lowercase      )

    # Finally, get the overall diff
    __A             =		[line for line in new_feature_code if line not in new_full_example_parts]
    if secondary_filename is not None:
        __A             =		[line for line in full_file_contents if line not in secondary_file_func]
        __A             =		[line for line in diff_from_example if line not in diff_from_two]

    return diff_from_example




