import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)






def  snake_case  (       snake_case__			:nn.ModuleList   ,	snake_case__			:nn.ModuleList   ,	snake_case__			:List[int])						->   None:
       _A        =  nn.ModuleList([src_layers[i] for i in layers_to_copy])
       assert len(snake_case__) == len(snake_case__), F'''{len(snake_case__)} != {len(snake_case__)}'''
       dest_layers.load_state_dict(layers_to_copy.state_dict())


_SCREAMING_SNAKE_CASE										=						{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
_SCREAMING_SNAKE_CASE										=						{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}






def  snake_case  (       snake_case__			:int   ,	snake_case__			:Tuple)						->   Tuple:
       try:
              _A        =  LAYERS_TO_COPY[n_teacher][n_student]
              return val
       except KeyError:
              if n_student != n_teacher:
                     warnings.warn(
                         F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
                         F''' {n_student}''')
              return list(range(snake_case__))






def  snake_case  (       snake_case__			:int   ,	snake_case__			:str)						->   List[int]:
       if n_student > n_teacher:
              raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''')
       elif n_teacher == n_student:
              return list(range(snake_case__))
       elif n_student == 1:
              return [n_teacher - 1]
       else:
              return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def  snake_case  (       snake_case__			:Union[str, PreTrainedModel]   ,	snake_case__			:Union[str, Path] = "student"   ,	snake_case__			:Union[int, None] = None   ,	snake_case__			:Union[int, None] = None   ,	snake_case__			:Optional[int]=False   ,	snake_case__			:Tuple=None   ,	snake_case__			:Any=None   ,	**snake_case__			:Any   ,	)						->   Tuple[PreTrainedModel, List[int], List[int]]:
       _A        =  """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
       assert (e is not None) or (d is not None), _msg
       if isinstance(snake_case__   ,	snake_case__):
              AutoTokenizer.from_pretrained(snake_case__).save_pretrained(snake_case__)  # purely for convenience
              _A        =  AutoModelForSeqaSeqLM.from_pretrained(snake_case__).eval()
       else:
              assert isinstance(snake_case__   ,	snake_case__), F'''teacher must be a model or string got type {type(snake_case__)}'''
       _A        =  teacher.config.to_diff_dict()

       try:
              _A   ,  _A        =  teacher.config.encoder_layers, teacher.config.decoder_layers
              if e is None:
                     _A        =  teacher_e
              if d is None:
                     _A        =  teacher_d
              init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d})
       except AttributeError:  # T5
              if hasattr(teacher.config   ,	"""num_encoder_layers"""):
                     _A   ,  _A        =  teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
              else:
                     _A   ,  _A        =  teacher.config.num_layers, teacher.config.num_decoder_layers
              if e is None:
                     _A        =  teacher_e
              if d is None:
                     _A        =  teacher_d
              if hasattr(teacher.config   ,	"""num_encoder_layers"""):
                     init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d})
              else:
                     init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d})

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
       init_kwargs.update(snake_case__)

       # Copy weights
       _A        =  teacher.config_class(**snake_case__)
       _A        =  AutoModelForSeqaSeqLM.from_config(snake_case__)
       # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
       _A        =  student.load_state_dict(teacher.state_dict()   ,	strict=snake_case__)
       assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

       if copy_first_teacher_layers:  # Our copying is done. We just log and save
              _A   ,  _A        =  list(range(snake_case__)), list(range(snake_case__))
              logger.info(
                  F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
                  F''' {save_path}''')
              student.save_pretrained(snake_case__)
              return student, e_layers_to_copy, d_layers_to_copy

       # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
       if e_layers_to_copy is None:
              _A        =  pick_layers_to_copy(snake_case__   ,	snake_case__)
       if d_layers_to_copy is None:
              _A        =  pick_layers_to_copy(snake_case__   ,	snake_case__)

       try:
              if hasattr(
                  snake_case__   ,	"""prophetnet"""):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
                     copy_layers(teacher.prophetnet.encoder.layers   ,	student.prophetnet.encoder.layers   ,	snake_case__)
                     copy_layers(teacher.prophetnet.decoder.layers   ,	student.prophetnet.decoder.layers   ,	snake_case__)
              else:
                     copy_layers(teacher.model.encoder.layers   ,	student.model.encoder.layers   ,	snake_case__)
                     copy_layers(teacher.model.decoder.layers   ,	student.model.decoder.layers   ,	snake_case__)
       except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
              copy_layers(teacher.encoder.block   ,	student.encoder.block   ,	snake_case__)
              copy_layers(teacher.decoder.block   ,	student.decoder.block   ,	snake_case__)
       logger.info(
           F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''')
       _A        =  {
           """teacher_type""": teacher.config.model_type,
           """copied_encoder_layers""": e_layers_to_copy,
           """copied_decoder_layers""": d_layers_to_copy,
       }
       student.save_pretrained(snake_case__)
       # Save information about copying for easier reproducibility

       return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
    fire.Fire(create_student_by_copying_alternating_layers)





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)






def  snake_case  (       snake_case__			:Union[str, Any]   ,	snake_case__			:Dict)						->   Any:
       _A        =  []
       for part_id in partition_order:
              _A        =  df.where(F'''SPARK_PARTITION_ID() = {part_id}''').collect()
              for row_idx, row in enumerate(snake_case__):
                     expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()))
       return expected_row_ids_and_row_dicts






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Optional[Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
       # that each partition can hold 2 rows.
       spark_builder._repartition_df_if_needed(max_shard_size=16)
       # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
       assert spark_builder.df.rdd.getNumPartitions() == 50






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(2)
       _A        =  [1, 0]
       _A        =  _generate_iterable_examples(snake_case__   ,	snake_case__)  # Reverse the partitions.
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	snake_case__)

       for i, (row_id, row_dict) in enumerate(generate_fn()):
              _A   ,  _A        =  expected_row_ids_and_row_dicts[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   int:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(1)
       _A        =  SparkExamplesIterable(snake_case__)
       assert it.n_shards == 1
       for i, (row_id, row_dict) in enumerate(snake_case__):
              assert row_id == F'''0_{i}'''
              assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(30).repartition(3)
       # Mock the generator so that shuffle reverses the partition indices.
       with patch("""numpy.random.Generator""") as generator_mock:
              _A        =  lambda snake_case__: x.reverse()
              _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[2, 1, 0])

              _A        =  SparkExamplesIterable(snake_case__).shuffle_data_sources(snake_case__)
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(snake_case__):
                     _A   ,  _A        =  expected_row_ids_and_row_dicts[i]
                     assert row_id == expected_row_id
                     assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   List[str]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(20).repartition(4)

       # Partitions 0 and 2
       _A        =  SparkExamplesIterable(snake_case__).shard_data_sources(worker_id=0   ,	num_workers=2)
       assert shard_it_a.n_shards == 2
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[0, 2])
       for i, (row_id, row_dict) in enumerate(snake_case__):
              _A   ,  _A        =  expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict

       # Partitions 1 and 3
       _A        =  SparkExamplesIterable(snake_case__).shard_data_sources(worker_id=1   ,	num_workers=2)
       assert shard_it_a.n_shards == 2
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[1, 3])
       for i, (row_id, row_dict) in enumerate(snake_case__):
              _A   ,  _A        =  expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Tuple:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # Choose a small max_shard_size for maximum partitioning.
       spark_builder._repartition_df_if_needed(max_shard_size=1)
       # The new number of partitions should not be greater than the number of rows.
       assert spark_builder.df.rdd.getNumPartitions() == 100




