from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}




class  __lowerCAmelCase	(							_lowerCamelCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						str					     =			'gpt2'
 _lowerCamelCase    :						List[Any]					     =			['past_key_values']
 _lowerCamelCase    :						Tuple					     =			{
     'hidden_size': 'n_embd',
     'max_position_embeddings': 'n_positions',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }

 def __init__(      self							:		Union[str, Any]							,      lowerCamelCase							:		Any=5_0_2_5_7							,      lowerCamelCase							:		int=1_0_2_4							,      lowerCamelCase							:		str=7_6_8							,      lowerCamelCase							:		Union[str, Any]=1_2							,      lowerCamelCase							:		Optional[int]=1_2							,      lowerCamelCase							:		Optional[int]=None							,      lowerCamelCase							:		Optional[int]="gelu_new"							,      lowerCamelCase							:		List[Any]=0.1							,      lowerCamelCase							:		Dict=0.1							,      lowerCamelCase							:		Tuple=0.1							,      lowerCamelCase							:		Optional[Any]=1e-5							,      lowerCamelCase							:		int=0.0_2							,      lowerCamelCase							:		List[str]="cls_index"							,      lowerCamelCase							:		Optional[Any]=True							,      lowerCamelCase							:		Tuple=None							,      lowerCamelCase							:		List[Any]=True							,      lowerCamelCase							:		Any=0.1							,      lowerCamelCase							:		Tuple=True							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		Optional[Any]=5_0_2_5_6							,      lowerCamelCase							:		Optional[Any]=5_0_2_5_6							,      lowerCamelCase							:		str=False							,      lowerCamelCase							:		List[str]=False							,      **lowerCamelCase							:		int							,      )			->				Any:
        _UpperCamelCase  :							List[str]	    =	vocab_size
        _UpperCamelCase  :							Optional[Any]	    =	n_positions
        _UpperCamelCase  :							str	    =	n_embd
        _UpperCamelCase  :							int	    =	n_layer
        _UpperCamelCase  :							Dict	    =	n_head
        _UpperCamelCase  :							int	    =	n_inner
        _UpperCamelCase  :							List[str]	    =	activation_function
        _UpperCamelCase  :							Any	    =	resid_pdrop
        _UpperCamelCase  :							Optional[int]	    =	embd_pdrop
        _UpperCamelCase  :							Dict	    =	attn_pdrop
        _UpperCamelCase  :							Optional[int]	    =	layer_norm_epsilon
        _UpperCamelCase  :							Optional[Any]	    =	initializer_range
        _UpperCamelCase  :							List[str]	    =	summary_type
        _UpperCamelCase  :							Any	    =	summary_use_proj
        _UpperCamelCase  :							List[Any]	    =	summary_activation
        _UpperCamelCase  :							Any	    =	summary_first_dropout
        _UpperCamelCase  :							Optional[Any]	    =	summary_proj_to_labels
        _UpperCamelCase  :							Any	    =	scale_attn_weights
        _UpperCamelCase  :							List[str]	    =	use_cache
        _UpperCamelCase  :							List[Any]	    =	scale_attn_by_inverse_layer_idx
        _UpperCamelCase  :							int	    =	reorder_and_upcast_attn

        _UpperCamelCase  :							Any	    =	bos_token_id
        _UpperCamelCase  :							Optional[Any]	    =	eos_token_id

        super().__init__(bos_token_id=__lowerCamelCase							,      eos_token_id=__lowerCamelCase							,      **__lowerCamelCase	)







class  __lowerCAmelCase	(							_lowerCamelCase    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[Any]							,      lowerCamelCase							:		PretrainedConfig							,      lowerCamelCase							:		str = "default"							,      lowerCamelCase							:		List[PatchingSpec] = None							,      lowerCamelCase							:		bool = False							,      )			->				Tuple:
        super().__init__(__lowerCamelCase							,      task=__lowerCamelCase							,      patching_specs=__lowerCamelCase							,      use_past=__lowerCamelCase	)
        if not getattr(self._config							,      'pad_token_id'							,      __lowerCamelCase	):
               # TODO: how to do that better?
               _UpperCamelCase  :							Tuple	    =	0

 @property
 def        __snake_case       (      self							:		Optional[Any]	)			->				Mapping[str, Mapping[int, str]]:
        _UpperCamelCase  :							List[Any]	    =	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}	)
        if self.use_past:
               self.fill_with_past_key_values_(__lowerCamelCase							,      direction='inputs'	)
               _UpperCamelCase  :							str	    =	{0: 'batch', 1: 'past_sequence + sequence'}
        else:
               _UpperCamelCase  :							Tuple	    =	{0: 'batch', 1: 'sequence'}

        return common_inputs

 @property
 def        __snake_case       (      self							:		Union[str, Any]	)			->				int:
        return self._config.n_layer

 @property
 def        __snake_case       (      self							:		Optional[int]	)			->				int:
        return self._config.n_head

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		PreTrainedTokenizer							,      lowerCamelCase							:		int = -1							,      lowerCamelCase							:		int = -1							,      lowerCamelCase							:		bool = False							,      lowerCamelCase							:		Optional[TensorType] = None							,      )			->				Mapping[str, Any]:
        _UpperCamelCase  :							str	    =	super(__lowerCamelCase							,      self	).generate_dummy_inputs(
            __lowerCamelCase							,      batch_size=__lowerCamelCase							,      seq_length=__lowerCamelCase							,      is_pair=__lowerCamelCase							,      framework=__lowerCamelCase	)

        # We need to order the input in the way they appears in the forward()
        _UpperCamelCase  :							str	    =	OrderedDict({'input_ids': common_inputs['input_ids']}	)

        # Need to add the past_keys
        if self.use_past:
               if not is_torch_available():
                      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
               else:
                      import torch

                      _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	common_inputs['input_ids'].shape
                      # Not using the same length for past_key_values
                      _UpperCamelCase  :							List[Any]	    =	seqlen + 2
                      _UpperCamelCase  :							int	    =	(
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      _UpperCamelCase  :							List[str]	    =	[
                          (torch.zeros(__lowerCamelCase	), torch.zeros(__lowerCamelCase	)) for _ in range(self.num_layers	)
                      ]

        _UpperCamelCase  :							Optional[Any]	    =	common_inputs['attention_mask']
        if self.use_past:
               _UpperCamelCase  :							List[str]	    =	ordered_inputs['attention_mask'].dtype
               _UpperCamelCase  :							List[str]	    =	torch.cat(
                   [ordered_inputs['attention_mask'], torch.ones(__lowerCamelCase							,      __lowerCamelCase							,      dtype=__lowerCamelCase	)]							,      dim=1	)

        return ordered_inputs

 @property
 def        __snake_case       (      self							:		Optional[int]	)			->				int:
        return 1_3











'''simple docstring'''





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch






def  lowerCamelCase_  (     ):
 lowerCamelCase__	=  Dataset.from_dict({"i": range(17)})
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  3
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(lowercase__) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






@pytest.mark.parametrize("shards_per_node"					,  [1, 2, 3])
def  lowerCamelCase_  (     lowercase__):
 def gen(lowercase__):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  shards_per_node * world_size
 lowerCamelCase__	=  {"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(lowercase__)]}
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__					,  gen_kwargs=lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 assert full_ds.n_shards == world_size * shards_per_node
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  2
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))

 lowerCamelCase__	=  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle(seed=42)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle()

 lowerCamelCase__	=  split_dataset_by_node(full_ds.shuffle(seed=42)					,  rank=0					,  world_size=lowercase__)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(full_ds.shuffle()					,  rank=0					,  world_size=lowercase__)






@pytest.mark.parametrize("streaming"					,  [False, True])
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  2
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())






@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,  [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,  )
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  True
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())

