
"""simple docstring"""





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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Optional[int]						   =		'''gptj'''
		__lowercase							:  Optional[Any]						   =		{
		    '''max_position_embeddings''': '''n_positions''',
		    '''hidden_size''': '''n_embd''',
		    '''num_attention_heads''': '''n_head''',
		    '''num_hidden_layers''': '''n_layer''',
		}




		def __init__(     self							,  lowerCAmelCase__=5_0_4_0_0							,  lowerCAmelCase__=2_0_4_8							,  lowerCAmelCase__=4_0_9_6							,  lowerCAmelCase__=2_8							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=6_4							,  lowerCAmelCase__=None							,  lowerCAmelCase__="gelu_new"							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=1E-5							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=True							,  lowerCAmelCase__=5_0_2_5_6							,  lowerCAmelCase__=5_0_2_5_6							,  lowerCAmelCase__=False							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = n_positions
				__SCREAMING_SNAKE_CASE            = n_embd
				__SCREAMING_SNAKE_CASE            = n_layer
				__SCREAMING_SNAKE_CASE            = n_head
				__SCREAMING_SNAKE_CASE            = n_inner
				__SCREAMING_SNAKE_CASE            = rotary_dim
				__SCREAMING_SNAKE_CASE            = activation_function
				__SCREAMING_SNAKE_CASE            = resid_pdrop
				__SCREAMING_SNAKE_CASE            = embd_pdrop
				__SCREAMING_SNAKE_CASE            = attn_pdrop
				__SCREAMING_SNAKE_CASE            = layer_norm_epsilon
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = use_cache

				__SCREAMING_SNAKE_CASE            = bos_token_id
				__SCREAMING_SNAKE_CASE            = eos_token_id

				super().__init__(
				    bos_token_id=lowerCAmelCase__							,  eos_token_id=lowerCAmelCase__							,  tie_word_embeddings=lowerCAmelCase__							,  **lowerCAmelCase__)



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = "default"							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False							,  ):
				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?
						__SCREAMING_SNAKE_CASE            = 0




		@property
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
				if self.use_past:
						self.fill_with_past_key_values_(lowerCAmelCase__							,  direction="""inputs""")
						__SCREAMING_SNAKE_CASE            = {0: """batch""", 1: """past_sequence + sequence"""}
				else:
						__SCREAMING_SNAKE_CASE            = {0: """batch""", 1: """sequence"""}

				return common_inputs




		@property
		def 	snake_case_     (     self):
				return self._config.n_layer




		@property
		def 	snake_case_     (     self):
				return self._config.n_head




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = -1							,  lowerCAmelCase__ = -1							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  ):
				__SCREAMING_SNAKE_CASE            = 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()
				__SCREAMING_SNAKE_CASE            = 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

								__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = common_inputs["""input_ids"""].shape
								# Not using the same length for past_key_values
								__SCREAMING_SNAKE_CASE            = seqlen + 2
								__SCREAMING_SNAKE_CASE            = (
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								__SCREAMING_SNAKE_CASE            = [
								    (torch.zeros(lowerCAmelCase__), torch.zeros(lowerCAmelCase__)) for _ in range(self.num_layers)
								]

				__SCREAMING_SNAKE_CASE            = common_inputs["""attention_mask"""]
				if self.use_past:
						__SCREAMING_SNAKE_CASE            = ordered_inputs["""attention_mask"""].dtype
						__SCREAMING_SNAKE_CASE            = torch.cat(
						    [ordered_inputs["""attention_mask"""], torch.ones(lowerCAmelCase__							,  lowerCAmelCase__							,  dtype=lowerCAmelCase__)]							,  dim=1)

				return ordered_inputs




		@property
		def 	snake_case_     (     self):
				return 1_3






"""simple docstring"""





import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs



@pytest.mark.parametrize(
    """kwargs, expected"""  ,				[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10				)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(UpperCamelCase_  ,				i + 1				) for i in range(10				)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1				)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0  ,				4				), range(4  ,				7				), range(7  ,				10				)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0  ,				1				), range(1  ,				2				), range(2  ,				3				)]),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = _distribute_shards(**UpperCamelCase_				)
		assert out == expected



@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected"""  ,				[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = _split_gen_kwargs(UpperCamelCase_  ,				UpperCamelCase_				)
		assert out == expected



@pytest.mark.parametrize(
    """gen_kwargs, expected"""  ,				[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		if expected is RuntimeError:
				with pytest.raises(UpperCamelCase_				):
						_number_of_shards_in_gen_kwargs(UpperCamelCase_				)
		else:
				__SCREAMING_SNAKE_CASE            = _number_of_shards_in_gen_kwargs(UpperCamelCase_				)
				assert out == expected




