



"""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


lowercase__    :  str							    =	logging.get_logger(__name__)

lowercase__    :  Union[str, Any]							    =	{
    """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        _UpperCAmelCase       (			_lowerCamelCase):
 _lowerCAmelCase   :			List[Any]							=		"""gptj"""
 _lowerCAmelCase   :			Optional[Any]							=		{
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }

 def __init__(     self		:       str  ,     lowercase_		:       List[Any]=50400  ,     lowercase_		:       Tuple=2048  ,     lowercase_		:       Optional[int]=4096  ,     lowercase_		:       Tuple=28  ,     lowercase_		:       List[Any]=16  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       str=None  ,     lowercase_		:       Tuple="gelu_new"  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       str=0.0  ,     lowercase_		:       str=0.0  ,     lowercase_		:       List[Any]=1E-5  ,     lowercase_		:       Tuple=0.02  ,     lowercase_		:       Tuple=True  ,     lowercase_		:       List[Any]=50256  ,     lowercase_		:       str=50256  ,     lowercase_		:       Union[str, Any]=False  ,     **lowercase_		:       List[str]  ,     ):
     snake_case_ :     Optional[int]				=	vocab_size
     snake_case_ :     int				=	n_positions
     snake_case_ :     Optional[Any]				=	n_embd
     snake_case_ :     List[Any]				=	n_layer
     snake_case_ :     Dict				=	n_head
     snake_case_ :     Dict				=	n_inner
     snake_case_ :     str				=	rotary_dim
     snake_case_ :     Any				=	activation_function
     snake_case_ :     Optional[Any]				=	resid_pdrop
     snake_case_ :     List[Any]				=	embd_pdrop
     snake_case_ :     Tuple				=	attn_pdrop
     snake_case_ :     Optional[Any]				=	layer_norm_epsilon
     snake_case_ :     Dict				=	initializer_range
     snake_case_ :     Optional[Any]				=	use_cache

     snake_case_ :     Union[str, Any]				=	bos_token_id
     snake_case_ :     Optional[int]				=	eos_token_id

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





class        _UpperCAmelCase       (			_lowerCamelCase):

 def __init__(     self		:       List[str]  ,     lowercase_		:       Tuple  ,     lowercase_		:       str = "default"  ,     lowercase_		:       Any = None  ,     lowercase_		:       List[Any] = False  ,     ):
     super().__init__(a_  ,     task=a_  ,     patching_specs=a_  ,     use_past=a_				)
     if not getattr(self._config  ,     '''pad_token_id'''  ,     a_				):
         # TODO: how to do that better?
         snake_case_ :     List[str]				=	0

 @property
 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     Any				=	OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}				)
     if self.use_past:
         self.fill_with_past_key_values_(a_  ,     direction='''inputs'''				)
         snake_case_ :     str				=	{0: """batch""", 1: """past_sequence + sequence"""}
     else:
         snake_case_ :     Any				=	{0: """batch""", 1: """sequence"""}

     return common_inputs

 @property
 def 						_snake_case					(     self		:       List[str]				):
     return self._config.n_layer

 @property
 def 						_snake_case					(     self		:       int				):
     return self._config.n_head

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       str  ,     lowercase_		:       str = -1  ,     lowercase_		:       Union[str, Any] = -1  ,     lowercase_		:       Dict = False  ,     lowercase_		:       List[Any] = None  ,     ):
     snake_case_ :     Dict				=	super(a_  ,     self				).generate_dummy_inputs(
         a_  ,     batch_size=a_  ,     seq_length=a_  ,     is_pair=a_  ,     framework=a_				)

     # We need to order the input in the way they appears in the forward()
     snake_case_ :     int				=	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

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

     snake_case_ :     int				=	common_inputs["""attention_mask"""]
     if self.use_past:
         snake_case_ :     int				=	ordered_inputs["""attention_mask"""].dtype
         snake_case_ :     List[Any]				=	torch.cat(
             [ordered_inputs['''attention_mask'''], torch.ones(a_  ,     a_  ,     dtype=a_				)]  ,     dim=1				)

     return ordered_inputs






 @property
 def 						_snake_case					(     self		:       Dict				):
     return 13










'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,dtype=tf.floataa  ,)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1E-4							)							)



