




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


lowerCamelCase       :	List[Any]									=     logging.get_logger(__name__)


lowerCamelCase       :	Any									=     {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		'''codegen'''
 UpperCamelCase		     =		{
     '''max_position_embeddings''': '''n_positions''',
     '''hidden_size''': '''n_embd''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(       self      :						Optional[Any]		,							A_      :						List[str]=50400		,							A_      :						str=2048		,							A_      :						int=2048		,							A_      :						Union[str, Any]=4096		,							A_      :						str=28		,							A_      :						Optional[Any]=16		,							A_      :						Dict=64		,							A_      :						int=None		,							A_      :						Optional[Any]="gelu_new"		,							A_      :						List[Any]=0.0		,							A_      :						Dict=0.0		,							A_      :						Dict=0.0		,							A_      :						Dict=1E-5		,							A_      :						Union[str, Any]=0.02		,							A_      :						Optional[Any]=True		,							A_      :						Union[str, Any]=50256		,							A_      :						Optional[int]=50256		,							A_      :						Dict=False		,							**A_      :						Union[str, Any]		,							)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					vocab_size
       lowerCamelCase_	  =					n_ctx
       lowerCamelCase_	  =					n_positions
       lowerCamelCase_	  =					n_embd
       lowerCamelCase_	  =					n_layer
       lowerCamelCase_	  =					n_head
       lowerCamelCase_	  =					n_inner
       lowerCamelCase_	  =					rotary_dim
       lowerCamelCase_	  =					activation_function
       lowerCamelCase_	  =					resid_pdrop
       lowerCamelCase_	  =					embd_pdrop
       lowerCamelCase_	  =					attn_pdrop
       lowerCamelCase_	  =					layer_norm_epsilon
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					use_cache

       lowerCamelCase_	  =					bos_token_id
       lowerCamelCase_	  =					eos_token_id

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







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						PretrainedConfig		,							A_      :						str = "default"		,							A_      :						List[PatchingSpec] = None		,							A_      :						bool = False		,							)     ->			Optional[Any]:

       """simple docstring"""






       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?
             lowerCamelCase_	  =					0

 @property
 def        a__				(       self      :						Union[str, Any]						)     ->			Mapping[str, Mapping[int, str]]:

       """simple docstring"""






       lowerCamelCase_	  =					OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}						)
       if self.use_past:
             self.fill_with_past_key_values_(A_		,							direction='inputs'						)
             lowerCamelCase_	  =					{0: 'batch', 1: 'past_sequence + sequence'}
       else:
             lowerCamelCase_	  =					{0: 'batch', 1: 'sequence'}

       return common_inputs

 @property
 def        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       return self._config.n_layer

 @property
 def        a__				(       self      :						List[str]						)     ->			int:

       """simple docstring"""






       return self._config.n_head

 def        a__				(       self      :						Union[str, Any]		,							A_      :						PreTrainedTokenizer		,							A_      :						int = -1		,							A_      :						int = -1		,							A_      :						bool = False		,							A_      :						Optional[TensorType] = None		,							)     ->			Mapping[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					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()
       lowerCamelCase_	  =					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

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

       lowerCamelCase_	  =					common_inputs['attention_mask']
       if self.use_past:
             lowerCamelCase_	  =					ordered_inputs['attention_mask'].dtype
             lowerCamelCase_	  =					torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(A_		,							A_		,							dtype=A_						)]		,							dim=1						)

       return ordered_inputs





 @property
 def        a__				(       self      :						List[Any]						)     ->			int:

       """simple docstring"""






       return 13









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops

 from transformers import GradientAccumulator, create_optimizer







@require_tf
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						int						)     ->			Any:

       """simple docstring"""






       self.assertEqual(len(A_						)		,							len(A_						)						)
       for a, b in zip(A_		,							A_						):
             self.assertAlmostEqual(A_		,							A_		,							delta=A_						)

 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(A_						):
             accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step		,							3						)
       self.assertEqual(len(accumulator.gradients						)		,							1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[-2.0, 5.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[0.0, 0.0]		,							tol=1E-2						)





 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					None
       ops.enable_eager_execution_internal()
       lowerCamelCase_	  =					tf.config.list_physical_devices('CPU'						)
       if len(A_						) == 1:
             tf.config.set_logical_device_configuration(
                 physical_devices[0]		,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       lowerCamelCase_	  =					tf.config.list_logical_devices(device_type='CPU'						)
       lowerCamelCase_	  =					tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
             lowerCamelCase_	  =					GradientAccumulator()
             lowerCamelCase_	  =					tf.Variable([4.0, 3.0]						)
             lowerCamelCase_  ,       lowerCamelCase_	  =					create_optimizer(5E-5		,							10		,							5						)
             lowerCamelCase_	  =					tf.Variable([0.0, 0.0]		,							trainable=A_						)

       def accumulate_on_replica(A_      :						Any						):
             accumulator([gradient]						)

       def apply_on_replica():
             optimizer.apply_gradients(list(zip(accumulator.gradients		,							[variable]						)						)						)

       @tf.function
       def accumulate(A_      :						List[Any]		,							A_      :						Tuple						):
             with strategy.scope():
                   lowerCamelCase_	  =					strategy.experimental_local_results(A_						)
                   local_variables[0].assign(A_						)
                   local_variables[1].assign(A_						)
                   strategy.run(A_		,							args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
             with strategy.scope():
                   strategy.run(A_						)

       def _check_local_values(A_      :						List[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					strategy.experimental_local_results(accumulator._gradients[0]						)
             self.assertListAlmostEqual(values[0].value()		,							A_		,							tol=1E-2						)
             self.assertListAlmostEqual(values[1].value()		,							A_		,							tol=1E-2						)

       accumulate([1.0, 2.0]		,							[-1.0, 1.0]						)
       accumulate([3.0, -1.0]		,							[-1.0, -1.0]						)
       accumulate([-2.0, 2.0]		,							[3.0, -2.0]						)
       self.assertEqual(accumulator.step		,							3						)
       _check_local_values([2.0, 3.0]		,							[1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()		,							[4.0, 3.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       _check_local_values([0.0, 0.0]		,							[0.0, 0.0]						)



