



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.layoutlm.modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMModel,
    )



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=13,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=99,			SCREAMING_SNAKE_CASE_=32,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=37,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=512,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=1000,			)     ->		List[str]:
          UpperCamelCase    :  Optional[Any]							=	parent
          UpperCamelCase    :  Tuple							=	batch_size
          UpperCamelCase    :  Any							=	seq_length
          UpperCamelCase    :  Dict							=	is_training
          UpperCamelCase    :  str							=	use_input_mask
          UpperCamelCase    :  Optional[Any]							=	use_token_type_ids
          UpperCamelCase    :  Optional[Any]							=	use_labels
          UpperCamelCase    :  Dict							=	vocab_size
          UpperCamelCase    :  Optional[Any]							=	hidden_size
          UpperCamelCase    :  List[Any]							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  List[Any]							=	intermediate_size
          UpperCamelCase    :  Any							=	hidden_act
          UpperCamelCase    :  Tuple							=	hidden_dropout_prob
          UpperCamelCase    :  List[str]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Union[str, Any]							=	max_position_embeddings
          UpperCamelCase    :  Any							=	type_vocab_size
          UpperCamelCase    :  str							=	type_sequence_label_size
          UpperCamelCase    :  int							=	initializer_range
          UpperCamelCase    :  List[Any]							=	num_labels
          UpperCamelCase    :  Optional[Any]							=	num_choices
          UpperCamelCase    :  Tuple							=	scope
          UpperCamelCase    :  List[Any]							=	range_bbox
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[Any]							=	ids_tensor([self.batch_size, self.seq_length],			self.vocab_size       )

          # convert bbox to numpy since TF does not support item assignment
          UpperCamelCase    :  Dict							=	ids_tensor([self.batch_size, self.seq_length, 4],			self.range_bbox       ).numpy()
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]       ):
             for j in range(bbox.shape[1]       ):
                if bbox[i, j, 3] < bbox[i, j, 1]:
                   UpperCamelCase    :  Optional[Any]							=	bbox[i, j, 3]
                   UpperCamelCase    :  Dict							=	bbox[i, j, 1]
                   UpperCamelCase    :  List[str]							=	t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                   UpperCamelCase    :  List[Any]							=	bbox[i, j, 2]
                   UpperCamelCase    :  Any							=	bbox[i, j, 0]
                   UpperCamelCase    :  List[str]							=	t
          UpperCamelCase    :  List[str]							=	tf.convert_to_tensor(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	None
          if self.use_input_mask:
             UpperCamelCase    :  Optional[int]							=	random_attention_mask([self.batch_size, self.seq_length]       )

          UpperCamelCase    :  Tuple							=	None
          if self.use_token_type_ids:
             UpperCamelCase    :  List[Any]							=	ids_tensor([self.batch_size, self.seq_length],			self.type_vocab_size       )

          UpperCamelCase    :  Optional[int]							=	None
          UpperCamelCase    :  Optional[int]							=	None
          UpperCamelCase    :  int							=	None
          if self.use_labels:
             UpperCamelCase    :  Any							=	ids_tensor([self.batch_size],			self.type_sequence_label_size       )
             UpperCamelCase    :  List[Any]							=	ids_tensor([self.batch_size, self.seq_length],			self.num_labels       )
             UpperCamelCase    :  Any							=	ids_tensor([self.batch_size],			self.num_choices       )

          UpperCamelCase    :  Optional[int]							=	LayoutLMConfig(
              vocab_size=self.vocab_size,			hidden_size=self.hidden_size,			num_hidden_layers=self.num_hidden_layers,			num_attention_heads=self.num_attention_heads,			intermediate_size=self.intermediate_size,			hidden_act=self.hidden_act,			hidden_dropout_prob=self.hidden_dropout_prob,			attention_probs_dropout_prob=self.attention_probs_dropout_prob,			max_position_embeddings=self.max_position_embeddings,			type_vocab_size=self.type_vocab_size,			initializer_range=self.initializer_range,			)

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		int:
          UpperCamelCase    :  Optional[int]							=	TFLayoutLMModel(config=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  int							=	model(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	model(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Tuple							=	model(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)       )
          self.parent.assertEqual(result.pooler_output.shape,			(self.batch_size, self.hidden_size)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          UpperCamelCase    :  Optional[int]							=	TFLayoutLMForMaskedLM(config=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	model(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_,			labels=SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.vocab_size)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[int]							=	self.num_labels
          UpperCamelCase    :  Optional[Any]							=	TFLayoutLMForSequenceClassification(config=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	model(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  Union[str, Any]							=	self.num_labels
          UpperCamelCase    :  Dict							=	TFLayoutLMForTokenClassification(config=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	model(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_,			labels=SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.num_labels)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  Union[str, Any]							=	TFLayoutLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	model(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_       )
          self.parent.assertEqual(result.start_logits.shape,			(self.batch_size, self.seq_length)       )
          self.parent.assertEqual(result.end_logits.shape,			(self.batch_size, self.seq_length)       )






       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Dict							=	self.prepare_config_and_inputs()
          (
              (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     (
              UpperCamelCase
          )	,     
          )    :  List[str]							=	config_and_inputs
          UpperCamelCase    :  List[str]							=	{
              'input_ids': input_ids,
              'bbox': bbox,
              'token_type_ids': token_type_ids,
              'attention_mask': input_mask,
          }
          return config, inputs_dict






@require_tf
class 				lowerCAmelCase_		(  a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					List[str]        =  (
           (
               TFLayoutLMModel,
               TFLayoutLMForMaskedLM,
               TFLayoutLMForTokenClassification,
               TFLayoutLMForSequenceClassification,
               TFLayoutLMForQuestionAnswering,
           )
           if is_tf_available()
           else ()
       )
       UpperCAmelCase__				:					List[Any]        =  (
           {
               "feature-extraction": TFLayoutLMModel,
               "fill-mask": TFLayoutLMForMaskedLM,
               "text-classification": TFLayoutLMForSequenceClassification,
               "token-classification": TFLayoutLMForTokenClassification,
               "zero-shot": TFLayoutLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       UpperCAmelCase__				:					Dict        =  False
       UpperCAmelCase__				:					Tuple        =  True
       UpperCAmelCase__				:					Optional[int]        =  10
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Optional[Any]							=	TFLayoutLMModelTester(self       )
          UpperCamelCase    :  Union[str, Any]							=	ConfigTester(self,			config_class=SCREAMING_SNAKE_CASE_,			hidden_size=37       )
       def 					snake_case_       (			self       )     ->		Any:
          self.config_tester.run_common_tests()
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  int							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Optional[Any]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  str							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  str							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_       )
       @slow
       def 					snake_case_       (			self       )     ->		Optional[int]:
          for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase    :  Dict							=	TFLayoutLMModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )






       @unittest.skip('Onnx compliancy broke with TF 2.10'       )
       def 					snake_case_       (			self       )     ->		int:
          pass






def 		UpperCamelCase	(   )	->     int:
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   UpperCamelCase    :  Optional[int]							=	tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]	)  # noqa: E231
   UpperCamelCase    :  str							=	tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	)  # noqa: E231
   UpperCamelCase    :  Dict							=	tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]	)  # noqa: E231
   UpperCamelCase    :  Optional[Any]							=	tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   UpperCamelCase    :  Optional[Any]							=	tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]	)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels



@require_tf
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Tuple							=	TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'       )

          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase    :  Any							=	model(input_ids=SCREAMING_SNAKE_CASE_,			bbox=SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_       )

          # test the sequence output on [0, :3, :3]
          UpperCamelCase    :  Optional[int]							=	tf.convert_to_tensor(
              [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]],			)

          self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3],			SCREAMING_SNAKE_CASE_,			atol=1e-3       )       )

          # test the pooled output on [1, :3]
          UpperCamelCase    :  str							=	tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]       )

          self.assertTrue(np.allclose(outputs.pooler_output[1, :3],			SCREAMING_SNAKE_CASE_,			atol=1e-3       )       )
       @slow
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          # initialize model with randomly initialized sequence classification head
          UpperCamelCase    :  List[Any]							=	TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased',			num_labels=2       )

          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Tuple							=	prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase    :  str							=	model(
              input_ids=SCREAMING_SNAKE_CASE_,			bbox=SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_,			labels=tf.convert_to_tensor([1, 1]       ),			)

          # test whether we get a loss as a scalar
          UpperCamelCase    :  Any							=	outputs.loss
          UpperCamelCase    :  List[str]							=	(2,)
          self.assertEqual(loss.shape,			SCREAMING_SNAKE_CASE_       )

          # test the shape of the logits
          UpperCamelCase    :  Any							=	outputs.logits
          UpperCamelCase    :  Optional[Any]							=	(2, 2)
          self.assertEqual(logits.shape,			SCREAMING_SNAKE_CASE_       )
       @slow
       def 					snake_case_       (			self       )     ->		List[Any]:
          # initialize model with randomly initialized token classification head
          UpperCamelCase    :  Tuple							=	TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased',			num_labels=13       )

          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  List[Any]							=	prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase    :  Optional[Any]							=	model(
              input_ids=SCREAMING_SNAKE_CASE_,			bbox=SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_,			labels=SCREAMING_SNAKE_CASE_       )

          # test the shape of the logits
          UpperCamelCase    :  Optional[int]							=	outputs.logits
          UpperCamelCase    :  Dict							=	tf.convert_to_tensor((2, 25, 13)       )
          self.assertEqual(logits.shape,			SCREAMING_SNAKE_CASE_       )






       @slow
       def 					snake_case_       (			self       )     ->		Dict:
          # initialize model with randomly initialized token classification head
          UpperCamelCase    :  str							=	TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'       )

          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Dict							=	prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase    :  Optional[int]							=	model(input_ids=SCREAMING_SNAKE_CASE_,			bbox=SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			token_type_ids=SCREAMING_SNAKE_CASE_       )

          # test the shape of the logits
          UpperCamelCase    :  List[str]							=	tf.convert_to_tensor((2, 25)       )
          self.assertEqual(outputs.start_logits.shape,			SCREAMING_SNAKE_CASE_       )
          self.assertEqual(outputs.end_logits.shape,			SCREAMING_SNAKE_CASE_       )








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          =       {
    '''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 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					List[str]        =  "codegen"
       UpperCAmelCase__				:					str        =  {
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=5_0400,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4096,			SCREAMING_SNAKE_CASE_=28,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_="gelu_new",			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=False,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  Tuple							=	vocab_size
          UpperCamelCase    :  Optional[int]							=	n_ctx
          UpperCamelCase    :  Optional[int]							=	n_positions
          UpperCamelCase    :  List[str]							=	n_embd
          UpperCamelCase    :  Dict							=	n_layer
          UpperCamelCase    :  int							=	n_head
          UpperCamelCase    :  Union[str, Any]							=	n_inner
          UpperCamelCase    :  int							=	rotary_dim
          UpperCamelCase    :  Optional[Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	resid_pdrop
          UpperCamelCase    :  Union[str, Any]							=	embd_pdrop
          UpperCamelCase    :  Optional[Any]							=	attn_pdrop
          UpperCamelCase    :  List[str]							=	layer_norm_epsilon
          UpperCamelCase    :  Union[str, Any]							=	initializer_range
          UpperCamelCase    :  str							=	use_cache

          UpperCamelCase    :  Dict							=	bos_token_id
          UpperCamelCase    :  Union[str, Any]							=	eos_token_id

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






class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = "default",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			)     ->		List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_,			task=SCREAMING_SNAKE_CASE_,			patching_specs=SCREAMING_SNAKE_CASE_,			use_past=SCREAMING_SNAKE_CASE_       )
          if not getattr(self._config,			'pad_token_id',			SCREAMING_SNAKE_CASE_       ):
             # TODO: how to do that better?
             UpperCamelCase    :  str							=	0
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  Tuple							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs'       )
             UpperCamelCase    :  List[Any]							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  Optional[int]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_layer
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_head
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Mapping[str, Any]:
          UpperCamelCase    :  Tuple							=	super(SCREAMING_SNAKE_CASE_,			self       ).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,			batch_size=SCREAMING_SNAKE_CASE_,			seq_length=SCREAMING_SNAKE_CASE_,			is_pair=SCREAMING_SNAKE_CASE_,			framework=SCREAMING_SNAKE_CASE_       )

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase    :  Optional[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

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

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

          return ordered_inputs






       @property
       def 					snake_case_       (			self       )     ->		int:
          return 13



