


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
				import torch







class 	__lowercase    (lowerCAmelCase__  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""char"""
							_UpperCAmelCase      =						"""bpe"""
							_UpperCAmelCase      =						"""wp"""







lowerCAmelCase__			:      int								=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class 	__lowercase    (lowerCAmelCase__  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""image_processor""", """char_tokenizer"""]
							_UpperCAmelCase      =						"""ViTImageProcessor"""
							_UpperCAmelCase      =						"""MgpstrTokenizer"""






							def __init__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
													    ' instead.'			,						__a			,						)
													SCREAMING_SNAKE_CASE_		:     Any					=					kwargs.pop('feature_extractor'       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError('You need to specify an `image_processor`.'       )
										if tokenizer is None:
													raise ValueError('You need to specify a `tokenizer`.'       )

										SCREAMING_SNAKE_CASE_		:     int					=					tokenizer
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					AutoTokenizer.from_pretrained('gpt2'       )
										SCREAMING_SNAKE_CASE_		:     Any					=					AutoTokenizer.from_pretrained('bert-base-uncased'       )

										super().__init__(__a			,						__a       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										if images is None and text is None:
													raise ValueError('You need to specify either an `images` or `text` input to process.'       )

										if images is not None:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					self.image_processor(__a			,						return_tensors=__a			,						**__a       )
										if text is not None:
													SCREAMING_SNAKE_CASE_		:     str					=					self.char_tokenizer(__a			,						return_tensors=__a			,						**__a       )

										if text is None:
													return inputs
										elif images is None:
													return encodings
										else:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					encodings['input_ids']
													return inputs






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					sequences
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					char_preds.size(0       )

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					self._decode_helper(__a			,						'char'       )
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					self._decode_helper(__a			,						'bpe'       )
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					self._decode_helper(__a			,						'wp'       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[]
										SCREAMING_SNAKE_CASE_		:     Tuple					=					[]
										for i in range(__a       ):
													SCREAMING_SNAKE_CASE_		:     int					=					[char_scores[i], bpe_scores[i], wp_scores[i]]
													SCREAMING_SNAKE_CASE_		:     str					=					[char_strs[i], bpe_strs[i], wp_strs[i]]
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					scores.index(max(__a       )       )
													final_strs.append(strs[max_score_index]       )
													final_scores.append(scores[max_score_index]       )

										SCREAMING_SNAKE_CASE_		:     str					=					{}
										SCREAMING_SNAKE_CASE_		:     List[str]					=					final_strs
										SCREAMING_SNAKE_CASE_		:     Dict					=					final_scores
										SCREAMING_SNAKE_CASE_		:     Tuple					=					char_strs
										SCREAMING_SNAKE_CASE_		:     int					=					bpe_strs
										SCREAMING_SNAKE_CASE_		:     int					=					wp_strs
										return out






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if format == DecodeType.CHARACTER:
													SCREAMING_SNAKE_CASE_		:     Any					=					self.char_decode
													SCREAMING_SNAKE_CASE_		:     Dict					=					1
													SCREAMING_SNAKE_CASE_		:     str					=					'[s]'
										elif format == DecodeType.BPE:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.bpe_decode
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					2
													SCREAMING_SNAKE_CASE_		:     Tuple					=					'#'
										elif format == DecodeType.WORDPIECE:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.wp_decode
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					1_0_2
													SCREAMING_SNAKE_CASE_		:     Any					=					'[SEP]'
										else:
													raise ValueError(F'''Format {format} is not supported.'''       )

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[], []
										SCREAMING_SNAKE_CASE_		:     int					=					pred_logits.size(0       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pred_logits.size(1       )
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pred_logits.topk(1			,						dim=-1			,						largest=__a			,						sorted=__a       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					preds_index.view(-1			,						__a       )[:, 1:]
										SCREAMING_SNAKE_CASE_		:     str					=					decoder(__a       )
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					torch.nn.functional.softmax(__a			,						dim=2       ).max(dim=2       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					preds_max_prob[:, 1:]

										for index in range(__a       ):
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					preds_str[index].find(__a       )
													SCREAMING_SNAKE_CASE_		:     str					=					preds_str[index][:pred_eos]
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					preds_index[index].cpu().tolist()
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pred_index.index(__a       ) if eos_token in pred_index else -1
													SCREAMING_SNAKE_CASE_		:     Tuple					=					preds_max_prob[index][: pred_eos_index + 1]
													SCREAMING_SNAKE_CASE_		:     Any					=					pred_max_prob.cumprod(dim=0       )[-1] if pred_max_prob.nelement() != 0 else 0.0
													dec_strs.append(__a       )
													conf_scores.append(__a       )

										return dec_strs, conf_scores






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[seq.replace(' '			,						''       ) for seq in self.char_tokenizer.batch_decode(__a       )]
										return decode_strs






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										return self.bpe_tokenizer.batch_decode(__a       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[seq.replace(' '			,						''       ) for seq in self.wp_tokenizer.batch_decode(__a       )]
										return decode_strs









"""simple docstring"""


import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    FalconForCausalLM,
	    FalconForQuestionAnswering,
	    FalconForSequenceClassification,
	    FalconForTokenClassification,
	    FalconModel,
	)
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a=3       ,  __a=7       ,  __a=True       ,  __a=True       ,  __a=False       ,  __a=True       ,  __a=99       ,  __a=32       ,  __a=5       ,  __a=4       ,  __a=37       ,  __a="gelu"       ,  __a=0.1       ,  __a=0.1       ,  __a=5_12       ,  __a=16       ,  __a=2       ,  __a=0.0_2       ,  __a=3       ,  __a=4       ,  __a=None       ,  ):
										__lowerCAmelCase							       =     parent
										__lowerCAmelCase							       =     batch_size
										__lowerCAmelCase							       =     seq_length
										__lowerCAmelCase							       =     is_training
										__lowerCAmelCase							       =     use_input_mask
										__lowerCAmelCase							       =     use_token_type_ids
										__lowerCAmelCase							       =     use_labels
										__lowerCAmelCase							       =     vocab_size
										__lowerCAmelCase							       =     hidden_size
										__lowerCAmelCase							       =     num_hidden_layers
										__lowerCAmelCase							       =     num_attention_heads
										__lowerCAmelCase							       =     intermediate_size
										__lowerCAmelCase							       =     hidden_act
										__lowerCAmelCase							       =     hidden_dropout_prob
										__lowerCAmelCase							       =     attention_probs_dropout_prob
										__lowerCAmelCase							       =     max_position_embeddings
										__lowerCAmelCase							       =     type_vocab_size
										__lowerCAmelCase							       =     type_sequence_label_size
										__lowerCAmelCase							       =     initializer_range
										__lowerCAmelCase							       =     num_labels
										__lowerCAmelCase							       =     num_choices
										__lowerCAmelCase							       =     scope

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size      )

										__lowerCAmelCase							       =     None
										if self.use_input_mask:
													__lowerCAmelCase							       =     random_attention_mask([self.batch_size, self.seq_length]      )

										__lowerCAmelCase							       =     None

										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     None
										if self.use_labels:
													__lowerCAmelCase							       =     ids_tensor([self.batch_size]       ,  self.type_sequence_label_size      )
													__lowerCAmelCase							       =     ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels      )
													__lowerCAmelCase							       =     ids_tensor([self.batch_size]       ,  self.num_choices      )

										__lowerCAmelCase							       =     self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

							def      snake_case							(	self      ):
										return FalconConfig(
										    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       ,  is_decoder=__a       ,  initializer_range=self.initializer_range       ,  pad_token_id=1       ,  new_decoder_architecture=__a       ,  )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     FalconModel(config=__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a      )
										__lowerCAmelCase							       =     model(__a      )
										self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  ):
										__lowerCAmelCase							       =     True
										__lowerCAmelCase							       =     FalconModel(__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(
										    __a       ,  attention_mask=__a       ,  encoder_hidden_states=__a       ,  encoder_attention_mask=__a       ,  )
										__lowerCAmelCase							       =     model(
										    __a       ,  attention_mask=__a       ,  encoder_hidden_states=__a       ,  )
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a      )
										self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  ):
										__lowerCAmelCase							       =     FalconForCausalLM(config=__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a       ,  labels=__a      )
										self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  ):
										__lowerCAmelCase							       =     True
										__lowerCAmelCase							       =     True
										__lowerCAmelCase							       =     FalconForCausalLM(config=__a      )
										model.to(__a      )
										model.eval()

										# first forward pass
										__lowerCAmelCase							       =     model(
										    __a       ,  attention_mask=__a       ,  encoder_hidden_states=__a       ,  encoder_attention_mask=__a       ,  use_cache=__a       ,  )
										__lowerCAmelCase							       =     outputs.past_key_values

										# create hypothetical multiple next token and extent to next_input_ids
										__lowerCAmelCase							       =     ids_tensor((self.batch_size, 3)       ,  config.vocab_size      )
										__lowerCAmelCase							       =     ids_tensor((self.batch_size, 3)       ,  vocab_size=2      )

										# append to next input_ids and
										__lowerCAmelCase							       =     torch.cat([input_ids, next_tokens]       ,  dim=-1      )
										__lowerCAmelCase							       =     torch.cat([input_mask, next_mask]       ,  dim=-1      )

										__lowerCAmelCase							       =     model(
										    __a       ,  attention_mask=__a       ,  encoder_hidden_states=__a       ,  encoder_attention_mask=__a       ,  output_hidden_states=__a       ,  )["hidden_states"][0]
										__lowerCAmelCase							       =     model(
										    __a       ,  attention_mask=__a       ,  encoder_hidden_states=__a       ,  encoder_attention_mask=__a       ,  past_key_values=__a       ,  output_hidden_states=__a       ,  )["hidden_states"][0]

										# select random slice
										__lowerCAmelCase							       =     ids_tensor((1,)       ,  output_from_past.shape[-1]      ).item()
										__lowerCAmelCase							       =     output_from_no_past[:, -3:, random_slice_idx].detach()
										__lowerCAmelCase							       =     output_from_past[:, :, random_slice_idx].detach()

										self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(__a       ,  __a       ,  atol=1e-3      )      )



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.prepare_config_and_inputs()
										(
										    (
										    __lowerCAmelCase
										)       ,      (
										    __lowerCAmelCase
										)       ,      (
										    __lowerCAmelCase
										)       ,      (
										    __lowerCAmelCase
										)       ,      (
										    __lowerCAmelCase
										)       ,      (
										    __lowerCAmelCase
										)       ,      (
										    __lowerCAmelCase
										)       ,      
										)							       =     config_and_inputs
										__lowerCAmelCase							       =     {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict
@require_torch
class     _UpperCamelCase    (  lowerCAmelCase__			,lowerCAmelCase__			,lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 =(
							    (
							        FalconModel,
							        FalconForCausalLM,
							        FalconForSequenceClassification,
							        FalconForTokenClassification,
							        FalconForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase		:       Union[str, Any]							 =(FalconForCausalLM,) if is_torch_available() else ()
							__UpperCAmelCase		:       List[Any]							 =(
							    {
							        """feature-extraction""": FalconModel,
							        """text-classification""": FalconForSequenceClassification,
							        """text-generation""": FalconForCausalLM,
							        """question-answering""": FalconForQuestionAnswering,
							        """token-classification""": FalconForTokenClassification,
							        """zero-shot""": FalconForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__UpperCAmelCase		:       Any							 =False
							__UpperCAmelCase		:       Tuple							 =False

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     FalconModelTester(self      )
										__lowerCAmelCase							       =     ConfigTester(self       ,  config_class=__a       ,  hidden_size=37      )

							def      snake_case							(	self      ):
										self.config_tester.run_common_tests()

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase       ,      *__lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs()
										for alibi in [True, False]:
													__lowerCAmelCase							       =     alibi
													self.model_tester.create_and_check_model(__a       ,  *__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase							       =     3
										__lowerCAmelCase							       =     input_dict["input_ids"]
										__lowerCAmelCase							       =     input_ids.ne(1      ).to(__a      )
										__lowerCAmelCase							       =     ids_tensor([self.model_tester.batch_size]       ,  self.model_tester.type_sequence_label_size      )
										__lowerCAmelCase							       =     FalconForSequenceClassification(__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a       ,  labels=__a      )
										self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)      )

							def      snake_case							(	self      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase							       =     3
										__lowerCAmelCase							       =     "single_label_classification"
										__lowerCAmelCase							       =     input_dict["input_ids"]
										__lowerCAmelCase							       =     input_ids.ne(1      ).to(__a      )
										__lowerCAmelCase							       =     ids_tensor([self.model_tester.batch_size]       ,  self.model_tester.type_sequence_label_size      )
										__lowerCAmelCase							       =     FalconForSequenceClassification(__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a       ,  labels=__a      )
										self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)      )

							def      snake_case							(	self      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase							       =     input_dict["input_ids"]
										__lowerCAmelCase							       =     FalconForCausalLM(__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  use_cache=__a      )
										__lowerCAmelCase							       =     input_ids.shape[0]
										__lowerCAmelCase							       =     model._convert_to_rw_cache(result.past_key_values      )
										__lowerCAmelCase							       =     model._convert_cache_to_standard_format(__a       ,  __a      )
										for layer in range(len(__a      )      ):
													for tensor_idx in range(2      ):
																self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3      )
																self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4      )
																self.assertTrue(
																    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]      )      )

							def      snake_case							(	self      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase							       =     3
										__lowerCAmelCase							       =     "multi_label_classification"
										__lowerCAmelCase							       =     input_dict["input_ids"]
										__lowerCAmelCase							       =     input_ids.ne(1      ).to(__a      )
										__lowerCAmelCase							       =     ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]       ,  self.model_tester.type_sequence_label_size      ).to(torch.float      )
										__lowerCAmelCase							       =     FalconForSequenceClassification(__a      )
										model.to(__a      )
										model.eval()
										__lowerCAmelCase							       =     model(__a       ,  attention_mask=__a       ,  labels=__a      )
										self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)      )



							def      snake_case							(	self      ):
										# Falcon can have different numbers of KV-heads than the number of query heads, so we need
										# to override this test to use the right head counts.
										for model_class in self.all_generative_model_classes:
													__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.model_tester.prepare_config_and_inputs_for_common()

													# If it doesn't support cache, pass the test
													if not hasattr(__a       ,  "use_cache"      ):
																return

													__lowerCAmelCase							       =     model_class(__a      ).to(__a      )
													if "use_cache" not in inputs:
																__lowerCAmelCase							       =     True
													__lowerCAmelCase							       =     model(**__a      )

													# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
													if "past_key_values" not in outputs:
																return

													__lowerCAmelCase							       =     (
													    getattr(__a       ,  "decoder_layers"       ,  __a      )
													    or getattr(__a       ,  "num_decoder_layers"       ,  __a      )
													    or config.num_hidden_layers
													)
													__lowerCAmelCase							       =     getattr(__a       ,  "num_kv_heads"       ,  config.num_attention_heads      )
													__lowerCAmelCase							       =     getattr(__a       ,  "d_model"       ,  config.hidden_size      )
													__lowerCAmelCase							       =     embed_dim // num_attention_heads

													__lowerCAmelCase							       =     outputs["past_key_values"]
													self.assertEqual(len(__a      )       ,  __a      )

													__lowerCAmelCase       ,      __lowerCAmelCase							       =     inputs["input_ids"].shape
													for i in range(__a      ):
																if config.new_decoder_architecture:
																			__lowerCAmelCase							       =     config.num_attention_heads
																elif config.multi_query:
																			__lowerCAmelCase							       =     1
																self.assertEqual(len(past_kv[0]      )       ,  2      )  # K V for the decoder = 2
																self.assertEqual(
																    past_kv[i][0].shape       ,  (batch_size, num_attention_heads, seq_length, per_head_embed_dim)      )
																self.assertEqual(
																    past_kv[i][1].shape       ,  (batch_size, num_attention_heads, seq_length, per_head_embed_dim)      )
@require_torch
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							@slow
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b"      )
										__lowerCAmelCase							       =     FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b"      )
										model.eval()
										model.to(__a      )
										__lowerCAmelCase							       =     tokenizer("My favorite food is"       ,  return_tensors="pt"      ).to(__a      )

										__lowerCAmelCase							       =     (
										    "My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
										)

										__lowerCAmelCase							       =     model.generate(**__a       ,  do_sample=__a       ,  max_new_tokens=19      )
										__lowerCAmelCase							       =     tokenizer.batch_decode(__a      )[0]

										self.assertEqual(__a       ,  __a      )

							@slow
							def      snake_case							(	self      ):
										# The big models are way too big for the CI, so we use tiny random models that resemble their
										# architectures but with much smaller and fewer layers
										for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
													__lowerCAmelCase							       =     AutoTokenizer.from_pretrained(__a      )
													__lowerCAmelCase							       =     FalconForCausalLM.from_pretrained(__a      )
													model.eval()
													model.to(__a      )
													__lowerCAmelCase							       =     tokenizer("My favorite food is"       ,  return_tensors="pt"      ).to(__a      )

													# We just test that these run without errors - the models are randomly initialized
													# and so the actual text outputs will be garbage
													model.generate(**__a       ,  do_sample=__a       ,  max_new_tokens=4      )
													model.generate(**__a       ,  do_sample=__a       ,  max_new_tokens=4      )
													model.generate(**__a       ,  num_beams=2       ,  max_new_tokens=4      )



							@slow
							def      snake_case							(	self      ):
										# The big models are way too big for the CI, so we use tiny random models that resemble their
										# architectures but with much smaller and fewer layers
										with torch.no_grad():
													for repo in [
													    "Rocketknight1/falcon-rw-1b",
													    "Rocketknight1/tiny-random-falcon-7b",
													    "Rocketknight1/tiny-random-falcon-40b",
													]:
																__lowerCAmelCase							       =     AutoTokenizer.from_pretrained(__a      )
																__lowerCAmelCase							       =     FalconForCausalLM.from_pretrained(__a      )
																model.eval()
																model.to(device=__a      )
																__lowerCAmelCase							       =     tokenizer("My favorite food is"       ,  return_tensors="pt"      ).to(__a      )

																# Test results are the same with and without cache
																__lowerCAmelCase							       =     model.generate(**__a       ,  do_sample=__a       ,  max_new_tokens=20       ,  use_cache=__a      )
																__lowerCAmelCase							       =     model.generate(**__a       ,  do_sample=__a       ,  max_new_tokens=20       ,  use_cache=__a      )
																self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0      )




