





"""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 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Optional[Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Dict=3		, lowerCamelCase_    :str=7		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :int=False		, lowerCamelCase_    :Optional[Any]=True		, lowerCamelCase_    :List[Any]=99		, lowerCamelCase_    :Union[str, Any]=32		, lowerCamelCase_    :Optional[Any]=5		, lowerCamelCase_    :Any=4		, lowerCamelCase_    :Tuple=37		, lowerCamelCase_    :Optional[int]="gelu"		, lowerCamelCase_    :int=0.1		, lowerCamelCase_    :Tuple=0.1		, lowerCamelCase_    :List[str]=5_12		, lowerCamelCase_    :Any=16		, lowerCamelCase_    :Any=2		, lowerCamelCase_    :int=0.0_2		, lowerCamelCase_    :Dict=3		, lowerCamelCase_    :List[Any]=4		, lowerCamelCase_    :Optional[int]=None		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= parent
			SCREAMING_SNAKE_CASE       :     Tuple				= batch_size
			SCREAMING_SNAKE_CASE       :     Tuple				= seq_length
			SCREAMING_SNAKE_CASE       :     Any				= is_training
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_input_mask
			SCREAMING_SNAKE_CASE       :     str				= use_token_type_ids
			SCREAMING_SNAKE_CASE       :     Dict				= use_labels
			SCREAMING_SNAKE_CASE       :     List[str]				= vocab_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= hidden_size
			SCREAMING_SNAKE_CASE       :     str				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     List[str]				= hidden_act
			SCREAMING_SNAKE_CASE       :     List[Any]				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     Dict				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     int				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= type_vocab_size
			SCREAMING_SNAKE_CASE       :     Any				= type_sequence_label_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= initializer_range
			SCREAMING_SNAKE_CASE       :     Tuple				= num_labels
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_choices
			SCREAMING_SNAKE_CASE       :     List[Any]				= scope





	def   __lowerCAmelCase (						self    :int					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size					)

			SCREAMING_SNAKE_CASE       :     List[str]				= None
			if self.use_input_mask:
					SCREAMING_SNAKE_CASE       :     Tuple				= random_attention_mask([self.batch_size, self.seq_length]					)

			SCREAMING_SNAKE_CASE       :     Tuple				= None

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

			SCREAMING_SNAKE_CASE       :     Any				= self.get_config()

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





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Tuple:







			'''simple docstring'''




			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=lowerCAmelCase__		, initializer_range=self.initializer_range		, pad_token_id=1		, new_decoder_architecture=lowerCAmelCase__		, )





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :str		, lowerCamelCase_    :Union[str, Any]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= FalconModel(config=lowerCAmelCase__					)
			model.to(lowerCAmelCase__					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     List[str]				= model(lowerCAmelCase__		, attention_mask=lowerCAmelCase__					)
			SCREAMING_SNAKE_CASE       :     Dict				= model(lowerCAmelCase__					)
			self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)					)





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :str		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :int		, lowerCamelCase_    :Union[str, Any]		, )							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= True
			SCREAMING_SNAKE_CASE       :     List[str]				= FalconModel(lowerCAmelCase__					)
			model.to(lowerCAmelCase__					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     List[Any]				= model(
			    lowerCAmelCase__		, attention_mask=lowerCAmelCase__		, encoder_hidden_states=lowerCAmelCase__		, encoder_attention_mask=lowerCAmelCase__		, )
			SCREAMING_SNAKE_CASE       :     str				= model(
			    lowerCAmelCase__		, attention_mask=lowerCAmelCase__		, encoder_hidden_states=lowerCAmelCase__		, )
			SCREAMING_SNAKE_CASE       :     Tuple				= model(lowerCAmelCase__		, attention_mask=lowerCAmelCase__					)
			self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)					)





	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :int		, lowerCamelCase_    :Any		, lowerCamelCase_    :Dict		, )							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= FalconForCausalLM(config=lowerCAmelCase__					)
			model.to(lowerCAmelCase__					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     int				= model(lowerCAmelCase__		, attention_mask=lowerCAmelCase__		, labels=lowerCAmelCase__					)
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size)					)





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :str		, lowerCamelCase_    :Dict		, )							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= True
			SCREAMING_SNAKE_CASE       :     List[str]				= True
			SCREAMING_SNAKE_CASE       :     int				= FalconForCausalLM(config=lowerCAmelCase__					)
			model.to(lowerCAmelCase__					)
			model.eval()

			# first forward pass
			SCREAMING_SNAKE_CASE       :     List[Any]				= model(
			    lowerCAmelCase__		, attention_mask=lowerCAmelCase__		, encoder_hidden_states=lowerCAmelCase__		, encoder_attention_mask=lowerCAmelCase__		, use_cache=lowerCAmelCase__		, )
			SCREAMING_SNAKE_CASE       :     Dict				= outputs.past_key_values

			# create hypothetical multiple next token and extent to next_input_ids
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= ids_tensor((self.batch_size, 3)		, config.vocab_size					)
			SCREAMING_SNAKE_CASE       :     str				= ids_tensor((self.batch_size, 3)		, vocab_size=2					)

			# append to next input_ids and
			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.cat([input_ids, next_tokens]		, dim=-1					)
			SCREAMING_SNAKE_CASE       :     str				= torch.cat([input_mask, next_mask]		, dim=-1					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= model(
			    lowerCAmelCase__		, attention_mask=lowerCAmelCase__		, encoder_hidden_states=lowerCAmelCase__		, encoder_attention_mask=lowerCAmelCase__		, output_hidden_states=lowerCAmelCase__		, )['''hidden_states'''][0]
			SCREAMING_SNAKE_CASE       :     Any				= model(
			    lowerCAmelCase__		, attention_mask=lowerCAmelCase__		, encoder_hidden_states=lowerCAmelCase__		, encoder_attention_mask=lowerCAmelCase__		, past_key_values=lowerCAmelCase__		, output_hidden_states=lowerCAmelCase__		, )['''hidden_states'''][0]

			# select random slice
			SCREAMING_SNAKE_CASE       :     Tuple				= ids_tensor((1,)		, output_from_past.shape[-1]					).item()
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= output_from_no_past[:, -3:, random_slice_idx].detach()
			SCREAMING_SNAKE_CASE       :     Dict				= 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(lowerCAmelCase__		, lowerCAmelCase__		, atol=1E-3					)					)







	def   __lowerCAmelCase (						self    :List[str]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.prepare_config_and_inputs()
			(
			    (
			    SCREAMING_SNAKE_CASE	
			),					(
			    SCREAMING_SNAKE_CASE	
			),					(
			    SCREAMING_SNAKE_CASE	
			),					(
			    SCREAMING_SNAKE_CASE	
			),					(
			    SCREAMING_SNAKE_CASE	
			),					(
			    SCREAMING_SNAKE_CASE	
			),					(
			    SCREAMING_SNAKE_CASE	
			),						
			)       :     List[str]				= config_and_inputs
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
			return config, inputs_dict





@require_torch
class 				lowercase__(				_snake_case		,						_snake_case		,						_snake_case		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (
	    (
	        FalconModel,
	        FalconForCausalLM,
	        FalconForSequenceClassification,
	        FalconForTokenClassification,
	        FalconForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase				=  (FalconForCausalLM,) if is_torch_available() else ()
	UpperCamelCase				=  (
	    {
	        """feature-extraction""": FalconModel,
	        """text-classification""": FalconForSequenceClassification,
	        """text-generation""": FalconForCausalLM,
	        """question-answering""": FalconForQuestionAnswering,
	        """token-classification""": FalconForTokenClassification,
	        """zero-shot""": FalconForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	UpperCamelCase				=  False
	UpperCamelCase				=  False





	def   __lowerCAmelCase (						self    :Any					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= FalconModelTester(self					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= ConfigTester(self		, config_class=lowerCAmelCase__		, hidden_size=37					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Any:







			'''simple docstring'''




			self.config_tester.run_common_tests()





	def   __lowerCAmelCase (						self    :List[str]					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCAmelCase__					)





	def   __lowerCAmelCase (						self    :Any					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					*SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			for alibi in [True, False]:
					SCREAMING_SNAKE_CASE       :     List[str]				= alibi
					self.model_tester.create_and_check_model(lowerCAmelCase__		, *lowerCAmelCase__					)





	def   __lowerCAmelCase (						self    :Dict					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= self.model_tester.prepare_config_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     List[str]				= 3
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= input_dict['''input_ids''']
			SCREAMING_SNAKE_CASE       :     Tuple				= input_ids.ne(1					).to(lowerCAmelCase__					)
			SCREAMING_SNAKE_CASE       :     int				= ids_tensor([self.model_tester.batch_size]		, self.model_tester.type_sequence_label_size					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= FalconForSequenceClassification(lowerCAmelCase__					)
			model.to(lowerCAmelCase__					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Tuple				= model(lowerCAmelCase__		, attention_mask=lowerCAmelCase__		, labels=lowerCAmelCase__					)
			self.assertEqual(result.logits.shape		, (self.model_tester.batch_size, self.model_tester.num_labels)					)





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= self.model_tester.prepare_config_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     str				= 3
			SCREAMING_SNAKE_CASE       :     Optional[int]				= '''single_label_classification'''
			SCREAMING_SNAKE_CASE       :     int				= input_dict['''input_ids''']
			SCREAMING_SNAKE_CASE       :     List[str]				= input_ids.ne(1					).to(lowerCAmelCase__					)
			SCREAMING_SNAKE_CASE       :     Dict				= ids_tensor([self.model_tester.batch_size]		, self.model_tester.type_sequence_label_size					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= FalconForSequenceClassification(lowerCAmelCase__					)
			model.to(lowerCAmelCase__					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     int				= model(lowerCAmelCase__		, attention_mask=lowerCAmelCase__		, labels=lowerCAmelCase__					)
			self.assertEqual(result.logits.shape		, (self.model_tester.batch_size, self.model_tester.num_labels)					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.model_tester.prepare_config_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     List[Any]				= input_dict['''input_ids''']
			SCREAMING_SNAKE_CASE       :     int				= FalconForCausalLM(lowerCAmelCase__					)
			model.to(lowerCAmelCase__					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Tuple				= model(lowerCAmelCase__		, use_cache=lowerCAmelCase__					)
			SCREAMING_SNAKE_CASE       :     Tuple				= input_ids.shape[0]
			SCREAMING_SNAKE_CASE       :     Any				= model._convert_to_rw_cache(result.past_key_values					)
			SCREAMING_SNAKE_CASE       :     int				= model._convert_cache_to_standard_format(lowerCAmelCase__		, lowerCAmelCase__					)
			for layer in range(len(lowerCAmelCase__					)					):
					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   __lowerCAmelCase (						self    :List[str]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= self.model_tester.prepare_config_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     List[str]				= 3
			SCREAMING_SNAKE_CASE       :     List[str]				= '''multi_label_classification'''
			SCREAMING_SNAKE_CASE       :     Any				= input_dict['''input_ids''']
			SCREAMING_SNAKE_CASE       :     List[str]				= input_ids.ne(1					).to(lowerCAmelCase__					)
			SCREAMING_SNAKE_CASE       :     Any				= ids_tensor(
			    [self.model_tester.batch_size, config.num_labels]		, self.model_tester.type_sequence_label_size					).to(torch.float					)
			SCREAMING_SNAKE_CASE       :     List[str]				= FalconForSequenceClassification(lowerCAmelCase__					)
			model.to(lowerCAmelCase__					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Tuple				= model(lowerCAmelCase__		, attention_mask=lowerCAmelCase__		, labels=lowerCAmelCase__					)
			self.assertEqual(result.logits.shape		, (self.model_tester.batch_size, self.model_tester.num_labels)					)







	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Tuple:







			'''simple docstring'''




			for model_class in self.all_generative_model_classes:
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= self.model_tester.prepare_config_and_inputs_for_common()

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

					SCREAMING_SNAKE_CASE       :     Dict				= model_class(lowerCAmelCase__					).to(lowerCAmelCase__					)
					if "use_cache" not in inputs:
							SCREAMING_SNAKE_CASE       :     int				= True
					SCREAMING_SNAKE_CASE       :     str				= model(**lowerCAmelCase__					)

					# 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

					SCREAMING_SNAKE_CASE       :     List[str]				= (
					    getattr(lowerCAmelCase__		, '''decoder_layers'''		, lowerCAmelCase__					)
					    or getattr(lowerCAmelCase__		, '''num_decoder_layers'''		, lowerCAmelCase__					)
					    or config.num_hidden_layers
					)
					SCREAMING_SNAKE_CASE       :     str				= getattr(lowerCAmelCase__		, '''num_kv_heads'''		, config.num_attention_heads					)
					SCREAMING_SNAKE_CASE       :     Tuple				= getattr(lowerCAmelCase__		, '''d_model'''		, config.hidden_size					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= embed_dim // num_attention_heads

					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= outputs['''past_key_values''']
					self.assertEqual(len(lowerCAmelCase__					)		, lowerCAmelCase__					)

					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[Any]				= inputs['''input_ids'''].shape
					for i in range(lowerCAmelCase__					):
							if config.new_decoder_architecture:
									SCREAMING_SNAKE_CASE       :     Tuple				= config.num_attention_heads
							elif config.multi_query:
									SCREAMING_SNAKE_CASE       :     List[str]				= 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 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	@slow
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b'''					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b'''					)
			model.eval()
			model.to(lowerCAmelCase__					)
			SCREAMING_SNAKE_CASE       :     Dict				= tokenizer('''My favorite food is'''		, return_tensors='''pt'''					).to(lowerCAmelCase__					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= (
			    '''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
			)

			SCREAMING_SNAKE_CASE       :     List[Any]				= model.generate(**lowerCAmelCase__		, do_sample=lowerCAmelCase__		, max_new_tokens=19					)
			SCREAMING_SNAKE_CASE       :     Dict				= tokenizer.batch_decode(lowerCAmelCase__					)[0]

			self.assertEqual(lowerCAmelCase__		, lowerCAmelCase__					)





	@slow
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Optional[int]:







			'''simple docstring'''




			for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
					SCREAMING_SNAKE_CASE       :     str				= AutoTokenizer.from_pretrained(lowerCAmelCase__					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= FalconForCausalLM.from_pretrained(lowerCAmelCase__					)
					model.eval()
					model.to(lowerCAmelCase__					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= tokenizer('''My favorite food is'''		, return_tensors='''pt'''					).to(lowerCAmelCase__					)

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







	@slow
	def   __lowerCAmelCase (						self    :List[str]					)							->			Tuple:







			'''simple docstring'''




			with torch.no_grad():
					for repo in [
					    "Rocketknight1/falcon-rw-1b",
					    "Rocketknight1/tiny-random-falcon-7b",
					    "Rocketknight1/tiny-random-falcon-40b",
					]:
							SCREAMING_SNAKE_CASE       :     Dict				= AutoTokenizer.from_pretrained(lowerCAmelCase__					)
							SCREAMING_SNAKE_CASE       :     Dict				= FalconForCausalLM.from_pretrained(lowerCAmelCase__					)
							model.eval()
							model.to(device=lowerCAmelCase__					)
							SCREAMING_SNAKE_CASE       :     Dict				= tokenizer('''My favorite food is'''		, return_tensors='''pt'''					).to(lowerCAmelCase__					)

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







"""simple docstring"""






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCamelCase__ :       Optional[Any]						     =			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def __A						(   a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






		if "token" in model_name_or_path:
				return "rag_token"
		if "sequence" in model_name_or_path:
				return "rag_sequence"
		if "bart" in model_name_or_path:
				return "bart"
		return None





def __A						(   a_ :							List[Any]		,		a_ :							Optional[int]		,		a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






		return max(metric_fn(a_		,		a_     ) for gt in ground_truths     )





def __A						(   a_ :							List[Any]		,		a_ :							Union[str, Any]		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []

		if args.gold_data_mode == "qa":
				SCREAMING_SNAKE_CASE       :     List[Any]				= pd.read_csv(a_		,		sep='''\t'''		,		header=a_     )
				for answer_list in data[1]:
						SCREAMING_SNAKE_CASE       :     str				= ast.literal_eval(a_     )
						answers.append(a_     )
		else:
				SCREAMING_SNAKE_CASE       :     Any				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
				SCREAMING_SNAKE_CASE       :     Dict				= [[reference] for reference in references]

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for prediction, ground_truths in zip(a_		,		a_     ):
				total += 1
				em += metric_max_over_ground_truths(a_		,		a_		,		a_     )
				fa += metric_max_over_ground_truths(a_		,		a_		,		a_     )

		SCREAMING_SNAKE_CASE       :     Any				= 100.0 * em / total
		SCREAMING_SNAKE_CASE       :     Optional[int]				= 100.0 * fa / total

		logger.info(F"F1: {fa:.2f}"     )
		logger.info(F"EM: {em:.2f}"     )





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							List[Any]     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= args.k
		SCREAMING_SNAKE_CASE       :     Tuple				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for hypo, reference in zip(a_		,		a_     ):
				SCREAMING_SNAKE_CASE       :     Optional[int]				= set(hypo.split('''\t'''     )[:k]     )
				SCREAMING_SNAKE_CASE       :     List[str]				= set(reference.split('''\t'''     )     )
				total += 1
				em += len(hypo_provenance & ref_provenance     ) / k

		SCREAMING_SNAKE_CASE       :     Dict				= 100.0 * em / total
		logger.info(F"Precision@{k}: {em: .2f}"     )





def __A						(   a_ :							Any		,		a_ :							List[str]		,		a_ :							str     )->							int:




		'''simple docstring'''






		def strip_title(a_ :							Optional[Any]     ):
				if title.startswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Tuple				= title[1:]
				if title.endswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= title[:-1]
				return title

		SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_		,		)['''input_ids'''].to(args.device     )

		SCREAMING_SNAKE_CASE       :     Any				= rag_model.rag.question_encoder(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= question_enc_outputs[0]

		SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever(
		    a_		,		question_enc_pool_output.cpu().detach().to(torch.floataa     ).numpy()		,		prefix=rag_model.rag.generator.config.prefix		,		n_docs=rag_model.config.n_docs		,		return_tensors='''pt'''		,		)
		SCREAMING_SNAKE_CASE       :     Any				= rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
		SCREAMING_SNAKE_CASE       :     Dict				= []
		for docs in all_docs:
				SCREAMING_SNAKE_CASE       :     List[Any]				= [strip_title(a_     ) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(a_     )     )
		return provenance_strings





def __A						(   a_ :							List[Any]		,		a_ :							int		,		a_ :							str     )->							Tuple:




		'''simple docstring'''






		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     Optional[int]				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_     )

				SCREAMING_SNAKE_CASE       :     Dict				= inputs_dict.input_ids.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Any				= inputs_dict.attention_mask.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.generate(  # rag_model overwrites generate
				    a_		,		attention_mask=a_		,		num_beams=args.num_beams		,		min_length=args.min_length		,		max_length=args.max_length		,		early_stopping=a_		,		num_return_sequences=1		,		bad_words_ids=[[0, 0]]		,		)
				SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever.generator_tokenizer.batch_decode(a_		,		skip_special_tokens=a_     )

				if args.print_predictions:
						for q, a in zip(a_		,		a_     ):
								logger.info('''Q: {} - A: {}'''.format(a_		,		a_     )     )

				return answers





def __A						(   )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type'''		,		choices=['''rag_sequence''', '''rag_token''', '''bart''']		,		type=a_		,		help=(
		        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
		        ''' model_name_or_path'''
		    )		,		)
		parser.add_argument(
		    '''--index_name'''		,		default=a_		,		choices=['''exact''', '''compressed''', '''legacy''']		,		type=a_		,		help='''RAG model retriever type'''		,		)
		parser.add_argument(
		    '''--index_path'''		,		default=a_		,		type=a_		,		help='''Path to the retrieval index'''		,		)
		parser.add_argument('''--n_docs'''		,		default=5		,		type=a_		,		help='''Number of retrieved docs'''     )
		parser.add_argument(
		    '''--model_name_or_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''		,		)
		parser.add_argument(
		    '''--eval_mode'''		,		choices=['''e2e''', '''retrieval''']		,		default='''e2e'''		,		type=a_		,		help=(
		        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
		        ''' precision@k.'''
		    )		,		)
		parser.add_argument('''--k'''		,		default=1		,		type=a_		,		help='''k for the precision@k calculation'''     )
		parser.add_argument(
		    '''--evaluation_set'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a file containing evaluation samples'''		,		)
		parser.add_argument(
		    '''--gold_data_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a tab-separated file with gold samples'''		,		)
		parser.add_argument(
		    '''--gold_data_mode'''		,		default='''qa'''		,		type=a_		,		choices=['''qa''', '''ans''']		,		help=(
		        '''Format of the gold data file'''
		        '''qa - a single line in the following format: question [tab] answer_list'''
		        '''ans - a single line of the gold file contains the expected answer string'''
		    )		,		)
		parser.add_argument(
		    '''--predictions_path'''		,		type=a_		,		default='''predictions.txt'''		,		help='''Name of the predictions file, to be stored in the checkpoints directory'''		,		)
		parser.add_argument(
		    '''--eval_all_checkpoints'''		,		action='''store_true'''		,		help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''		,		)
		parser.add_argument(
		    '''--eval_batch_size'''		,		default=8		,		type=a_		,		help='''Batch size per GPU/CPU for evaluation.'''		,		)
		parser.add_argument(
		    '''--recalculate'''		,		help='''Recalculate predictions even if the prediction file exists'''		,		action='''store_true'''		,		)
		parser.add_argument(
		    '''--num_beams'''		,		default=4		,		type=a_		,		help='''Number of beams to be used when generating answers'''		,		)
		parser.add_argument('''--min_length'''		,		default=1		,		type=a_		,		help='''Min length of the generated answers'''     )
		parser.add_argument('''--max_length'''		,		default=50		,		type=a_		,		help='''Max length of the generated answers'''     )

		parser.add_argument(
		    '''--print_predictions'''		,		action='''store_true'''		,		help='''If True, prints predictions while evaluating.'''		,		)
		parser.add_argument(
		    '''--print_docs'''		,		action='''store_true'''		,		help='''If True, prints docs retried while generating.'''		,		)
		SCREAMING_SNAKE_CASE       :     List[str]				= parser.parse_args()
		SCREAMING_SNAKE_CASE       :     Dict				= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''     )
		return args





def __A						(   a_ :							Optional[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= {}
		if args.model_type is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= infer_model_type(args.model_name_or_path     )
				assert args.model_type is not None
		if args.model_type.startswith('''rag'''     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= args.n_docs
				if args.index_name is not None:
						SCREAMING_SNAKE_CASE       :     Tuple				= args.index_name
				if args.index_path is not None:
						SCREAMING_SNAKE_CASE       :     List[Any]				= args.index_path
		else:
				SCREAMING_SNAKE_CASE       :     str				= BartForConditionalGeneration

		SCREAMING_SNAKE_CASE       :     Optional[int]				= (
		    [f.path for f in os.scandir(args.model_name_or_path     ) if f.is_dir()]
		    if args.eval_all_checkpoints
		    else [args.model_name_or_path]
		)

		logger.info('''Evaluate the following checkpoints: %s'''		,		a_     )

		SCREAMING_SNAKE_CASE       :     int				= get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
		SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

		for checkpoint in checkpoints:
				if os.path.exists(args.predictions_path     ) and (not args.recalculate):
						logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path     )     )
						score_fn(a_		,		args.predictions_path		,		args.gold_data_path     )
						continue

				logger.info('''***** Running evaluation for {} *****'''.format(a_     )     )
				logger.info('''  Batch size = %d'''		,		args.eval_batch_size     )
				logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path     )     )

				if args.model_type.startswith('''rag'''     ):
						SCREAMING_SNAKE_CASE       :     Dict				= RagRetriever.from_pretrained(a_		,		**a_     )
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_class.from_pretrained(a_		,		retriever=a_		,		**a_     )
						model.retriever.init_retrieval()
				else:
						SCREAMING_SNAKE_CASE       :     str				= model_class.from_pretrained(a_		,		**a_     )
				model.to(args.device     )

				with open(args.evaluation_set		,		'''r'''     ) as eval_file, open(args.predictions_path		,		'''w'''     ) as preds_file:
						SCREAMING_SNAKE_CASE       :     Dict				= []
						for line in tqdm(a_     ):
								questions.append(line.strip()     )
								if len(a_     ) == args.eval_batch_size:
										SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_fn(a_		,		a_		,		a_     )
										preds_file.write('''\n'''.join(a_     ) + '''\n'''     )
										preds_file.flush()
										SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
						if len(a_     ) > 0:
								SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_fn(a_		,		a_		,		a_     )
								preds_file.write('''\n'''.join(a_     )     )
								preds_file.flush()

						score_fn(a_		,		args.predictions_path		,		args.gold_data_path     )


if __name__ == "__main__":
					lowerCamelCase__ :       List[str]						     =			get_args()
					main(args)





