



'''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 	a__     :


 def __init__(self : Tuple,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Any=3,					__UpperCAmelCase : Optional[Any]=7,					__UpperCAmelCase : str=True,					__UpperCAmelCase : int=True,					__UpperCAmelCase : Optional[Any]=False,					__UpperCAmelCase : str=True,					__UpperCAmelCase : List[str]=99,					__UpperCAmelCase : Optional[int]=32,					__UpperCAmelCase : List[Any]=5,					__UpperCAmelCase : Union[str, Any]=4,					__UpperCAmelCase : Dict=37,					__UpperCAmelCase : str="gelu",					__UpperCAmelCase : Optional[int]=0.1,					__UpperCAmelCase : int=0.1,					__UpperCAmelCase : Any=512,					__UpperCAmelCase : List[Any]=16,					__UpperCAmelCase : List[str]=2,					__UpperCAmelCase : Dict=0.02,					__UpperCAmelCase : Optional[int]=3,					__UpperCAmelCase : Dict=4,					__UpperCAmelCase : Union[str, Any]=None,					)     ->			List[Any]:

     """simple docstring"""

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


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

     """simple docstring"""

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

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

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= None

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

     SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_config()

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


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

     """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=__UpperCAmelCase,					initializer_range=self.initializer_range,					pad_token_id=1,					new_decoder_architecture=__UpperCAmelCase,					)


 def 				lowercase__  (self : List[str],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Any,					__UpperCAmelCase : List[str],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict      )     ->			Union[str, Any]:

     """simple docstring"""

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


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

     """simple docstring"""

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


 def 				lowercase__  (self : str,					__UpperCAmelCase : str,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : int,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Tuple,					)     ->			Any:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= FalconForCausalLM(config=__UpperCAmelCase      )
     model.to(__UpperCAmelCase      )
     model.eval()
     SCREAMING_SNAKE_CASE  :						List[str]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					labels=__UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)      )


 def 				lowercase__  (self : Dict,					__UpperCAmelCase : Any,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : str,					__UpperCAmelCase : int,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : List[Any],					)     ->			List[Any]:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= True
     SCREAMING_SNAKE_CASE  :						Optional[int]      					= True
     SCREAMING_SNAKE_CASE  :						Any      					= FalconForCausalLM(config=__UpperCAmelCase      )
     model.to(__UpperCAmelCase      )
     model.eval()

     # first forward pass
     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model(
         __UpperCAmelCase,					attention_mask=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					use_cache=__UpperCAmelCase,					)
     SCREAMING_SNAKE_CASE  :						Tuple      					= outputs.past_key_values

     # create hypothetical multiple next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE  :						int      					= ids_tensor((self.batch_size, 3),					config.vocab_size      )
     SCREAMING_SNAKE_CASE  :						Dict      					= 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  :						Optional[int]      					= torch.cat([input_mask, next_mask],					dim=-1      )

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

     # select random slice
     SCREAMING_SNAKE_CASE  :						List[str]      					= ids_tensor((1,),					output_from_past.shape[-1]      ).item()
     SCREAMING_SNAKE_CASE  :						Any      					= output_from_no_past[:, -3:, random_slice_idx].detach()
     SCREAMING_SNAKE_CASE  :						Tuple      					= 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(__UpperCAmelCase,					__UpperCAmelCase,					atol=1e-3      )      )






 def 				lowercase__  (self : Tuple      )     ->			Any:

     """simple docstring"""

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



@require_torch
class 	a__     (				_lowercase,			_lowercase,			_lowercase,			unittest.TestCase	):
 __magic_name__		:    Optional[int]	    =     (
     (
         FalconModel,
         FalconForCausalLM,
         FalconForSequenceClassification,
         FalconForTokenClassification,
         FalconForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __magic_name__		:    List[Any]	    =     (FalconForCausalLM,) if is_torch_available() else ()
 __magic_name__		:    Union[str, Any]	    =     (
     {
         "feature-extraction": FalconModel,
         "text-classification": FalconForSequenceClassification,
         "text-generation": FalconForCausalLM,
         "question-answering": FalconForQuestionAnswering,
         "token-classification": FalconForTokenClassification,
         "zero-shot": FalconForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __magic_name__		:    Dict	    =     False
 __magic_name__		:    Any	    =     False


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

     """simple docstring"""

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


 def 				lowercase__  (self : Tuple      )     ->			Dict:

     """simple docstring"""

     self.config_tester.run_common_tests()


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

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__UpperCAmelCase      )


 def 				lowercase__  (self : Dict      )     ->			Tuple:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.model_tester.prepare_config_and_inputs()
     for alibi in [True, False]:
         SCREAMING_SNAKE_CASE  :						Optional[Any]      					= alibi
         self.model_tester.create_and_check_model(__UpperCAmelCase,					*__UpperCAmelCase      )


 def 				lowercase__  (self : int      )     ->			List[Any]:

     """simple docstring"""

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


 def 				lowercase__  (self : Optional[Any]      )     ->			Tuple:

     """simple docstring"""

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


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

     """simple docstring"""

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

     """simple docstring"""

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






 def 				lowercase__  (self : Optional[int]      )     ->			Optional[Any]:

     """simple docstring"""

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

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

         SCREAMING_SNAKE_CASE  :						Optional[int]      					= model_class(__UpperCAmelCase      ).to(__UpperCAmelCase      )
         if "use_cache" not in inputs:
             SCREAMING_SNAKE_CASE  :						List[str]      					= True
         SCREAMING_SNAKE_CASE  :						Tuple      					= model(**__UpperCAmelCase      )

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

         SCREAMING_SNAKE_CASE  :						int      					= outputs['''past_key_values''']
         self.assertEqual(len(__UpperCAmelCase      ),					__UpperCAmelCase      )

         SCREAMING_SNAKE_CASE  :						List[Any]      					= inputs['''input_ids'''].shape
         for i in range(__UpperCAmelCase      ):
             if config.new_decoder_architecture:
                 SCREAMING_SNAKE_CASE  :						Optional[Any]      					= config.num_attention_heads
             elif config.multi_query:
                 SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 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 	a__     (				unittest.TestCase	):


 @slow
 def 				lowercase__  (self : List[str]      )     ->			Dict:

     """simple docstring"""

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

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

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

     self.assertEqual(__UpperCAmelCase,					__UpperCAmelCase      )


 @slow
 def 				lowercase__  (self : Tuple      )     ->			Any:

     """simple docstring"""

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

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






 @slow
 def 				lowercase__  (self : List[str]      )     ->			Dict:

     """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  :						Tuple      					= AutoTokenizer.from_pretrained(__UpperCAmelCase      )
             SCREAMING_SNAKE_CASE  :						Optional[Any]      					= FalconForCausalLM.from_pretrained(__UpperCAmelCase      )
             model.eval()
             model.to(device=__UpperCAmelCase      )
             SCREAMING_SNAKE_CASE  :						Optional[int]      					= tokenizer('''My favorite food is''',					return_tensors='''pt'''      ).to(__UpperCAmelCase      )

             # Test results are the same with and without cache
             SCREAMING_SNAKE_CASE  :						Optional[int]      					= model.generate(**__UpperCAmelCase,					do_sample=__UpperCAmelCase,					max_new_tokens=20,					use_cache=__UpperCAmelCase      )
             SCREAMING_SNAKE_CASE  :						Dict      					= model.generate(**__UpperCAmelCase,					do_sample=__UpperCAmelCase,					max_new_tokens=20,					use_cache=__UpperCAmelCase      )
             self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0      )









'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt


class 	a__     :


	def __init__(self : Any,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : List[str]=0.2,					__UpperCAmelCase : Dict=0.2      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= bp_numa
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= bp_numa
					SCREAMING_SNAKE_CASE  :						str      					= bp_numa
					SCREAMING_SNAKE_CASE  :						Dict      					= conva_get[:2]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= conva_get[2]
					SCREAMING_SNAKE_CASE  :						int      					= size_pa
					SCREAMING_SNAKE_CASE  :						int      					= rate_w
					SCREAMING_SNAKE_CASE  :						int      					= rate_t
					SCREAMING_SNAKE_CASE  :						int      					= [
					    np.mat(-1 * np.random.rand(self.conva[0],					self.conva[0]      ) + 0.5      )
					    for i in range(self.conva[1]      )
					]
					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.mat(-1 * np.random.rand(self.num_bpa,					self.num_bpa      ) + 0.5      )
					SCREAMING_SNAKE_CASE  :						Dict      					= np.mat(-1 * np.random.rand(self.num_bpa,					self.num_bpa      ) + 0.5      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= -2 * np.random.rand(self.conva[1]      ) + 1
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= -2 * np.random.rand(self.num_bpa      ) + 1
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= -2 * np.random.rand(self.num_bpa      ) + 1


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= {
					    '''num_bp1''': self.num_bpa,
					    '''num_bp2''': self.num_bpa,
					    '''num_bp3''': self.num_bpa,
					    '''conv1''': self.conva,
					    '''step_conv1''': self.step_conva,
					    '''size_pooling1''': self.size_poolinga,
					    '''rate_weight''': self.rate_weight,
					    '''rate_thre''': self.rate_thre,
					    '''w_conv1''': self.w_conva,
					    '''wkj''': self.wkj,
					    '''vji''': self.vji,
					    '''thre_conv1''': self.thre_conva,
					    '''thre_bp2''': self.thre_bpa,
					    '''thre_bp3''': self.thre_bpa,
					}
					with open(__UpperCAmelCase,					'''wb'''      ) as f:
									pickle.dump(__UpperCAmelCase,					__UpperCAmelCase      )

					print(F'''Model saved： {save_path}'''      )


	@classmethod
	def 				lowercase__  (cls : str,					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

					with open(__UpperCAmelCase,					'''rb'''      ) as f:
									SCREAMING_SNAKE_CASE  :						int      					= pickle.load(__UpperCAmelCase      )  # noqa: S301

					SCREAMING_SNAKE_CASE  :						List[str]      					= model_dic.get('''conv1'''      )
					conv_get.append(model_dic.get('''step_conv1'''      )      )
					SCREAMING_SNAKE_CASE  :						Dict      					= model_dic.get('''size_pooling1'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= model_dic.get('''num_bp1'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''num_bp2'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= model_dic.get('''num_bp3'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model_dic.get('''rate_weight'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model_dic.get('''rate_thre'''      )
					# create model instance
					SCREAMING_SNAKE_CASE  :						List[Any]      					= CNN(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )
					# modify model parameter
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''w_conv1'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= model_dic.get('''wkj'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model_dic.get('''vji'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''thre_conv1'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= model_dic.get('''thre_bp2'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''thre_bp3'''      )
					return conv_ins


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Dict      )     ->			Optional[Any]:

					"""simple docstring"""

					return 1 / (1 + np.exp(-1 * x      ))


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : List[str]      )     ->			str:

					"""simple docstring"""

					return round(__UpperCAmelCase,					3      )


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Any,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= convs[0]
					SCREAMING_SNAKE_CASE  :						int      					= convs[1]
					SCREAMING_SNAKE_CASE  :						List[str]      					= np.shape(__UpperCAmelCase      )[0]
					# get the data slice of original image data, data_focus
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
					for i_focus in range(0,					size_data - size_conv + 1,					__UpperCAmelCase      ):
									for j_focus in range(0,					size_data - size_conv + 1,					__UpperCAmelCase      ):
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= data[
													    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
													]
													data_focus.append(__UpperCAmelCase      )
        # calculate the feature map of every single kernel, and saved as list of matrix
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= int((size_data - size_conv) / conv_step + 1      )
					for i_map in range(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
									for i_focus in range(len(__UpperCAmelCase      )      ):
													SCREAMING_SNAKE_CASE  :						Any      					= (
													    np.sum(np.multiply(data_focus[i_focus],					w_convs[i_map]      )      )
													    - thre_convs[i_map]
													)
													featuremap.append(self.sig(__UpperCAmelCase      )      )
									SCREAMING_SNAKE_CASE  :						Dict      					= np.asmatrix(__UpperCAmelCase      ).reshape(
									    __UpperCAmelCase,					__UpperCAmelCase      )
									data_featuremap.append(__UpperCAmelCase      )

					# expanding the data slice to One dimenssion
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
					for each_focus in data_focus:
									focusa_list.extend(self.Expand_Mat(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asarray(__UpperCAmelCase      )
					return focus_list, data_featuremap


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]="average_pool"      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= len(featuremaps[0]      )
					SCREAMING_SNAKE_CASE  :						str      					= int(size_map / size_pooling      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= []
					for i_map in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						str      					= featuremaps[i_map]
									SCREAMING_SNAKE_CASE  :						Any      					= []
									for i_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
													for j_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						str      					= feature_map[
																	    i_focus : i_focus + size_pooling,
																	    j_focus : j_focus + size_pooling,
																	]
																	if pooling_type == "average_pool":
																					# average pooling
																					map_pooled.append(np.average(__UpperCAmelCase      )      )
																	elif pooling_type == "max_pooling":
																					# max pooling
																					map_pooled.append(np.max(__UpperCAmelCase      )      )
									SCREAMING_SNAKE_CASE  :						Tuple      					= np.asmatrix(__UpperCAmelCase      ).reshape(__UpperCAmelCase,					__UpperCAmelCase      )
									featuremap_pooled.append(__UpperCAmelCase      )
					return featuremap_pooled


	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= []
					for i in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.shape(data[i]      )
									SCREAMING_SNAKE_CASE  :						str      					= data[i].reshape(1,					shapes[0] * shapes[1]      )
									SCREAMING_SNAKE_CASE  :						int      					= data_listed.getA().tolist()[0]
									data_expanded.extend(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.asarray(__UpperCAmelCase      )
					return data_expanded


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= np.asarray(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= np.shape(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= data_mat.reshape(1,					shapes[0] * shapes[1]      )
					return data_expanded


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= []
					SCREAMING_SNAKE_CASE  :						List[str]      					= 0
					for i_map in range(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Dict      					= np.ones((size_map, size_map)      )
									for i in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
													for j in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						str      					= pd_pool[
																	    i_pool
																	]
																	SCREAMING_SNAKE_CASE  :						Dict      					= i_pool + 1
									SCREAMING_SNAKE_CASE  :						List[Any]      					= np.multiply(
									    __UpperCAmelCase,					np.multiply(out_map[i_map],					(1 - out_map[i_map])      )      )
									pd_all.append(__UpperCAmelCase      )
					return pd_all


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[int]=bool      )     ->			List[Any]:

					"""simple docstring"""

					print('''----------------------Start Training-------------------------'''      )
					print((''' - - Shape: Train_Data  ''', np.shape(__UpperCAmelCase      ))      )
					print((''' - - Shape: Teach_Data  ''', np.shape(__UpperCAmelCase      ))      )
					SCREAMING_SNAKE_CASE  :						Dict      					= 0
					SCREAMING_SNAKE_CASE  :						str      					= []
					SCREAMING_SNAKE_CASE  :						Any      					= 10000
					while rp < n_repeat and mse >= error_accuracy:
									SCREAMING_SNAKE_CASE  :						int      					= 0
									print(F'''-------------Learning Time {rp}--------------'''      )
									for p in range(len(__UpperCAmelCase      )      ):
													# print('------------Learning Image: %d--------------'%p)
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(datas_train[p]      )
													SCREAMING_SNAKE_CASE  :						Tuple      					= np.asarray(datas_teach[p]      )
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self.convolute(
													    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
													SCREAMING_SNAKE_CASE  :						Dict      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.shape(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						str      					= self._expand(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= data_bp_input

													SCREAMING_SNAKE_CASE  :						Tuple      					= np.dot(__UpperCAmelCase,					self.vji.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						List[Any]      					= self.sig(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.dot(__UpperCAmelCase,					self.wkj.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.sig(__UpperCAmelCase      )

													# --------------Model Leaning ------------------------
													# calculate error and gradient---------------
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.multiply(
													    (data_teach - bp_outa),					np.multiply(__UpperCAmelCase,					(1 - bp_outa)      )      )
													SCREAMING_SNAKE_CASE  :						List[Any]      					= np.multiply(
													    np.dot(__UpperCAmelCase,					self.wkj      ),					np.multiply(__UpperCAmelCase,					(1 - bp_outa)      )      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.dot(__UpperCAmelCase,					self.vji      )

													SCREAMING_SNAKE_CASE  :						Dict      					= pd_i_all / (self.size_poolinga * self.size_poolinga)
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pd_conva_pooled.T.getA().tolist()
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self._calculate_gradient_from_pool(
													    __UpperCAmelCase,					__UpperCAmelCase,					shape_featuremapa[0],					shape_featuremapa[1],					self.size_poolinga,					)
													# weight and threshold learning process---------
													# convolution layer
													for k_conv in range(self.conva[1]      ):
																	SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self._expand_mat(pd_conva_all[k_conv]      )
																	SCREAMING_SNAKE_CASE  :						Any      					= self.rate_weight * np.dot(__UpperCAmelCase,					__UpperCAmelCase      )

																	SCREAMING_SNAKE_CASE  :						List[Any]      					= self.w_conva[k_conv] + delta_w.reshape(
																	    (self.conva[0], self.conva[0])      )

																	SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= (
																	    self.thre_conva[k_conv]
																	    - np.sum(pd_conva_all[k_conv]      ) * self.rate_thre
																	)
													# all connected layer
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.wkj + pd_k_all.T * bp_outa * self.rate_weight
													SCREAMING_SNAKE_CASE  :						List[str]      					= self.vji + pd_j_all.T * bp_outa * self.rate_weight
													SCREAMING_SNAKE_CASE  :						List[Any]      					= self.thre_bpa - pd_k_all * self.rate_thre
													SCREAMING_SNAKE_CASE  :						int      					= self.thre_bpa - pd_j_all * self.rate_thre
													# calculate the sum error of all single image
													SCREAMING_SNAKE_CASE  :						List[Any]      					= np.sum(abs(data_teach - bp_outa      )      )
													error_count += errors
													# print('   ----Teach      ',data_teach)
													# print('   ----BP_output  ',bp_out3)
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= rp + 1
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= error_count / patterns
									all_mse.append(__UpperCAmelCase      )

					def draw_error():
									SCREAMING_SNAKE_CASE  :						Tuple      					= [error_accuracy for i in range(int(n_repeat * 1.2      )      )]
									plt.plot(__UpperCAmelCase,					'''+-'''      )
									plt.plot(__UpperCAmelCase,					'''r--'''      )
									plt.xlabel('''Learning Times'''      )
									plt.ylabel('''All_mse'''      )
									plt.grid(__UpperCAmelCase,					alpha=0.5      )
									plt.show()

					print('''------------------Training Complished---------------------'''      )
					print((''' - - Training epoch: ''', rp, F'''     - - Mse: {mse:.6f}''')      )
					if draw_e:
									draw_error()
					return mse


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					print('''-------------------Start Testing-------------------------'''      )
					print((''' - - Shape: Test_Data  ''', np.shape(__UpperCAmelCase      ))      )
					for p in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(datas_test[p]      )
									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= self.convolute(
									    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
									SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )
									SCREAMING_SNAKE_CASE  :						str      					= self._expand(__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						Any      					= data_bp_input
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= bp_outa * self.vji.T - self.thre_bpa
									SCREAMING_SNAKE_CASE  :						Dict      					= self.sig(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Any      					= bp_outa * self.wkj.T - self.thre_bpa
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.sig(__UpperCAmelCase      )
									produce_out.extend(bp_outa.getA().tolist()      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= [list(map(self.do_round,					__UpperCAmelCase      )      ) for each in produce_out]
					return np.asarray(__UpperCAmelCase      )






	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.convolute(
					    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )

					return data_conveda, data_pooleda


if __name__ == "__main__":
	pass




