



'''simple docstring'''

from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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


if is_tf_available():
	import tensorflow as tf

	from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=None    ,	_SCREAMING_SNAKE_CASE      :Optional[int]=None       ):
				if attention_mask is None:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE    ,	config.pad_token_id       )    ,	tf.inta       )
				return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class 	a__     :
	__magic_name__		:    str	    =     OPTConfig
	__magic_name__		:    Optional[int]	    =     {}
	__magic_name__		:    Union[str, Any]	    =     "gelu"


	def __init__(self : List[str],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Any=13,					__UpperCAmelCase : List[Any]=7,					__UpperCAmelCase : Union[str, Any]=True,					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : Optional[int]=99,					__UpperCAmelCase : Dict=16,					__UpperCAmelCase : List[str]=2,					__UpperCAmelCase : List[str]=4,					__UpperCAmelCase : List[str]=4,					__UpperCAmelCase : List[str]="gelu",					__UpperCAmelCase : List[Any]=0.1,					__UpperCAmelCase : Optional[Any]=0.1,					__UpperCAmelCase : Optional[Any]=20,					__UpperCAmelCase : Any=2,					__UpperCAmelCase : Union[str, Any]=1,					__UpperCAmelCase : Optional[int]=0,					__UpperCAmelCase : Optional[int]=16,					__UpperCAmelCase : Optional[Any]=16,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= parent
					SCREAMING_SNAKE_CASE  :						Any      					= batch_size
					SCREAMING_SNAKE_CASE  :						int      					= seq_length
					SCREAMING_SNAKE_CASE  :						int      					= is_training
					SCREAMING_SNAKE_CASE  :						List[str]      					= use_labels
					SCREAMING_SNAKE_CASE  :						str      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Dict      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Dict      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						str      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						List[str]      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						Tuple      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						List[Any]      					= max_position_embeddings
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= eos_token_id
					SCREAMING_SNAKE_CASE  :						Tuple      					= pad_token_id
					SCREAMING_SNAKE_CASE  :						List[str]      					= bos_token_id
					SCREAMING_SNAKE_CASE  :						List[Any]      					= embed_dim
					SCREAMING_SNAKE_CASE  :						int      					= word_embed_proj_dim
					SCREAMING_SNAKE_CASE  :						Any      					= False


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= ids_tensor([self.batch_size, self.seq_length - 1],					self.vocab_size      )
					SCREAMING_SNAKE_CASE  :						int      					= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      ),					1      )
					SCREAMING_SNAKE_CASE  :						Dict      					= tf.concat([input_ids, eos_tensor],					axis=1      )

					SCREAMING_SNAKE_CASE  :						Any      					= self.config_cls(
					    vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					ffn_dim=self.intermediate_size,					dropout=self.hidden_dropout_prob,					attention_dropout=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					eos_token_id=self.eos_token_id,					bos_token_id=self.bos_token_id,					pad_token_id=self.pad_token_id,					embed_dim=self.embed_dim,					word_embed_proj_dim=self.word_embed_proj_dim,					is_encoder_decoder=__UpperCAmelCase,					**self.config_updates,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= prepare_opt_inputs_dict(__UpperCAmelCase,					__UpperCAmelCase      )
					return config, inputs_dict






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= TFOPTModel(config=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= inputs_dict['''input_ids''']

					SCREAMING_SNAKE_CASE  :						List[str]      					= input_ids[:1, :]
					SCREAMING_SNAKE_CASE  :						Any      					= inputs_dict['''attention_mask'''][:1, :]
					SCREAMING_SNAKE_CASE  :						Tuple      					= 1

					# first forward pass
					SCREAMING_SNAKE_CASE  :						int      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					use_cache=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					SCREAMING_SNAKE_CASE  :						List[Any]      					= ids_tensor((self.batch_size, 3),					config.vocab_size      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= tf.cast(ids_tensor((self.batch_size, 3),					2      ),					tf.inta      )

					# append to next input_ids and
					SCREAMING_SNAKE_CASE  :						str      					= tf.concat([input_ids, next_tokens],					axis=-1      )
					SCREAMING_SNAKE_CASE  :						str      					= tf.concat([attention_mask, next_attn_mask],					axis=-1      )

					SCREAMING_SNAKE_CASE  :						int      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase      )[0]
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					past_key_values=__UpperCAmelCase      )[0]

					self.parent.assertEqual(next_tokens.shape[1],					output_from_past.shape[1]      )

					# select random slice
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= int(ids_tensor((1,),					output_from_past.shape[-1]      )      )
					SCREAMING_SNAKE_CASE  :						int      					= output_from_no_past[:, -3:, random_slice_idx]
					SCREAMING_SNAKE_CASE  :						List[Any]      					= output_from_past[:, :, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(__UpperCAmelCase,					__UpperCAmelCase,					rtol=1e-3      )



@require_tf
class 	a__     (				_lowercase,			_lowercase,			unittest.TestCase	):
	__magic_name__		:    Optional[Any]	    =     (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
	__magic_name__		:    Union[str, Any]	    =     (TFOPTForCausalLM,) if is_tf_available() else ()
	__magic_name__		:    List[str]	    =     (
	    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
	)
	__magic_name__		:    Optional[int]	    =     False
	__magic_name__		:    Dict	    =     False
	__magic_name__		:    Optional[int]	    =     False
	__magic_name__		:    List[str]	    =     10


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= TFOPTModelTester(self      )
					SCREAMING_SNAKE_CASE  :						int      					= ConfigTester(self,					config_class=__UpperCAmelCase      )


	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					self.config_tester.run_common_tests()


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase      )






	def 				lowercase__  (self : Dict      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.model_tester.prepare_config_and_inputs_for_common()

					def _get_word_embedding_weight(__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[Any]      ):
									if hasattr(__UpperCAmelCase,					'''weight'''      ):
													return embedding_layer.weight
									else:
													# Here we build the word embeddings weights if not exists.
													# And then we retry to get the attribute once built.
													model.build()
													if hasattr(__UpperCAmelCase,					'''weight'''      ):
																	return embedding_layer.weight
													else:
																	return None

					for model_class in self.all_model_classes:
									for size in [config.vocab_size - 10, config.vocab_size + 10]:
													# build the embeddings
													SCREAMING_SNAKE_CASE  :						List[str]      					= model_class(config=__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Any      					= _get_word_embedding_weight(__UpperCAmelCase,					model.get_input_embeddings()      )
													SCREAMING_SNAKE_CASE  :						Tuple      					= _get_word_embedding_weight(__UpperCAmelCase,					model.get_output_embeddings()      )

													# reshape the embeddings
													model.resize_token_embeddings(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Dict      					= _get_word_embedding_weight(__UpperCAmelCase,					model.get_input_embeddings()      )
													SCREAMING_SNAKE_CASE  :						Tuple      					= _get_word_embedding_weight(__UpperCAmelCase,					model.get_output_embeddings()      )

													# check that the resized embeddings size matches the desired size.
													SCREAMING_SNAKE_CASE  :						List[str]      					= size if size is not None else config.vocab_size

													self.assertEqual(new_input_embeddings.shape[0],					__UpperCAmelCase      )

													# check that weights remain the same after resizing
													SCREAMING_SNAKE_CASE  :						int      					= True
													for pa, pa in zip(old_input_embeddings.value(),					new_input_embeddings.value()      ):
																	if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
																					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= False
													self.assertTrue(__UpperCAmelCase      )

													if old_output_embeddings is not None and new_output_embeddings is not None:
																	self.assertEqual(new_output_embeddings.shape[0],					__UpperCAmelCase      )

																	SCREAMING_SNAKE_CASE  :						int      					= True
																	for pa, pa in zip(old_output_embeddings.value(),					new_output_embeddings.value()      ):
																					if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
																									SCREAMING_SNAKE_CASE  :						Any      					= False
																	self.assertTrue(__UpperCAmelCase      )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				return tf.constant(_SCREAMING_SNAKE_CASE    ,	dtype=tf.intaa       )


@require_tf
class 	a__     (				unittest.TestCase	):
	__magic_name__		:    Union[str, Any]	    =     99


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= tf.ones((4, 1),					dtype=tf.intaa      ) * 2
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= tf.concat([ids_tensor((4, 6),					self.vocab_size - 3      ) + 3, eos_column_vector],					axis=1      )
					SCREAMING_SNAKE_CASE  :						str      					= input_ids.shape[0]
					SCREAMING_SNAKE_CASE  :						Dict      					= OPTConfig(
					    vocab_size=self.vocab_size,					hidden_size=24,					num_hidden_layers=2,					num_attention_heads=2,					ffn_dim=32,					max_position_embeddings=48,					eos_token_id=2,					pad_token_id=1,					bos_token_id=0,					)
					return config, input_ids, batch_size



@require_sentencepiece
@require_tf
class 	a__     (				unittest.TestCase	):


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= TFOPTModel.from_pretrained('''facebook/opt-350m'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= tf.not_equal(__UpperCAmelCase,					model.config.pad_token_id      )
					with tf.GradientTape():
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(input_ids=__UpperCAmelCase,					attention_mask=__UpperCAmelCase      ).last_hidden_state
					SCREAMING_SNAKE_CASE  :						List[str]      					= (1, 11, 512)
					self.assertEqual(output.shape,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= tf.constant(
					    [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]      )
					self.assertTrue(np.allclose(output[:, :3, :3],					__UpperCAmelCase,					atol=4e-3      )      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= tf.function(__UpperCAmelCase,					jit_compile=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= xla_generate(__UpperCAmelCase,					__UpperCAmelCase      )[0]
					self.assertTrue(np.allclose(output[:, :3, :3],					__UpperCAmelCase,					atol=4e-2      )      )



@require_tf
@slow
class 	a__     (				unittest.TestCase	):


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

					"""simple docstring"""

					super().setUp()
					SCREAMING_SNAKE_CASE  :						Any      					= '''facebook/opt-350m'''






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= TFOPTForCausalLM.from_pretrained(self.path_model      )
					SCREAMING_SNAKE_CASE  :						str      					= GPTaTokenizer.from_pretrained(self.path_model      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [
					    '''Today is a beautiful day and I want to''',
					    '''In the city of''',
					    '''Paris is the capital of France and''',
					    '''Computers and mobile phones have taken''',
					]
					# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
					SCREAMING_SNAKE_CASE  :						int      					= tokenizer(__UpperCAmelCase,					return_tensors='''tf''',					padding=__UpperCAmelCase,					add_special_tokens=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= tf.math.reduce_mean(model(inputs.input_ids,					attention_mask=inputs.attention_mask      )[0],					axis=-1      )
					SCREAMING_SNAKE_CASE  :						int      					= tf.constant(
					    [
					        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
					        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
					        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
					        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
					    ]      )
					self.assertTrue(np.allclose(__UpperCAmelCase,					__UpperCAmelCase,					atol=1e-4      )      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= tf.function(__UpperCAmelCase,					jit_compile=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= tf.math.reduce_mean(xla_generate(inputs.input_ids,					attention_mask=inputs.attention_mask      )[0],					axis=-1      )
					self.assertTrue(np.allclose(__UpperCAmelCase,					__UpperCAmelCase,					atol=1e-4      )      )



@require_tf
@slow
class 	a__     (				unittest.TestCase	):


	@property
	def 				lowercase__  (self : Tuple      )     ->			List[Any]:

					"""simple docstring"""

					return [
					    "Today is a beautiful day and I want",
					    "In the city of",
					    "Paris is the capital of France and",
					    "Computers and mobile phones have taken",
					]


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''facebook/opt-125m'''

					SCREAMING_SNAKE_CASE  :						str      					= [
					    '''Today is a beautiful day and I want to''',
					    '''In the city of New York, the city''',
					    '''Paris is the capital of France and the capital''',
					    '''Computers and mobile phones have taken over the''',
					]

					SCREAMING_SNAKE_CASE  :						Any      					= []
					SCREAMING_SNAKE_CASE  :						List[Any]      					= GPTaTokenizer.from_pretrained(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= TFOPTForCausalLM.from_pretrained(__UpperCAmelCase      )

					for prompt in self.prompts:
									SCREAMING_SNAKE_CASE  :						Tuple      					= tokenizer(__UpperCAmelCase,					return_tensors='''tf'''      ).input_ids

									SCREAMING_SNAKE_CASE  :						List[Any]      					= model.generate(__UpperCAmelCase,					max_length=10      )

									SCREAMING_SNAKE_CASE  :						List[Any]      					= tokenizer.batch_decode(__UpperCAmelCase,					skip_special_tokens=__UpperCAmelCase      )
									predicted_outputs += generated_string

					self.assertListEqual(__UpperCAmelCase,					__UpperCAmelCase      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''facebook/opt-350m'''

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= GPTaTokenizer.from_pretrained(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= TFOPTForCausalLM.from_pretrained(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''left'''

					# use different length sentences to test batching
					SCREAMING_SNAKE_CASE  :						List[str]      					= [
					    '''Hello, my dog is a little''',
					    '''Today, I''',
					]

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= tokenizer(__UpperCAmelCase,					return_tensors='''tf''',					padding=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= inputs['''input_ids''']

					SCREAMING_SNAKE_CASE  :						Any      					= model.generate(input_ids=__UpperCAmelCase,					attention_mask=inputs['''attention_mask''']      )

					SCREAMING_SNAKE_CASE  :						Dict      					= tokenizer(sentences[0],					return_tensors='''tf'''      ).input_ids
					SCREAMING_SNAKE_CASE  :						Dict      					= model.generate(input_ids=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= inputs_non_padded.shape[-1] - tf.math.reduce_sum(
					    tf.cast(inputs['''attention_mask'''][-1],					tf.intaa      )      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= tokenizer(sentences[1],					return_tensors='''tf'''      ).input_ids
					SCREAMING_SNAKE_CASE  :						Any      					= model.generate(input_ids=__UpperCAmelCase,					max_length=model.config.max_length - num_paddings      )

					SCREAMING_SNAKE_CASE  :						str      					= tokenizer.batch_decode(__UpperCAmelCase,					skip_special_tokens=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= tokenizer.decode(output_non_padded[0],					skip_special_tokens=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= tokenizer.decode(output_padded[0],					skip_special_tokens=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= [
					    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
					    '''Today, I was in the middle of a conversation with a friend about the''',
					]
					self.assertListEqual(__UpperCAmelCase,					__UpperCAmelCase      )
					self.assertListEqual(__UpperCAmelCase,					[non_padded_sentence, padded_sentence]      )






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= '''facebook/opt-350m'''

					SCREAMING_SNAKE_CASE  :						Tuple      					= [
					    '''Today is a beautiful day and I want to''',
					    '''In the city of San Francisco, the city''',
					    '''Paris is the capital of France and the capital''',
					    '''Computers and mobile phones have taken over the''',
					]

					SCREAMING_SNAKE_CASE  :						Tuple      					= []
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= GPTaTokenizer.from_pretrained(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= TFOPTForCausalLM.from_pretrained(__UpperCAmelCase      )

					for prompt in self.prompts:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= tokenizer(__UpperCAmelCase,					return_tensors='''tf'''      ).input_ids

									SCREAMING_SNAKE_CASE  :						List[Any]      					= model.generate(__UpperCAmelCase,					max_length=10      )

									SCREAMING_SNAKE_CASE  :						Optional[int]      					= tokenizer.batch_decode(__UpperCAmelCase,					skip_special_tokens=__UpperCAmelCase      )
									predicted_outputs += generated_string

					self.assertListEqual(__UpperCAmelCase,					__UpperCAmelCase      )









'''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




