





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

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


if is_tf_available():
	import tensorflow as tf

	from transformers.models.gptj.modeling_tf_gptj import (
	    TFGPTJForCausalLM,
	    TFGPTJForQuestionAnswering,
	    TFGPTJForSequenceClassification,
	    TFGPTJModel,
	    shape_list,
	)





class     _UpperCAmelCase							:
		def __init__(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				parent
					__lowerCAmelCase											=				13
					__lowerCAmelCase											=				7
					__lowerCAmelCase											=				True
					__lowerCAmelCase											=				True
					__lowerCAmelCase											=				True
					__lowerCAmelCase											=				True
					__lowerCAmelCase											=				True
					__lowerCAmelCase											=				99
					__lowerCAmelCase											=				32
					__lowerCAmelCase											=				4
					__lowerCAmelCase											=				2
					__lowerCAmelCase											=				4
					__lowerCAmelCase											=				37
					__lowerCAmelCase											=				"""gelu"""
					__lowerCAmelCase											=				0.1
					__lowerCAmelCase											=				0.1
					__lowerCAmelCase											=				5_12
					__lowerCAmelCase											=				16
					__lowerCAmelCase											=				2
					__lowerCAmelCase											=				0.02
					__lowerCAmelCase											=				3
					__lowerCAmelCase											=				4
					__lowerCAmelCase											=				None
					__lowerCAmelCase											=				self.vocab_size - 1
					__lowerCAmelCase											=				self.vocab_size - 1
					__lowerCAmelCase											=				self.vocab_size - 1
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

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

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

					__lowerCAmelCase											=				None
					if self.use_token_type_ids:
								__lowerCAmelCase											=				ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size			)

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

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

					__lowerCAmelCase											=				GPTJConfig(
					    vocab_size=self.vocab_size,n_embd=self.hidden_size,n_layer=self.num_hidden_layers,n_head=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,n_positions=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,initializer_range=self.initializer_range,bos_token_id=self.bos_token_id,eos_token_id=self.eos_token_id,pad_token_id=self.pad_token_id,rotary_dim=self.rotary_dim,return_dict=__SCREAMING_SNAKE_CASE,)

					__lowerCAmelCase											=				ids_tensor([self.num_hidden_layers, self.num_attention_heads],2			)

					return (
					    config,
					    input_ids,
					    input_mask,
					    head_mask,
					    token_type_ids,
					    mc_token_ids,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				TFGPTJModel(config=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				{
					    """input_ids""": input_ids,
					    """attention_mask""": input_mask,
					    """token_type_ids""": token_type_ids,
					}
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				[input_ids, None, input_mask]  # None is the input for 'past'
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE			)

					self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				TFGPTJModel(config=__SCREAMING_SNAKE_CASE			)

					# first forward pass
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE,use_cache=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE,use_cache=__SCREAMING_SNAKE_CASE			)

					self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE			) == len(__SCREAMING_SNAKE_CASE			)			)
					self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE			) == len(__SCREAMING_SNAKE_CASE			) + 1			)

					__lowerCAmelCase						,       __lowerCAmelCase											=				outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					__lowerCAmelCase											=				ids_tensor((self.batch_size, 1),config.vocab_size			)
					__lowerCAmelCase											=				ids_tensor([self.batch_size, 1],self.type_vocab_size			)

					# append to next input_ids and token_type_ids
					__lowerCAmelCase											=				tf.concat([input_ids, next_tokens],axis=-1			)
					__lowerCAmelCase											=				tf.concat([token_type_ids, next_token_types],axis=-1			)

					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE			)["""last_hidden_state"""]
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE,past_key_values=__SCREAMING_SNAKE_CASE			)[
					    """last_hidden_state"""
					]

					# select random slice
					__lowerCAmelCase											=				int(ids_tensor((1,),shape_list(__SCREAMING_SNAKE_CASE			)[-1]			)			)
					__lowerCAmelCase											=				output_from_no_past[:, -1, random_slice_idx]
					__lowerCAmelCase											=				output_from_past[:, 0, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,rtol=1e-6			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				TFGPTJModel(config=__SCREAMING_SNAKE_CASE			)

					# create attention mask
					__lowerCAmelCase											=				self.seq_length // 2
					__lowerCAmelCase											=				tf.ones((self.batch_size, half_seq_length),dtype=tf.intaa			)
					__lowerCAmelCase											=				tf.zeros((self.batch_size, self.seq_length - half_seq_length),dtype=tf.intaa			)
					__lowerCAmelCase											=				tf.concat([attn_mask_begin, attn_mask_end],axis=1			)

					# first forward pass
					__lowerCAmelCase						,       __lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE			).to_tuple()

					# create hypothetical next token and extent to next_input_ids
					__lowerCAmelCase											=				ids_tensor((self.batch_size, 1),config.vocab_size			)

					# change a random masked slice from input_ids
					__lowerCAmelCase											=				ids_tensor((1,),__SCREAMING_SNAKE_CASE			).numpy() + 1
					__lowerCAmelCase											=				ids_tensor((self.batch_size, self.seq_length),config.vocab_size			)
					__lowerCAmelCase											=				tf.range(self.seq_length			) == (self.seq_length - random_seq_idx_to_change)
					__lowerCAmelCase											=				tf.transpose(
					    tf.broadcast_to(tf.expand_dims(__SCREAMING_SNAKE_CASE,-1			),(self.seq_length, self.batch_size)			)			)
					__lowerCAmelCase											=				tf.where(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)

					# append to next input_ids and attn_mask
					__lowerCAmelCase											=				tf.concat([input_ids, next_tokens],axis=-1			)
					__lowerCAmelCase											=				tf.concat([attn_mask, tf.ones((shape_list(__SCREAMING_SNAKE_CASE			)[0], 1),dtype=tf.intaa			)],axis=1			)

					# get two different outputs
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE			)["""last_hidden_state"""]
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE,past_key_values=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE			)[
					    """last_hidden_state"""
					]

					# select random slice
					__lowerCAmelCase											=				int(ids_tensor((1,),shape_list(__SCREAMING_SNAKE_CASE			)[-1]			)			)
					__lowerCAmelCase											=				output_from_no_past[:, -1, random_slice_idx]
					__lowerCAmelCase											=				output_from_past[:, 0, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,rtol=1e-12			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				TFGPTJModel(config=__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				input_ids[:1, :]
					__lowerCAmelCase											=				input_mask[:1, :]
					__lowerCAmelCase											=				token_type_ids[:1, :]
					__lowerCAmelCase											=				1

					# first forward pass
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE,use_cache=__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase						,       __lowerCAmelCase											=				outputs.to_tuple()

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

					# append to next input_ids and token_type_ids
					__lowerCAmelCase											=				tf.concat([input_ids, next_tokens],axis=-1			)
					__lowerCAmelCase											=				tf.concat([input_mask, next_attn_mask],axis=-1			)
					__lowerCAmelCase											=				tf.concat([token_type_ids, next_token_types],axis=-1			)

					__lowerCAmelCase											=				model(
					    __SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE			)["""last_hidden_state"""]
					__lowerCAmelCase											=				model(
					    __SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,past_key_values=__SCREAMING_SNAKE_CASE,)["""last_hidden_state"""]
					self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]			)

					# select random slice
					__lowerCAmelCase											=				int(ids_tensor((1,),shape_list(__SCREAMING_SNAKE_CASE			)[-1]			)			)
					__lowerCAmelCase											=				output_from_no_past[:, -3:, random_slice_idx]
					__lowerCAmelCase											=				output_from_past[:, :, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,rtol=1e-3			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				TFGPTJForCausalLM(config=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				{
					    """input_ids""": input_ids,
					    """attention_mask""": input_mask,
					    """token_type_ids""": token_type_ids,
					}
					__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE			)
					self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)			)







		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.prepare_config_and_inputs()

					(
					    (
					    __lowerCAmelCase
					)						,       (
					    __lowerCAmelCase
					)						,       (
					    __lowerCAmelCase
					)						,       (
					    __lowerCAmelCase
					)						,       (
					    __lowerCAmelCase
					)						,       (
					    __lowerCAmelCase
					)						,       (
					    __lowerCAmelCase
					)						,       (
					    __lowerCAmelCase
					)						,       (
					    __lowerCAmelCase
					)						,       
					)											=				config_and_inputs

					__lowerCAmelCase											=				{
					    """input_ids""": input_ids,
					    """token_type_ids""": token_type_ids,
					    """attention_mask""": input_mask,
					}
					return config, inputs_dict





@require_tf
class     _UpperCAmelCase							(						lowerCAmelCase_	,					lowerCAmelCase_	,					lowerCAmelCase_	,					unittest.TestCase   ):
		a   :   int     =(
		    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
		    if is_tf_available()
		    else ()
		)

		a   :   Dict     =(TFGPTJForCausalLM,) if is_tf_available() else ()
		a   :   Optional[Any]     =(
		    {
		        """feature-extraction""": TFGPTJModel,
		        """question-answering""": TFGPTJForQuestionAnswering,
		        """text-classification""": TFGPTJForSequenceClassification,
		        """text-generation""": TFGPTJForCausalLM,
		        """zero-shot""": TFGPTJForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		a   :   Tuple     =False
		a   :   Optional[Any]     =False
		a   :   List[Any]     =False
		a   :   Any     =False
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					if (
					    pipeline_test_casse_name == "QAPipelineTests"
					    and tokenizer_name is not None
					    and not tokenizer_name.endswith("""Fast"""			)
					):
								# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
								# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
								# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
								return True

					return False
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				TFGPTJModelTester(self			)
					__lowerCAmelCase											=				ConfigTester(self,config_class=__SCREAMING_SNAKE_CASE,n_embd=37			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

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



					'''simple docstring'''

					__lowerCAmelCase											=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model(*__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_past(*__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_attention_mask_past(*__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_past_large_inputs(*__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_lm_head_model(*__SCREAMING_SNAKE_CASE			)
		@slow
		@unittest.skipIf(
		    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""			)			) > 0,"""skip testing on GPU for now to avoid GPU OOM.""",)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B""",from_pt=__SCREAMING_SNAKE_CASE			)
					self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)







		@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					super().test_resize_token_embeddings()





@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class     _UpperCAmelCase							(						unittest.TestCase   ):
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""",from_pt=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				tf.convert_to_tensor([[4_64, 32_90]],dtype=tf.intaa			)  # The dog
					# fmt: off
					# The dog is a man's best friend. It is a loyal companion, and it is a friend
					__lowerCAmelCase											=				[4_64, 32_90, 3_18, 2_57, 5_82, 3_38, 12_66, 15_45, 13, 6_32, 3_18, 2_57, 91_12, 1_51_85, 11, 2_90, 3_40, 3_18, 2_57, 15_45]
					# fmt: on
					__lowerCAmelCase											=				model.generate(__SCREAMING_SNAKE_CASE,do_sample=__SCREAMING_SNAKE_CASE			)
					self.assertListEqual(output_ids[0].numpy().tolist(),__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B""",revision="""float16"""			)
					__lowerCAmelCase											=				TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""",revision="""float16""",from_pt=__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				tokenizer("""Today is a nice day and""",return_tensors="""tf"""			)
					# forces the generation to happen on CPU, to avoid GPU-related quirks
					with tf.device(""":/CPU:0"""			):
								__lowerCAmelCase											=				model.generate(**__SCREAMING_SNAKE_CASE,do_sample=__SCREAMING_SNAKE_CASE,seed=[42, 0]			)
					__lowerCAmelCase											=				tokenizer.decode(output_ids[0],skip_special_tokens=__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				"""Today is a nice day and I’m going to go for a walk. I’"""
					self.assertEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""",revision="""float16""",from_pt=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B""",revision="""float16"""			)

					__lowerCAmelCase											=				"""left"""

					# Define PAD Token = EOS Token = 50256
					__lowerCAmelCase											=				tokenizer.eos_token
					__lowerCAmelCase											=				model.config.eos_token_id

					# use different length sentences to test batching
					__lowerCAmelCase											=				[
					    """Hello, my dog is a little""",
					    """Today, I""",
					]
					__lowerCAmelCase											=				[
					    """Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia""",
					    """Today, I’m going to be talking about a topic that’""",
					]
					return model, tokenizer, sentences, expected_output_sentences
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				self._get_beam_search_test_objects()

					__lowerCAmelCase											=				tokenizer(__SCREAMING_SNAKE_CASE,return_tensors="""tf""",padding=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				model.generate(**__SCREAMING_SNAKE_CASE,do_sample=__SCREAMING_SNAKE_CASE,num_beams=2			)
					__lowerCAmelCase											=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE,skip_special_tokens=__SCREAMING_SNAKE_CASE			)
					self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				self._get_beam_search_test_objects()

					__lowerCAmelCase											=				tokenizer(__SCREAMING_SNAKE_CASE,return_tensors="""tf""",padding=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				tokenizer(sentences[0],return_tensors="""tf"""			)
					__lowerCAmelCase											=				model.generate(**__SCREAMING_SNAKE_CASE,do_sample=__SCREAMING_SNAKE_CASE,num_beams=2			)
					__lowerCAmelCase											=				(
					    shape_list(inputs_non_padded["""input_ids"""]			)[-1]
					    - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1],tf.intaa			)			).numpy()
					)
					__lowerCAmelCase											=				tokenizer(sentences[1],return_tensors="""tf"""			)
					__lowerCAmelCase											=				model.generate(
					    **__SCREAMING_SNAKE_CASE,do_sample=__SCREAMING_SNAKE_CASE,num_beams=2,max_length=model.config.max_length - num_paddings			)
					__lowerCAmelCase											=				tokenizer.decode(output_non_padded[0],skip_special_tokens=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				tokenizer.decode(output_padded[0],skip_special_tokens=__SCREAMING_SNAKE_CASE			)
					self.assertListEqual(__SCREAMING_SNAKE_CASE,[non_padded_sentence, padded_sentence]			)







		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				self._get_beam_search_test_objects()

					__lowerCAmelCase											=				tokenizer(__SCREAMING_SNAKE_CASE,return_tensors="""tf""",padding=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				tf.function(model.generate,jit_compile=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				xla_generate(**__SCREAMING_SNAKE_CASE,do_sample=__SCREAMING_SNAKE_CASE,num_beams=2			)
					__lowerCAmelCase											=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE,skip_special_tokens=__SCREAMING_SNAKE_CASE			)
					self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)








'''simple docstring'''







from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					list:
			seed(1	)
			return [gauss(lowercase					,	lowercase	) for _ in range(lowercase	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					list:
			return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return sum(lowercase	) / instance_count





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return instance_count / total_count





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				[]  # An empty list to store all squared differences
			# iterate over number of elements in items
			for i in range(len(lowercase	)	):
						# for loop iterates over number of elements in inner layer of items
						for j in range(len(items[i]	)	):
									# appending squared differences to 'squared_diff' list
									squared_diff.append((items[i][j] - means[i]) ** 2	)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
			__lowerCAmelCase											=				len(lowercase	)  # Number of classes in dataset
			return 1 / (total_count - n_classes) * sum(lowercase	)





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase	)					->					list:
			__lowerCAmelCase											=				[]
			# for loop iterates over number of elements in list
			for i in range(len(lowercase	)	):
						# for loop iterates over number of inner items of each element
						for j in range(len(x_items[i]	)	):
									__lowerCAmelCase											=				[]  # to store all discriminant values of each item as a list
									# for loop iterates over number of classes we have in our dataset
									for k in range(len(lowercase	)	):
												# appending values of discriminants for each class to 'temp' list
												temp.append(
												    x_items[i][j] * (means[k] / variance)
												    - (means[k] ** 2 / (2 * variance))
												    + log(probabilities[k]	)	)
									# appending discriminant values of each item to 'results' list
									results.append(lowercase	)

			return [result.index(max(lowercase	)	) for result in results]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				sum(1 for i, j in zip(lowercase					,	lowercase	) if i == j	)
			# percentage of accuracy equals to number of correct predictions divided by number
			# of all data and multiplied by 100
			return (correct / len(lowercase	)) * 100


_a					: Optional[int]      =    TypeVar("""num""")





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase = lambda lowercase	: True					,	lowercase = None					,	)					->					num:
			while True:
						try:
									__lowerCAmelCase											=				input_type(input(lowercase	).strip() or default	)
									if condition(lowercase	):
												return user_input
									else:
												print(f'{user_input}: {err_msg}'	)
												continue
						except ValueError:
									print(
									    f'{user_input}: Incorrect input type, expected {input_type.__name__!r}'	)





def    _lowerCAmelCase						(						)					->					List[str]:
			while True:
						print(""" Linear Discriminant Analysis """.center(50					,	"""*"""	)	)
						print("""*""" * 50					,	"""\n"""	)
						print("""First of all we should specify the number of classes that"""	)
						print("""we want to generate as training dataset"""	)
						# Trying to get number of classes
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg="""Enter the number of classes (Data Groupings): """					,	err_msg="""Number of classes should be positive!"""					,	)

						print("""-""" * 100	)

						# Trying to get the value of standard deviation
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: x >= 0					,	input_msg=(
						        """Enter the value of standard deviation"""
						        """(Default value is 1.0 for all classes): """
						    )					,	err_msg="""Standard deviation should not be negative!"""					,	default="""1.0"""					,	)

						print("""-""" * 100	)

						# Trying to get number of instances in classes and theirs means to generate
						# dataset
						__lowerCAmelCase											=				[]  # An empty list to store instance counts of classes in dataset
						for i in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg=(f'Enter The number of instances for class_{i+1}: ')					,	err_msg="""Number of instances should be positive!"""					,	)
									counts.append(lowercase	)
						print("""-""" * 100	)

						# An empty list to store values of user-entered means of classes
						__lowerCAmelCase											=				[]
						for a in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	input_msg=(f'Enter the value of mean for class_{a+1}: ')					,	err_msg="""This is an invalid value."""					,	)
									user_means.append(lowercase	)
						print("""-""" * 100	)

						print("""Standard deviation: """					,	lowercase	)
						# print out the number of instances in classes in separated line
						for i, count in enumerate(lowercase					,	1	):
									print(f'Number of instances in class_{i} is: {count}'	)
						print("""-""" * 100	)

						# print out mean values of classes separated line
						for i, user_mean in enumerate(lowercase					,	1	):
									print(f'Mean of class_{i} is: {user_mean}'	)
						print("""-""" * 100	)

						# Generating training dataset drawn from gaussian distribution
						__lowerCAmelCase											=				[
						    gaussian_distribution(user_means[j]					,	lowercase					,	counts[j]	)
						    for j in range(lowercase	)
						]
						print("""Generated Normal Distribution: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Generating Ys to detecting corresponding classes
						__lowerCAmelCase											=				y_generator(lowercase					,	lowercase	)
						print("""Generated Corresponding Ys: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Calculating the value of actual mean for each class
						__lowerCAmelCase											=				[calculate_mean(counts[k]					,	x[k]	) for k in range(lowercase	)]
						# for loop iterates over number of elements in 'actual_means' list and print
						# out them in separated line
						for i, actual_mean in enumerate(lowercase					,	1	):
									print(f'Actual(Real) mean of class_{i} is: {actual_mean}'	)
						print("""-""" * 100	)

						# Calculating the value of probabilities for each class
						__lowerCAmelCase											=				[
						    calculate_probabilities(counts[i]					,	sum(lowercase	)	) for i in range(lowercase	)
						]

						# for loop iterates over number of elements in 'probabilities' list and print
						# out them in separated line
						for i, probability in enumerate(lowercase					,	1	):
									print(f'Probability of class_{i} is: {probability}'	)
						print("""-""" * 100	)

						# Calculating the values of variance for each class
						__lowerCAmelCase											=				calculate_variance(lowercase					,	lowercase					,	sum(lowercase	)	)
						print("""Variance: """					,	lowercase	)
						print("""-""" * 100	)

						# Predicting Y values
						# storing predicted Y values in 'pre_indexes' variable
						__lowerCAmelCase											=				predict_y_values(lowercase					,	lowercase					,	lowercase					,	lowercase	)
						print("""-""" * 100	)

						# Calculating Accuracy of the model
						print(f'Accuracy: {accuracy(lowercase					,	lowercase	)}'	)
						print("""-""" * 100	)
						print(""" DONE """.center(100					,	"""+"""	)	)

						if input("""Press any key to restart or 'q' for quit: """	).strip().lower() == "q":
									print("""\n""" + """GoodBye!""".center(100					,	"""-"""	) + """\n"""	)
									break
						system("""cls""" if name == """nt""" else """clear"""	)  # noqa: S605


if __name__ == "__main__":
	main()

