



"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
			import tensorflow as tf

			from transformers.models.xglm.modeling_tf_xglm import (
			    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFXGLMForCausalLM,
			    TFXGLMModel,
			)


@require_tf
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__   :       List[Any]										= XGLMConfig
					lowercase__   :       Optional[Any]										= {}
					lowercase__   :       Optional[int]										= 'gelu'

					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=1_4				,       lowerCamelCase__=7				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=9_9				,       lowerCamelCase__=3_2				,       lowerCamelCase__=2				,       lowerCamelCase__=4				,       lowerCamelCase__=3_7				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=5_1_2				,       lowerCamelCase__=0.0_2				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				seq_length
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_input_mask
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				d_model
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				ffn_dim
									_lowerCamelCase        =				activation_function
									_lowerCamelCase        =				activation_dropout
									_lowerCamelCase        =				attention_dropout
									_lowerCamelCase        =				max_position_embeddings
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				None
									_lowerCamelCase        =				0
									_lowerCamelCase        =				2
									_lowerCamelCase        =				1

					def 			snake_case__      (    self   ):
									return XGLMConfig.from_pretrained('''facebook/xglm-564M'''   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				tf.clip_by_value(
									    ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   )				,       clip_value_min=0				,       clip_value_max=3   )

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

									_lowerCamelCase        =				self.get_config()

									_lowerCamelCase        =				floats_tensor([self.num_hidden_layers, self.num_attention_heads]				,       2   )

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									)

					def 			snake_case__      (    self   ):
									return XGLMConfig(
									    vocab_size=self.vocab_size				,       d_model=self.hidden_size				,       num_layers=self.num_hidden_layers				,       attention_heads=self.num_attention_heads				,       ffn_dim=self.ffn_dim				,       activation_function=self.activation_function				,       activation_dropout=self.activation_dropout				,       attention_dropout=self.attention_dropout				,       max_position_embeddings=self.max_position_embeddings				,       initializer_range=self.initializer_range				,       use_cache=lowerCamelCase__				,       bos_token_id=self.bos_token_id				,       eos_token_id=self.eos_token_id				,       pad_token_id=self.pad_token_id				,       return_dict=lowerCamelCase__				,       )





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()

									(
									    (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     
									)        =				config_and_inputs

									_lowerCamelCase        =				{
									    '''input_ids''': input_ids,
									    '''head_mask''': head_mask,
									}

									return config, inputs_dict


@require_tf
class 							lowerCamelCase_(   A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Optional[int]										= (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
					lowercase__   :       Tuple										= (TFXGLMForCausalLM,) if is_tf_available() else ()
					lowercase__   :       Union[str, Any]										= (
					    {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
					)
					lowercase__   :       int										= False
					lowercase__   :       Dict										= False
					lowercase__   :       str										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFXGLMModelTester(self   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__				,       n_embd=3_7   )

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

					@slow
					def 			snake_case__      (    self   ):
									for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase        =				TFXGLMModel.from_pretrained(lowerCamelCase__   )
													self.assertIsNotNone(lowerCamelCase__   )





					@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''   )
					def 			snake_case__      (    self   ):
									super().test_resize_token_embeddings()


@require_tf
class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					@slow
					def 			snake_case__      (    self				,       lowerCamelCase__=True   ):
									_lowerCamelCase        =				TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''   )
									_lowerCamelCase        =				tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]				,       dtype=tf.intaa   )  # The dog
									# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
									# fmt: off
									_lowerCamelCase        =				[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
									# fmt: on
									_lowerCamelCase        =				model.generate(lowerCamelCase__				,       do_sample=lowerCamelCase__				,       num_beams=1   )
									if verify_outputs:
													self.assertListEqual(output_ids[0].numpy().tolist()				,       lowerCamelCase__   )

					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''   )
									_lowerCamelCase        =				TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''   )

									tf.random.set_seed(0   )
									_lowerCamelCase        =				tokenizer('''Today is a nice day and'''				,       return_tensors='''tf'''   )
									_lowerCamelCase        =				tokenized.input_ids
									# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
									with tf.device(''':/CPU:0'''   ):
													_lowerCamelCase        =				model.generate(lowerCamelCase__				,       do_sample=lowerCamelCase__				,       seed=[7, 0]   )
									_lowerCamelCase        =				tokenizer.decode(output_ids[0]				,       skip_special_tokens=lowerCamelCase__   )

									_lowerCamelCase        =				(
									    '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
									)
									self.assertEqual(lowerCamelCase__				,       lowerCamelCase__   )





					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''   )
									_lowerCamelCase        =				XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''   )

									_lowerCamelCase        =				'''left'''

									# use different length sentences to test batching
									_lowerCamelCase        =				[
									    '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
									    '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
									    '''regardless of whether left padding is applied or not. When''',
									    '''Hello, my dog is a little''',
									]

									_lowerCamelCase        =				tokenizer(lowerCamelCase__				,       return_tensors='''tf'''				,       padding=lowerCamelCase__   )
									_lowerCamelCase        =				inputs['''input_ids''']

									_lowerCamelCase        =				model.generate(input_ids=lowerCamelCase__				,       attention_mask=inputs['''attention_mask''']				,       max_new_tokens=1_2   )

									_lowerCamelCase        =				tokenizer(sentences[0]				,       return_tensors='''tf'''   ).input_ids
									_lowerCamelCase        =				model.generate(input_ids=lowerCamelCase__				,       max_new_tokens=1_2   )

									_lowerCamelCase        =				tokenizer(sentences[1]				,       return_tensors='''tf'''   ).input_ids
									_lowerCamelCase        =				model.generate(input_ids=lowerCamelCase__				,       max_new_tokens=1_2   )

									_lowerCamelCase        =				tokenizer.batch_decode(lowerCamelCase__				,       skip_special_tokens=lowerCamelCase__   )
									_lowerCamelCase        =				tokenizer.decode(output_non_padded[0]				,       skip_special_tokens=lowerCamelCase__   )
									_lowerCamelCase        =				tokenizer.decode(output_padded[0]				,       skip_special_tokens=lowerCamelCase__   )

									_lowerCamelCase        =				[
									    '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
									    '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
									    '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
									    '''a single''',
									    '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
									]
									self.assertListEqual(lowerCamelCase__				,       lowerCamelCase__   )
									self.assertListEqual(lowerCamelCase__				,       [non_padded_sentence, padded_sentence]   )





"""simple docstring"""
import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=3.6   ):
									_lowerCamelCase        =				tokenizer
									_lowerCamelCase        =				tokenizer.bos_token_id
									_lowerCamelCase        =				dataset
									_lowerCamelCase        =				seq_length
									_lowerCamelCase        =				seq_length * chars_per_token * num_of_sequences





					def __iter__(    self   ):
									_lowerCamelCase        =				iter(self.dataset   )
									_lowerCamelCase        =				True
									while more_examples:
													_lowerCamelCase  ,     _lowerCamelCase        =				[], 0
													while True:
																	if buffer_len >= self.input_characters:
																					break
																	try:
																					buffer.append(next(lowerCamelCase__   )['''content''']   )
																					buffer_len += len(buffer[-1]   )
																	except StopIteration:
																					_lowerCamelCase        =				False
																					break
													_lowerCamelCase        =				tokenizer(lowerCamelCase__				,       truncation=lowerCamelCase__   )['''input_ids''']
													_lowerCamelCase        =				[]
													for tokenized_input in tokenized_inputs:
																	all_token_ids.extend(tokenized_input + [self.concat_token_id]   )
													for i in range(0				,       len(lowerCamelCase__   )				,       self.seq_length   ):
																	_lowerCamelCase        =				all_token_ids[i : i + self.seq_length]
																	if len(lowerCamelCase__   ) == self.seq_length:
																					yield torch.tensor(lowerCamelCase__   )



def        lowerCAmelCase_(   lowercase_     :							Any							)	->       Optional[Any]:
				_lowerCamelCase        =				{'''streaming''': True}
				_lowerCamelCase        =				load_dataset(args.dataset_name						,   split='''train'''						,   **lowercase_							)
				_lowerCamelCase        =				ConstantLengthDataset(lowercase_						,   lowercase_						,   seq_length=args.seq_length							)
				_lowerCamelCase        =				DataLoader(lowercase_						,   batch_size=args.batch_size							)
				return eval_dataloader



def        lowerCAmelCase_(   lowercase_     :							Tuple							)	->       str:
				model.eval()
				_lowerCamelCase        =				[]
				for step, batch in enumerate(lowercase_							):
								with torch.no_grad():
												_lowerCamelCase        =				model(lowercase_						,   labels=lowercase_							)
								_lowerCamelCase        =				outputs.loss.repeat(args.batch_size							)
								losses.append(accelerator.gather(lowercase_							)							)

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				_lowerCamelCase        =				torch.mean(torch.cat(lowercase_							)							)
				try:
								_lowerCamelCase        =				torch.exp(lowercase_							)
				except OverflowError:
								_lowerCamelCase        =				float('''inf'''							)
				return loss.item(), perplexity.item()


# Setup Accelerator
__SCREAMING_SNAKE_CASE  :						Dict    							=      Accelerator()

# Parse configuration
__SCREAMING_SNAKE_CASE  :						Tuple    							=      HfArgumentParser(EvaluationArguments)
__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      parser.parse_args()
set_seed(args.seed)

# Logging
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      logging.getLogger(__name__)
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)

# Load model and tokenizer
__SCREAMING_SNAKE_CASE  :						str    							=      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__SCREAMING_SNAKE_CASE  :						str    							=      create_dataloader(args)

# Prepare everything with our `accelerator`.
__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						Any    							=      accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						List[str]    							=      evaluate(args)




logger.info(F"""loss/eval: {eval_loss}, perplexity: {perplexity}""")
