
'''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 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							XGLMConfig
				lowerCAmelCase_     			=							{}
				lowerCAmelCase_     			=							"""gelu"""







				def __init__(       self						,				A_						,				A_=14						,				A_=7						,				A_=True						,				A_=True						,				A_=True						,				A_=99						,				A_=32						,				A_=2						,				A_=4						,				A_=37						,				A_="gelu"						,				A_=0.1						,				A_=0.1						,				A_=512						,				A_=0.02						,				)->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     seq_length
											UpperCamelCase   			=     is_training
											UpperCamelCase   			=     use_input_mask
											UpperCamelCase   			=     use_labels
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     ffn_dim
											UpperCamelCase   			=     activation_function
											UpperCamelCase   			=     activation_dropout
											UpperCamelCase   			=     attention_dropout
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     None
											UpperCamelCase   			=     0
											UpperCamelCase   			=     2
											UpperCamelCase   			=     1







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







											'''simple docstring'''






											return XGLMConfig.from_pretrained('facebook/xglm-564M'     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     tf.clip_by_value(
											    ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size     )						,				clip_value_min=0						,				clip_value_max=3     )

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

											UpperCamelCase   			=     self.get_config()

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

											return (
											    config,
											    input_ids,
											    input_mask,
											    head_mask,
											)







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







											'''simple docstring'''






											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=A_						,				bos_token_id=self.bos_token_id						,				eos_token_id=self.eos_token_id						,				pad_token_id=self.pad_token_id						,				return_dict=A_						,				)







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     self.prepare_config_and_inputs()

											(
											    (
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	
											)   			=     config_and_inputs

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

											return config, inputs_dict


@require_tf
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
				lowerCAmelCase_     			=							(TFXGLMForCausalLM,) if is_tf_available() else ()
				lowerCAmelCase_     			=							(
				    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
				)
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     TFXGLMModelTester(self     )
											UpperCamelCase   			=     ConfigTester(self						,				config_class=A_						,				n_embd=37     )







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											self.config_tester.run_common_tests()







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







											'''simple docstring'''






											for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCamelCase   			=     TFXGLMModel.from_pretrained(A_     )
																		self.assertIsNotNone(A_     )







				@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'     )
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											super().test_resize_token_embeddings()


@require_tf
class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				@slow
				def 						UpperCAmelCase_    (       self						,				A_=True     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'     )
											UpperCamelCase   			=     tf.convert_to_tensor([[2, 268, 9865]]						,				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
											UpperCamelCase   			=     [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
											# fmt: on
											UpperCamelCase   			=     model.generate(A_						,				do_sample=A_						,				num_beams=1     )
											if verify_outputs:
																		self.assertListEqual(output_ids[0].numpy().tolist()						,				A_     )







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







											'''simple docstring'''






											UpperCamelCase   			=     XGLMTokenizer.from_pretrained('facebook/xglm-564M'     )
											UpperCamelCase   			=     TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'     )

											tf.random.set_seed(0     )
											UpperCamelCase   			=     tokenizer('Today is a nice day and'						,				return_tensors='tf'     )
											UpperCamelCase   			=     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'     ):
																		UpperCamelCase   			=     model.generate(A_						,				do_sample=A_						,				seed=[7, 0]     )
											UpperCamelCase   			=     tokenizer.decode(output_ids[0]						,				skip_special_tokens=A_     )

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







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







											'''simple docstring'''






											UpperCamelCase   			=     TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'     )
											UpperCamelCase   			=     XGLMTokenizer.from_pretrained('facebook/xglm-564M'     )

											UpperCamelCase   			=     'left'

											# use different length sentences to test batching
											UpperCamelCase   			=     [
											    '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',
											]

											UpperCamelCase   			=     tokenizer(A_						,				return_tensors='tf'						,				padding=A_     )
											UpperCamelCase   			=     inputs['input_ids']

											UpperCamelCase   			=     model.generate(input_ids=A_						,				attention_mask=inputs['attention_mask']						,				max_new_tokens=12     )

											UpperCamelCase   			=     tokenizer(sentences[0]						,				return_tensors='tf'     ).input_ids
											UpperCamelCase   			=     model.generate(input_ids=A_						,				max_new_tokens=12     )

											UpperCamelCase   			=     tokenizer(sentences[1]						,				return_tensors='tf'     ).input_ids
											UpperCamelCase   			=     model.generate(input_ids=A_						,				max_new_tokens=12     )

											UpperCamelCase   			=     tokenizer.batch_decode(A_						,				skip_special_tokens=A_     )
											UpperCamelCase   			=     tokenizer.decode(output_non_padded[0]						,				skip_special_tokens=A_     )
											UpperCamelCase   			=     tokenizer.decode(output_padded[0]						,				skip_special_tokens=A_     )

											UpperCamelCase   			=     [
											    '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(A_						,				A_     )
											self.assertListEqual(A_						,				[non_padded_sentence, padded_sentence]     )






'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler


@dataclass
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							0
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							3.0


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											self.assertDictEqual(MockClass().to_kwargs()						,				{}     )
											self.assertDictEqual(MockClass(a=2     ).to_kwargs()						,				{'a': 2}     )
											self.assertDictEqual(MockClass(a=2						,				b=A_     ).to_kwargs()						,				{'a': 2, 'b': True}     )
											self.assertDictEqual(MockClass(a=2						,				c=2.25     ).to_kwargs()						,				{'a': 2, 'c': 2.25}     )







				@require_cuda
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     GradScalerKwargs(init_scale=1024						,				growth_factor=2     )
											AcceleratorState._reset_state()
											UpperCamelCase   			=     Accelerator(mixed_precision='fp16'						,				kwargs_handlers=[scaler_handler]     )
											print(accelerator.use_fpaa     )
											UpperCamelCase   			=     accelerator.scaler

											# Check the kwargs have been applied
											self.assertEqual(scaler._init_scale						,				1_024.0     )
											self.assertEqual(scaler._growth_factor						,				2.0     )

											# Check the other values are at the default
											self.assertEqual(scaler._backoff_factor						,				0.5     )
											self.assertEqual(scaler._growth_interval						,				2000     )
											self.assertEqual(scaler._enabled						,				A_     )







				@require_multi_gpu
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
											execute_subprocess_async(A_						,				env=os.environ.copy()     )


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						lowerCAmelCase					:    List[str]           = Accelerator(kwargs_handlers=[ddp_scaler])
						lowerCAmelCase					:    List[Any]           = torch.nn.Linear(1_00, 2_00)
						lowerCAmelCase					:    int           = accelerator.prepare(model)

						# Check the values changed in kwargs
						lowerCAmelCase					:    Dict           = ''
						lowerCAmelCase					:    Dict           = model.bucket_bytes_cap // (10_24 * 10_24)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)




