

"""simple docstring"""




import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   fname.split(os.path.sep )[-1]
					return re.search(r"^(.*)_\d+\.jpg$"     ,				lowercase__ ).groups()[0]





class      __UpperCamelCase					(  _A		):


		def __init__(self					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=None	,	__SCREAMING_SNAKE_CASE					:						List[Any]=None):
							A     =   file_names
							A     =   image_transform
							A     =   label_to_id


		def __len__(self					:						str):
							return len(self.file_names)


		def __getitem__(self					:						int	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]):
							A     =   self.file_names[idx]
							A     =   PIL.Image.open(__SCREAMING_SNAKE_CASE)
							A     =   raw_image.convert("RGB")
							if self.image_transform is not None:
												A     =   self.image_transform(__SCREAMING_SNAKE_CASE)
							A     =   extract_label(__SCREAMING_SNAKE_CASE)
							if self.label_to_id is not None:
												A     =   self.label_to_id[label]
							return {"image": image, "label": label}

def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					# Initialize accelerator
					A     =   Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision )

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					A     =   config["lr"]
					A     =   int(config["num_epochs"] )
					A     =   int(config["seed"] )
					A     =   int(config["batch_size"] )
					A     =   config["image_size"]
					if not isinstance(lowercase__     ,				(list, tuple) ):
										A     =   (image_size, image_size)

					# Grab all the image filenames
					A     =   [os.path.join(args.data_dir     ,				lowercase__ ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]

					# Build the label correspondences
					A     =   [extract_label(lowercase__ ) for fname in file_names]
					A     =   list(set(lowercase__ ) )
					id_to_label.sort()
					A     =   {lbl: i for i, lbl in enumerate(lowercase__ )}

					# Set the seed before splitting the data.
					np.random.seed(lowercase__ )
					torch.manual_seed(lowercase__ )
					torch.cuda.manual_seed_all(lowercase__ )

					# Split our filenames between train and validation
					A     =   np.random.permutation(len(lowercase__ ) )
					A     =   int(0.8 * len(lowercase__ ) )
					A     =   random_perm[:cut]
					A     =   random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					A     =   Compose([RandomResizedCrop(lowercase__     ,				scale=(0.5, 1.0) ), ToTensor()] )
					A     =   PetsDataset(
					    [file_names[i] for i in train_split]     ,				image_transform=lowercase__     ,				label_to_id=lowercase__ )

					# For evaluation, we use a deterministic Resize
					A     =   Compose([Resize(lowercase__ ), ToTensor()] )
					A     =   PetsDataset([file_names[i] for i in eval_split]     ,				image_transform=lowercase__     ,				label_to_id=lowercase__ )

					# Instantiate dataloaders.
					A     =   DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__     ,				num_workers=4 )
					A     =   DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__     ,				num_workers=4 )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					A     =   create_model("resnet50d"     ,				pretrained=lowercase__     ,				num_classes=len(lowercase__ ) )

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					A     =   model.to(accelerator.device )

					# Freezing the base model
					for param in model.parameters():
										A     =   False
					for param in model.get_classifier().parameters():
										A     =   True

					# We normalize the batches of images to be a bit faster.
					A     =   torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
					A     =   torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )

					# Instantiate optimizer
					A     =   torch.optim.Adam(params=model.parameters()     ,				lr=lr / 25 )

					# Instantiate learning rate scheduler
					A     =   OneCycleLR(optimizer=lowercase__     ,				max_lr=lowercase__     ,				epochs=lowercase__     ,				steps_per_epoch=len(lowercase__ ) )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					A     ,       A     ,       A     ,       A     ,       A     =   accelerator.prepare(
					    lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ )

					# Now we train the model
					for epoch in range(lowercase__ ):
										model.train()
										for step, batch in enumerate(lowercase__ ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															A     =   {k: v.to(accelerator.device ) for k, v in batch.items()}
															A     =   (batch["image"] - mean) / std
															A     =   model(lowercase__ )
															A     =   torch.nn.functional.cross_entropy(lowercase__     ,				batch["label"] )
															accelerator.backward(lowercase__ )
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

										model.eval()
										A     =   0
										A     =   0
										for _, batch in enumerate(lowercase__ ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															A     =   {k: v.to(accelerator.device ) for k, v in batch.items()}
															A     =   (batch["image"] - mean) / std
															with torch.no_grad():
																				A     =   model(lowercase__ )
															A     =   outputs.argmax(dim=-1 )
															A     ,       A     =   accelerator.gather_for_metrics((predictions, batch["label"]) )
															A     =   predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										A     =   accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}""" )
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   argparse.ArgumentParser(description="Simple example of training script." )
					parser.add_argument("--data_dir"     ,				required=lowercase__     ,				help="The data folder on disk." )
					parser.add_argument(
					    "--mixed_precision"     ,				type=lowercase__     ,				default=lowercase__     ,				choices=["no", "fp16", "bf16", "fp8"]     ,				help="Whether to use mixed precision. Choose"
					    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
					    "and an Nvidia Ampere GPU."     ,				)
					parser.add_argument(
					    "--checkpointing_steps"     ,				type=lowercase__     ,				default=lowercase__     ,				help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."     ,				)
					parser.add_argument("--cpu"     ,				action="store_true"     ,				help="If passed, will train on the CPU." )
					A     =   parser.parse_args()
					A     =   {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
					training_function(lowercase__     ,				lowercase__ )


if __name__ == "__main__":
	main()













"""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    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	List[Any],			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					parent
     __A		     =					13
     __A		     =					7
     __A		     =					True
     __A		     =					True
     __A		     =					True
     __A		     =					True
     __A		     =					True
     __A		     =					99
     __A		     =					32
     __A		     =					4
     __A		     =					2
     __A		     =					4
     __A		     =					37
     __A		     =					'''gelu'''
     __A		     =					0.1
     __A		     =					0.1
     __A		     =					5_12
     __A		     =					16
     __A		     =					2
     __A		     =					0.02
     __A		     =					3
     __A		     =					4
     __A		     =					None
     __A		     =					self.vocab_size - 1
     __A		     =					self.vocab_size - 1
     __A		     =					self.vocab_size - 1


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					ids_tensor([self.batch_size, self.seq_length],			self.vocab_size    )

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

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

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

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

     __A		     =					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=_lowerCamelCase,			)

     __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	int,			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	List[str],			*_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					TFGPTJModel(config=_lowerCamelCase    )
     __A		     =					{
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
         '''token_type_ids''': token_type_ids,
     }
     __A		     =					model(_lowerCamelCase    )

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

     __A		     =					model(_lowerCamelCase    )

     self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	int,			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Optional[int],			*_lowerCamelCase					:	List[str]    ):

     '''simple docstring'''
     __A		     =					TFGPTJModel(config=_lowerCamelCase    )

     # first forward pass
     __A		     =					model(_lowerCamelCase,			token_type_ids=_lowerCamelCase,			use_cache=_lowerCamelCase    )
     __A		     =					model(_lowerCamelCase,			token_type_ids=_lowerCamelCase    )
     __A		     =					model(_lowerCamelCase,			token_type_ids=_lowerCamelCase,			use_cache=_lowerCamelCase    )

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

     __A     ,       __A		     =					outputs.to_tuple()

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

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

     __A		     =					model(_lowerCamelCase,			token_type_ids=_lowerCamelCase    )['''last_hidden_state''']
     __A		     =					model(_lowerCamelCase,			token_type_ids=_lowerCamelCase,			past_key_values=_lowerCamelCase    )[
         '''last_hidden_state'''
     ]

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

     # test that outputs are equal for slice
     tf.debugging.assert_near(_lowerCamelCase,			_lowerCamelCase,			rtol=1e-6    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	int,			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Dict,			*_lowerCamelCase					:	Dict    ):

     '''simple docstring'''
     __A		     =					TFGPTJModel(config=_lowerCamelCase    )

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

     # first forward pass
     __A     ,       __A		     =					model(_lowerCamelCase,			attention_mask=_lowerCamelCase    ).to_tuple()

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

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

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

     # get two different outputs
     __A		     =					model(_lowerCamelCase,			attention_mask=_lowerCamelCase    )['''last_hidden_state''']
     __A		     =					model(_lowerCamelCase,			past_key_values=_lowerCamelCase,			attention_mask=_lowerCamelCase    )[
         '''last_hidden_state'''
     ]

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

     # test that outputs are equal for slice
     tf.debugging.assert_near(_lowerCamelCase,			_lowerCamelCase,			rtol=1e-12    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	int,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Tuple,			*_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					TFGPTJModel(config=_lowerCamelCase    )

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

     # first forward pass
     __A		     =					model(_lowerCamelCase,			attention_mask=_lowerCamelCase,			token_type_ids=_lowerCamelCase,			use_cache=_lowerCamelCase    )

     __A     ,       __A		     =					outputs.to_tuple()

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

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

     __A		     =					model(
         _lowerCamelCase,			token_type_ids=_lowerCamelCase,			attention_mask=_lowerCamelCase    )['''last_hidden_state''']
     __A		     =					model(
         _lowerCamelCase,			token_type_ids=_lowerCamelCase,			attention_mask=_lowerCamelCase,			past_key_values=_lowerCamelCase,			)['''last_hidden_state''']
     self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]    )

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

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	str,			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Tuple,			*_lowerCamelCase					:	int    ):

     '''simple docstring'''
     __A		     =					TFGPTJForCausalLM(config=_lowerCamelCase    )
     __A		     =					{
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
         '''token_type_ids''': token_type_ids,
     }
     __A		     =					model(_lowerCamelCase    )
     self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.vocab_size)    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					self.prepare_config_and_inputs()

     (
         (
         __A
     )     ,       (
         __A
     )     ,       (
         __A
     )     ,       (
         __A
     )     ,       (
         __A
     )     ,       (
         __A
     )     ,       (
         __A
     )     ,       (
         __A
     )     ,       (
         __A
     )     ,       
     )		     =					config_and_inputs

     __A		     =					{
         '''input_ids''': input_ids,
         '''token_type_ids''': token_type_ids,
         '''attention_mask''': input_mask,
     }
     return config, inputs_dict





@require_tf
class    snake_case						(				_lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase  ,      unittest.TestCase		):


   '''simple docstring'''

   A_		:      Union[str, Any]							=					(
       (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
       if is_tf_available()
       else ()
   )

   A_		:      Dict							=					(TFGPTJForCausalLM,) if is_tf_available() else ()
   A_		:      Dict							=					(
       {
           "feature-extraction": TFGPTJModel,
           "question-answering": TFGPTJForQuestionAnswering,
           "text-classification": TFGPTJForSequenceClassification,
           "text-generation": TFGPTJForCausalLM,
           "zero-shot": TFGPTJForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A_		:      List[str]							=					False
   A_		:      Any							=					False
   A_		:      Optional[int]							=					False
   A_		:      Union[str, Any]							=					False


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	int,			_lowerCamelCase					:	str    ):

     '''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 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					TFGPTJModelTester(self    )
     __A		     =					ConfigTester(self,			config_class=_lowerCamelCase,			n_embd=37    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_model(*_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_model_past(*_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_model_attention_mask_past(*_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_model_past_large_inputs(*_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_lm_head_model(*_lowerCamelCase    )


   @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 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					TFGPTJModel.from_pretrained('''EleutherAI/gpt-j-6B''',			from_pt=_lowerCamelCase    )
     self.assertIsNotNone(_lowerCamelCase    )






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

     '''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    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''',			from_pt=_lowerCamelCase    )
     __A		     =					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
     __A		     =					[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
     __A		     =					model.generate(_lowerCamelCase,			do_sample=_lowerCamelCase    )
     self.assertListEqual(output_ids[0].numpy().tolist(),			_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B''',			revision='''float16'''    )
     __A		     =					TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''',			revision='''float16''',			from_pt=_lowerCamelCase    )

     __A		     =					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'''    ):
       __A		     =					model.generate(**_lowerCamelCase,			do_sample=_lowerCamelCase,			seed=[42, 0]    )
     __A		     =					tokenizer.decode(output_ids[0],			skip_special_tokens=_lowerCamelCase    )

     __A		     =					'''Today is a nice day and I’m going to go for a walk. I’'''
     self.assertEqual(_lowerCamelCase,			_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''',			revision='''float16''',			from_pt=_lowerCamelCase    )
     __A		     =					AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B''',			revision='''float16'''    )

     __A		     =					'''left'''

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

     # use different length sentences to test batching
     __A		     =					[
         '''Hello, my dog is a little''',
         '''Today, I''',
     ]
     __A		     =					[
         '''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 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     # Confirms that we get the expected results with left-padded beam search
     __A     ,       __A     ,       __A     ,       __A		     =					self._get_beam_search_test_objects()

     __A		     =					tokenizer(_lowerCamelCase,			return_tensors='''tf''',			padding=_lowerCamelCase    )
     __A		     =					model.generate(**_lowerCamelCase,			do_sample=_lowerCamelCase,			num_beams=2    )
     __A		     =					tokenizer.batch_decode(_lowerCamelCase,			skip_special_tokens=_lowerCamelCase    )
     self.assertListEqual(_lowerCamelCase,			_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     # Confirms that left-padding is working properly
     __A     ,       __A     ,       __A     ,       __A		     =					self._get_beam_search_test_objects()

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






   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     # Confirms that XLA is working properly
     __A     ,       __A     ,       __A     ,       __A		     =					self._get_beam_search_test_objects()

     __A		     =					tokenizer(_lowerCamelCase,			return_tensors='''tf''',			padding=_lowerCamelCase    )
     __A		     =					tf.function(model.generate,			jit_compile=_lowerCamelCase    )
     __A		     =					xla_generate(**_lowerCamelCase,			do_sample=_lowerCamelCase,			num_beams=2    )
     __A		     =					tokenizer.batch_decode(_lowerCamelCase,			skip_special_tokens=_lowerCamelCase    )
     self.assertListEqual(_lowerCamelCase,			_lowerCamelCase    )




