



"""simple docstring"""


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				import jax
				import jax.numpy as jnp

				from transformers.modeling_flax_pytorch_utils import (
				    convert_pytorch_state_dict_to_flax,
				    load_flax_weights_in_pytorch_model,
				)
				from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
				import torch







class       A_			:



						"""simple docstring"""






						def __init__(					self :str   ,  lowercase_ :Tuple   ,  lowercase_ :Tuple=14   ,  lowercase_ :str=7   ,  lowercase_ :int=True   ,  lowercase_ :Optional[Any]=True   ,  lowercase_ :Union[str, Any]=False   ,  lowercase_ :str=True   ,  lowercase_ :int=99   ,  lowercase_ :List[Any]=32   ,  lowercase_ :Any=4   ,  lowercase_ :int=4   ,  lowercase_ :Any=4   ,  lowercase_ :Optional[Any]=37   ,  lowercase_ :Dict="gelu"   ,  lowercase_ :Optional[Any]=0.1   ,  lowercase_ :Tuple=0.1   ,  lowercase_ :int=5_12   ,  lowercase_ :Optional[Any]=0.02   ,  )				->			Optional[int]:
												UpperCAmelCase             = parent
												UpperCAmelCase             = batch_size
												UpperCAmelCase             = seq_length
												UpperCAmelCase             = is_training
												UpperCAmelCase             = use_input_mask
												UpperCAmelCase             = use_token_type_ids
												UpperCAmelCase             = use_labels
												UpperCAmelCase             = vocab_size
												UpperCAmelCase             = hidden_size
												UpperCAmelCase             = rotary_dim
												UpperCAmelCase             = num_hidden_layers
												UpperCAmelCase             = num_attention_heads
												UpperCAmelCase             = intermediate_size
												UpperCAmelCase             = hidden_act
												UpperCAmelCase             = hidden_dropout_prob
												UpperCAmelCase             = attention_probs_dropout_prob
												UpperCAmelCase             = max_position_embeddings
												UpperCAmelCase             = initializer_range
												UpperCAmelCase             = None
												UpperCAmelCase             = vocab_size - 1
												UpperCAmelCase             = vocab_size - 1
												UpperCAmelCase             = vocab_size - 1






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			str:
												UpperCAmelCase             = ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size					)

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

												UpperCAmelCase             = GPTJConfig(
												    vocab_size=self.vocab_size   ,  n_embd=self.hidden_size   ,  n_layer=self.num_hidden_layers   ,  n_head=self.num_attention_heads   ,  n_positions=self.max_position_embeddings   ,  use_cache=a_   ,  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 (config, input_ids, input_mask)






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			List[str]:
												UpperCAmelCase             = self.prepare_config_and_inputs()
												UpperCAmelCase             = config_and_inputs
												UpperCAmelCase             = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :Any   ,  lowercase_ :Optional[Any]   ,  lowercase_ :Optional[Any]   ,  lowercase_ :Optional[Any]					)				->			Dict:
												UpperCAmelCase             = 20
												UpperCAmelCase             = model_class_name(a_					)

												UpperCAmelCase             = model.init_cache(input_ids.shape[0]   ,  a_					)
												UpperCAmelCase             = jnp.ones((input_ids.shape[0], max_decoder_length)   ,  dtype='i4'					)

												UpperCAmelCase             = jnp.broadcast_to(
												    jnp.arange(input_ids.shape[-1] - 1					)[None, :]   ,  (input_ids.shape[0], input_ids.shape[-1] - 1)					)
												UpperCAmelCase             = model(
												    input_ids[:, :-1]   ,  attention_mask=a_   ,  past_key_values=a_   ,  position_ids=a_   ,  )

												UpperCAmelCase             = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,  dtype='i4'					)
												UpperCAmelCase             = model(
												    input_ids[:, -1:]   ,  attention_mask=a_   ,  past_key_values=outputs_cache.past_key_values   ,  position_ids=a_   ,  )

												UpperCAmelCase             = model(a_					)

												UpperCAmelCase             = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
												self.parent.assertTrue(diff < 1E-3   ,  msg=f"""Max diff is {diff}"""					)






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :Any   ,  lowercase_ :Optional[Any]   ,  lowercase_ :Optional[Any]   ,  lowercase_ :Union[str, Any]					)				->			Tuple:
												UpperCAmelCase             = 20
												UpperCAmelCase             = model_class_name(a_					)

												UpperCAmelCase             = jnp.concatenate(
												    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)]   ,  axis=-1   ,  )

												UpperCAmelCase             = model.init_cache(input_ids.shape[0]   ,  a_					)
												UpperCAmelCase             = jnp.broadcast_to(
												    jnp.arange(input_ids.shape[-1] - 1					)[None, :]   ,  (input_ids.shape[0], input_ids.shape[-1] - 1)					)

												UpperCAmelCase             = model(
												    input_ids[:, :-1]   ,  attention_mask=a_   ,  past_key_values=a_   ,  position_ids=a_   ,  )
												UpperCAmelCase             = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,  dtype='i4'					)
												UpperCAmelCase             = model(
												    input_ids[:, -1:]   ,  past_key_values=outputs_cache.past_key_values   ,  attention_mask=a_   ,  position_ids=a_   ,  )

												UpperCAmelCase             = model(a_   ,  attention_mask=a_					)

												UpperCAmelCase             = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
												self.parent.assertTrue(diff < 1E-3   ,  msg=f"""Max diff is {diff}"""					)







@require_flax
class       A_			(     __snake_case	,   __snake_case	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
						__UpperCamelCase						=							(FlaxGPTJForCausalLM,) if is_flax_available() else ()






						def 	UpperCAmelCase__     (					self :int					)				->			str:
												UpperCAmelCase             = FlaxGPTJModelTester(self					)






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

												for model_class_name in self.all_model_classes:
																		UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward(a_   ,  a_   ,  a_   ,  a_					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			Optional[Any]:

												for model_class_name in self.all_model_classes:
																		UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward_with_attn_mask(
																		    a_   ,  a_   ,  a_   ,  a_					)






						@tooslow
						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Tuple:
												UpperCAmelCase             = GPTaTokenizer.from_pretrained('gpt2'   ,  pad_token='<|endoftext|>'   ,  padding_side='left'					)
												UpperCAmelCase             = tokenizer(['Hello this is a long string', 'Hey']   ,  return_tensors='np'   ,  padding=a_   ,  truncation=a_					)

												UpperCAmelCase             = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'					)
												UpperCAmelCase             = False
												UpperCAmelCase             = model.config.eos_token_id

												UpperCAmelCase             = jax.jit(model.generate					)

												UpperCAmelCase             = jit_generate(
												    inputs['input_ids']   ,  attention_mask=inputs['attention_mask']   ,  pad_token_id=tokenizer.pad_token_id					).sequences

												UpperCAmelCase             = tokenizer.batch_decode(a_   ,  skip_special_tokens=a_					)

												UpperCAmelCase             = [
												    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
												    '''Hey, I\'m a little late to the party. I\'m going to''',
												]

												self.assertListEqual(a_   ,  a_					)






						@is_pt_flax_cross_test
						def 	UpperCAmelCase__     (					self :Any					)				->			Optional[Any]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__					):
																								# prepare inputs
																								UpperCAmelCase             = self._prepare_for_class(a_   ,  a_					)
																								UpperCAmelCase             = {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																								# load corresponding PyTorch class
																								UpperCAmelCase             = model_class.__name__[4:]  # Skip the "Flax" at the beginning
																								UpperCAmelCase             = getattr(a_   ,  a_					)

																								UpperCAmelCase             = pt_inputs['''input_ids'''].shape
																								UpperCAmelCase             = np.random.randint(0   ,  seq_length - 1   ,  size=(batch_size,)					)
																								for batch_idx, start_index in enumerate(a_					):
																														UpperCAmelCase             = 0
																														UpperCAmelCase             = 1
																														UpperCAmelCase             = 0
																														UpperCAmelCase             = 1
																								UpperCAmelCase             = pt_model_class(a_					).eval()
																								UpperCAmelCase             = model_class(a_   ,  dtype=jnp.floataa					)

																								UpperCAmelCase             = convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,  a_					)
																								UpperCAmelCase             = fx_state

																								with torch.no_grad():
																														UpperCAmelCase             = pt_model(**a_					).to_tuple()

																								UpperCAmelCase             = fx_model(**a_					).to_tuple()
																								self.assertEqual(len(a_					)   ,  len(a_					)   ,  'Output lengths differ between Flax and PyTorch'					)
																								for fx_output, pt_output in zip(a_   ,  a_					):
																														self.assert_almost_equals(fx_output[:, -1]   ,  pt_output[:, -1].numpy()   ,  4E-2					)

																								with tempfile.TemporaryDirectory() as tmpdirname:
																														pt_model.save_pretrained(a_					)
																														UpperCAmelCase             = model_class.from_pretrained(a_   ,  from_pt=a_					)

																								UpperCAmelCase             = fx_model_loaded(**a_					).to_tuple()
																								self.assertEqual(
																								    len(a_					)   ,  len(a_					)   ,  'Output lengths differ between Flax and PyTorch'					)
																								for fx_output_loaded, pt_output in zip(a_   ,  a_					):
																														self.assert_almost_equals(fx_output_loaded[:, -1]   ,  pt_output[:, -1].numpy()   ,  4E-2					)






						@is_pt_flax_cross_test
						def 	UpperCAmelCase__     (					self :Any					)				->			int:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__					):
																								# prepare inputs
																								UpperCAmelCase             = self._prepare_for_class(a_   ,  a_					)
																								UpperCAmelCase             = {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																								# load corresponding PyTorch class
																								UpperCAmelCase             = model_class.__name__[4:]  # Skip the "Flax" at the beginning
																								UpperCAmelCase             = getattr(a_   ,  a_					)

																								UpperCAmelCase             = pt_model_class(a_					).eval()
																								UpperCAmelCase             = model_class(a_   ,  dtype=jnp.floataa					)

																								UpperCAmelCase             = load_flax_weights_in_pytorch_model(a_   ,  fx_model.params					)
																								UpperCAmelCase             = pt_inputs['''input_ids'''].shape
																								UpperCAmelCase             = np.random.randint(0   ,  seq_length - 1   ,  size=(batch_size,)					)
																								for batch_idx, start_index in enumerate(a_					):
																														UpperCAmelCase             = 0
																														UpperCAmelCase             = 1
																														UpperCAmelCase             = 0
																														UpperCAmelCase             = 1

																								# make sure weights are tied in PyTorch
																								pt_model.tie_weights()

																								with torch.no_grad():
																														UpperCAmelCase             = pt_model(**a_					).to_tuple()

																								UpperCAmelCase             = fx_model(**a_					).to_tuple()
																								self.assertEqual(len(a_					)   ,  len(a_					)   ,  'Output lengths differ between Flax and PyTorch'					)
																								for fx_output, pt_output in zip(a_   ,  a_					):
																														self.assert_almost_equals(fx_output[:, -1]   ,  pt_output[:, -1].numpy()   ,  4E-2					)

																								with tempfile.TemporaryDirectory() as tmpdirname:
																														fx_model.save_pretrained(a_					)
																														UpperCAmelCase             = pt_model_class.from_pretrained(a_   ,  from_flax=a_					)

																								with torch.no_grad():
																														UpperCAmelCase             = pt_model_loaded(**a_					).to_tuple()

																								self.assertEqual(
																								    len(a_					)   ,  len(a_					)   ,  'Output lengths differ between Flax and PyTorch'					)
																								for fx_output, pt_output in zip(a_   ,  a_					):
																														self.assert_almost_equals(fx_output[:, -1]   ,  pt_output[:, -1].numpy()   ,  4E-2					)






						@tooslow
						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Any:

												for model_class_name in self.all_model_classes:
																		UpperCAmelCase             = model_class_name.from_pretrained('EleutherAI/gpt-j-6B'					)
																		UpperCAmelCase             = model(np.ones((1, 1)					)					)
																		self.assertIsNotNone(a_					)









"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


SCREAMING_SNAKE_CASE :    Union[str, Any]									=				"""\

"""

SCREAMING_SNAKE_CASE :    Any									=				"""
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
"""

SCREAMING_SNAKE_CASE :    Dict									=				"""
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to 'cuda' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        78.22
        >>> print(round(results[\"perplexities\"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = datasets.load_dataset(\"wikitext\",
        ...                                     \"wikitext-2-raw-v1\",
        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!='']
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        60.35
        >>> print(round(results[\"perplexities\"][0], 2))
        81.12
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION				)
class 			_UpperCAmelCase (							datasets.Metric				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return datasets.MetricInfo(
			    description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
			        {
			            '''input_texts''': datasets.Value('''string'''					),
			        }					)				,       reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = 16				,       a_ = True				,       a_=None					):





			'''simple docstring'''

			if device is not None:
				assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
				if device == "gpu":
					__snake_case	:						Optional[Any]						=				'''cuda'''
			else:
				__snake_case	:						Tuple						=				'''cuda''' if torch.cuda.is_available() else '''cpu'''

			__snake_case	:						int						=				AutoModelForCausalLM.from_pretrained(a_					)
			__snake_case	:						Optional[int]						=				model.to(a_					)

			__snake_case	:						Optional[int]						=				AutoTokenizer.from_pretrained(a_					)

			# if batch_size > 1 (which generally leads to padding being required), and
			# if there is not an already assigned pad_token, assign an existing
			# special token to also be the padding token
			if tokenizer.pad_token is None and batch_size > 1:
				__snake_case	:						List[Any]						=				list(tokenizer.special_tokens_map_extended.values()					)
				# check that the model already has at least one special token defined
				assert (
				    len(a_					) > 0
				), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
				# assign one of the special tokens to also be the pad token
				tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}					)

			if add_start_token:
				# leave room for <BOS> token to be added:
				assert (
				    tokenizer.bos_token is not None
				), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
				__snake_case	:						List[Any]						=				model.config.max_length - 1
			else:
				__snake_case	:						Dict						=				model.config.max_length

			__snake_case	:						Tuple						=				tokenizer(
			    a_				,       add_special_tokens=a_				,       padding=a_				,       truncation=a_				,       max_length=a_				,       return_tensors='''pt'''				,       return_attention_mask=a_				,       ).to(a_					)

			__snake_case	:						List[Any]						=				encodings['''input_ids''']
			__snake_case	:						str						=				encodings['''attention_mask''']

			# check that each input is long enough:
			if add_start_token:
				assert torch.all(torch.ge(attn_masks.sum(1					)				,       1					)					), "Each input text must be at least one token long."
			else:
				assert torch.all(
				    torch.ge(attn_masks.sum(1					)				,       2					)					), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

			__snake_case	:						Union[str, Any]						=				[]
			__snake_case	:						str						=				CrossEntropyLoss(reduction='''none'''					)

			for start_index in logging.tqdm(range(0				,       len(a_					)				,       a_					)					):
				__snake_case	:						Optional[int]						=				min(start_index + batch_size				,       len(a_					)					)
				__snake_case	:						int						=				encoded_texts[start_index:end_index]
				__snake_case	:						Optional[int]						=				attn_masks[start_index:end_index]

				if add_start_token:
					__snake_case	:						List[Any]						=				torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0					)					).to(a_					)
					__snake_case	:						Union[str, Any]						=				torch.cat([bos_tokens_tensor, encoded_batch]				,       dim=1					)
					__snake_case	:						int						=				torch.cat(
					    [torch.ones(bos_tokens_tensor.size()				,       dtype=torch.intaa					).to(a_					), attn_mask]				,       dim=1					)

				__snake_case	:						List[Any]						=				encoded_batch

				with torch.no_grad():
					__snake_case	:						List[str]						=				model(a_				,       attention_mask=a_					).logits

				__snake_case	:						List[str]						=				out_logits[..., :-1, :].contiguous()
				__snake_case	:						int						=				labels[..., 1:].contiguous()
				__snake_case	:						int						=				attn_mask[..., 1:].contiguous()

				__snake_case	:						Tuple						=				torch.expa(
				    (loss_fct(shift_logits.transpose(1				,       2					)				,       a_					) * shift_attention_mask_batch).sum(1					)
				    / shift_attention_mask_batch.sum(1					)					)

				ppls += perplexity_batch.tolist()

			return {"perplexities": ppls, "mean_perplexity": np.mean(a_					)}





