





import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


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

       from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor







class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__=3   ,				a__=32   ,				a__=3   ,				a__=10   ,				a__=[10, 20, 30, 40]   ,				a__=[1, 1, 2, 1]   ,				a__=True   ,				a__=True   ,				a__="relu"   ,				a__=3   ,				a__=None   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		parent
             _lowerCamelCase				:  List[str]						=		batch_size
             _lowerCamelCase				:  Dict						=		image_size
             _lowerCamelCase				:  Optional[int]						=		num_channels
             _lowerCamelCase				:  Any						=		embeddings_size
             _lowerCamelCase				:  Union[str, Any]						=		hidden_sizes
             _lowerCamelCase				:  str						=		depths
             _lowerCamelCase				:  str						=		is_training
             _lowerCamelCase				:  Any						=		use_labels
             _lowerCamelCase				:  Optional[int]						=		hidden_act
             _lowerCamelCase				:  List[Any]						=		num_labels
             _lowerCamelCase				:  Optional[Any]						=		scope
             _lowerCamelCase				:  str						=		len(a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

             _lowerCamelCase				:  int						=		self.get_config()

             return config, pixel_values


      def 					__snake_case      (		self):



             """simple docstring"""





             return RegNetConfig(
                 num_channels=self.num_channels   ,				embeddings_size=self.embeddings_size   ,				hidden_sizes=self.hidden_sizes   ,				depths=self.depths   ,				hidden_act=self.hidden_act   ,				num_labels=self.num_labels   ,				image_size=self.image_size   ,				)


      def 					__snake_case      (		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		FlaxRegNetModel(config=a__)
             _lowerCamelCase				:  Any						=		model(a__)

             # Output shape (b, c, h, w)
             self.parent.assertEqual(
                 result.last_hidden_state.shape   ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,				)


      def 					__snake_case      (		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.num_labels
             _lowerCamelCase				:  List[Any]						=		FlaxRegNetForImageClassification(config=a__)
             _lowerCamelCase				:  Tuple						=		model(a__)
             self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels))



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.prepare_config_and_inputs()
             _lowerCamelCase,	_lowerCamelCase				:  Tuple						=		config_and_inputs
             _lowerCamelCase				:  int						=		{'''pixel_values''': pixel_values}
             return config, inputs_dict







@require_flax
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      UpperCAmelCase__							=   False
      UpperCAmelCase__							=   False
      UpperCAmelCase__							=   False


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		FlaxRegNetModelTester(self)
             _lowerCamelCase				:  str						=		ConfigTester(self   ,				config_class=a__   ,				has_text_modality=a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             self.create_and_test_config_common_properties()
             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.create_and_test_config_with_num_labels()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()


      def 					__snake_case      (		self):



             """simple docstring"""





             return


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a__)


      @unittest.skip(reason='''RegNet does not use inputs_embeds''')
      def 					__snake_case      (		self):



             """simple docstring"""





             pass


      @unittest.skip(reason='''RegNet does not support input and output embeddings''')
      def 					__snake_case      (		self):



             """simple docstring"""





             pass


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase,	_lowerCamelCase				:  Union[str, Any]						=		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase				:  Optional[Any]						=		model_class(a__)
                    _lowerCamelCase				:  str						=		inspect.signature(model.__call__)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    _lowerCamelCase				:  Optional[int]						=		[*signature.parameters.keys()]

                    _lowerCamelCase				:  List[Any]						=		['''pixel_values''']
                    self.assertListEqual(arg_names[:1]   ,				a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             def check_hidden_states_output(a__   ,				a__   ,				a__):
                    _lowerCamelCase				:  Optional[int]						=		model_class(a__)

                    _lowerCamelCase				:  Union[str, Any]						=		model(**self._prepare_for_class(a__   ,				a__))

                    _lowerCamelCase				:  List[str]						=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                    _lowerCamelCase				:  Union[str, Any]						=		self.model_tester.num_stages
                    self.assertEqual(len(a__)   ,				expected_num_stages + 1)

             _lowerCamelCase,	_lowerCamelCase				:  Any						=		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase				:  Union[str, Any]						=		True
                    check_hidden_states_output(a__   ,				a__   ,				a__)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    _lowerCamelCase				:  Any						=		True

                    check_hidden_states_output(a__   ,				a__   ,				a__)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase,	_lowerCamelCase				:  int						=		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__):
                           _lowerCamelCase				:  Union[str, Any]						=		self._prepare_for_class(a__   ,				a__)
                           _lowerCamelCase				:  Dict						=		model_class(a__)

                           @jax.jit
                           def model_jitted(a__   ,				**a__):
                                  return model(pixel_values=a__   ,				**a__)

                           with self.subTest('''JIT Enabled'''):
                                  _lowerCamelCase				:  Any						=		model_jitted(**a__).to_tuple()

                           with self.subTest('''JIT Disabled'''):
                                  with jax.disable_jit():
                                         _lowerCamelCase				:  List[Any]						=		model_jitted(**a__).to_tuple()

                           self.assertEqual(len(a__)   ,				len(a__))
                           for jitted_output, output in zip(a__   ,				a__):
                                  self.assertEqual(jitted_output.shape   ,				output.shape)




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  List[Any]						=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
       return image







@require_flax
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''') if is_vision_available() else None



      @slow
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  str						=		FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''')

             _lowerCamelCase				:  Optional[int]						=		self.default_image_processor
             _lowerCamelCase				:  Dict						=		prepare_img()
             _lowerCamelCase				:  Optional[Any]						=		image_processor(images=a__   ,				return_tensors='''np''')

             _lowerCamelCase				:  Union[str, Any]						=		model(**a__)

             # verify the logits
             _lowerCamelCase				:  Optional[Any]						=		(1, 1000)
             self.assertEqual(outputs.logits.shape   ,				a__)

             _lowerCamelCase				:  Dict						=		jnp.array([-0.4180, -1.5051, -3.4836])

             self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,				a__   ,				atol=1e-4))








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

import datasets
from datasets import logging


_lowerCamelCase      							=   '\\n\n'

_lowerCamelCase      							=   '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

_lowerCamelCase      							=   '\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'







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




      """simple docstring"""







      def 					__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 					__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":
                           _lowerCamelCase				:  List[Any]						=		'''cuda'''
             else:
                    _lowerCamelCase				:  Dict						=		'''cuda''' if torch.cuda.is_available() else '''cpu'''

             _lowerCamelCase				:  List[str]						=		AutoModelForCausalLM.from_pretrained(a__)
             _lowerCamelCase				:  Union[str, Any]						=		model.to(a__)

             _lowerCamelCase				:  Tuple						=		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:
                    _lowerCamelCase				:  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"
                    _lowerCamelCase				:  Optional[int]						=		model.config.max_length - 1
             else:
                    _lowerCamelCase				:  Optional[int]						=		model.config.max_length

             _lowerCamelCase				:  Union[str, Any]						=		tokenizer(
                 a__   ,				add_special_tokens=a__   ,				padding=a__   ,				truncation=a__   ,				max_length=a__   ,				return_tensors='''pt'''   ,				return_attention_mask=a__   ,				).to(a__)

             _lowerCamelCase				:  Any						=		encodings['''input_ids''']
             _lowerCamelCase				:  Dict						=		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."

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

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

                    if add_start_token:
                           _lowerCamelCase				:  Tuple						=		torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0)).to(a__)
                           _lowerCamelCase				:  str						=		torch.cat([bos_tokens_tensor, encoded_batch]   ,				dim=1)
                           _lowerCamelCase				:  str						=		torch.cat(
                               [torch.ones(bos_tokens_tensor.size()   ,				dtype=torch.intaa).to(a__), attn_mask]   ,				dim=1)

                    _lowerCamelCase				:  Dict						=		encoded_batch

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

                    _lowerCamelCase				:  List[str]						=		out_logits[..., :-1, :].contiguous()
                    _lowerCamelCase				:  Dict						=		labels[..., 1:].contiguous()
                    _lowerCamelCase				:  List[Any]						=		attn_mask[..., 1:].contiguous()

                    _lowerCamelCase				:  str						=		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__)}

