

import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
			from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image






class      lowerCamelCase_      :




	def __init__(     self						:     Any					,      __A						:     Any					,      __A						:     Optional[Any]=3					,      __A						:     List[str]=32					,      __A						:     Tuple=3					,      __A						:     Optional[Any]=10					,      __A						:     Optional[Any]=[8, 16, 32, 64]					,      __A						:     int=[1, 1, 2, 1]					,      __A						:     Any=True					,      __A						:     int=True					,      __A						:     Tuple="relu"					,      __A						:     Tuple=3					,      __A						:     Dict=None					,      __A						:     Optional[int]=["stage2", "stage3", "stage4"]					,      __A						:     Dict=[2, 3, 4]					,      __A						:     int=1					,      ):
		__A :			Any		    =			parent
		__A :			str		    =			batch_size
		__A :			Any		    =			image_size
		__A :			Any		    =			num_channels
		__A :			List[str]		    =			embeddings_size
		__A :			Optional[Any]		    =			hidden_sizes
		__A :			List[Any]		    =			depths
		__A :			Dict		    =			is_training
		__A :			Dict		    =			use_labels
		__A :			Optional[Any]		    =			hidden_act
		__A :			Union[str, Any]		    =			num_labels
		__A :			Optional[int]		    =			scope
		__A :			Union[str, Any]		    =			len(__A							)
		__A :			Dict		    =			out_features
		__A :			str		    =			out_indices
		__A :			int		    =			num_groups




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Any		    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		__A :			int		    =			None
		if self.use_labels:
			__A :			Optional[int]		    =			ids_tensor([self.batch_size]					,      self.num_labels							)

		__A :			List[Any]		    =			self.get_config()

		return config, pixel_values, labels




	def        lowerCAmelCase_       (     self						:     int							):
		return BitConfig(
		    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					,      out_features=self.out_features					,      out_indices=self.out_indices					,      num_groups=self.num_groups					,      )




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     str					,      __A						:     Optional[Any]					,      __A						:     str							):
		__A :			List[str]		    =			BitModel(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			str		    =			model(__A							)
		self.parent.assertEqual(
		    result.last_hidden_state.shape					,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,      )




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     str					,      __A						:     List[Any]					,      __A						:     Any							):
		__A :			Dict		    =			self.num_labels
		__A :			Optional[Any]		    =			BitForImageClassification(__A							)
		model.to(__A							)
		model.eval()
		__A :			Any		    =			model(__A					,      labels=__A							)
		self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Optional[Any]					,      __A						:     Optional[Any]					,      __A						:     List[Any]							):
		__A :			List[Any]		    =			BitBackbone(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			Tuple		    =			model(__A							)

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps							)					,      len(config.out_features							)							)
		self.parent.assertListEqual(list(result.feature_maps[0].shape							)					,      [self.batch_size, self.hidden_sizes[1], 4, 4]							)

		# verify channels
		self.parent.assertEqual(len(model.channels							)					,      len(config.out_features							)							)
		self.parent.assertListEqual(model.channels					,      config.hidden_sizes[1:]							)

		# verify backbone works with out_features=None
		__A :			Union[str, Any]		    =			None
		__A :			List[str]		    =			BitBackbone(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			str		    =			model(__A							)

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps							)					,      1							)
		self.parent.assertListEqual(list(result.feature_maps[0].shape							)					,      [self.batch_size, self.hidden_sizes[-1], 1, 1]							)

		# verify channels
		self.parent.assertEqual(len(model.channels							)					,      1							)
		self.parent.assertListEqual(model.channels					,      [config.hidden_sizes[-1]]							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			int		    =			self.prepare_config_and_inputs()
		__A    ,				__A    ,				__A :			Union[str, Any]		    =			config_and_inputs
		__A :			Optional[int]		    =			{"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_torch
class      lowerCamelCase_      (	_lowercase						,      _lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	_lowercase		:   List[Any]     					=				(
	    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_lowercase		:   int     					=				False
	_lowercase		:   str     					=				False
	_lowercase		:   List[str]     					=				False
	_lowercase		:   List[str]     					=				False
	_lowercase		:   Any     					=				False




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Any		    =			BitModelTester(self							)
		__A :			List[Any]		    =			ConfigTester(self					,      config_class=__A					,      has_text_modality=__A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		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        lowerCAmelCase_       (     self						:     Tuple							):
		return




	@unittest.skip(reason="""Bit does not output attentions"""							)
	def        lowerCAmelCase_       (     self						:     Tuple							):
		pass




	@unittest.skip(reason="""Bit does not use inputs_embeds"""							)
	def        lowerCAmelCase_       (     self						:     int							):
		pass




	@unittest.skip(reason="""Bit does not support input and output embeddings"""							)
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		pass




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A    ,				__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

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

			__A :			int		    =			["""pixel_values"""]
			self.assertListEqual(arg_names[:1]					,      __A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			List[str]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__A							)




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		__A    ,				__A :			int		    =			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__A :			int		    =			model_class(config=__A							)
			for name, module in model.named_modules():
				if isinstance(__A					,      (nn.BatchNormad, nn.GroupNorm)							):
					self.assertTrue(
					    torch.all(module.weight == 1							)					,      msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,      )
					self.assertTrue(
					    torch.all(module.bias == 0							)					,      msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,      )




	def        lowerCAmelCase_       (     self						:     Dict							):
		def check_hidden_states_output(__A						:     Union[str, Any]					,      __A						:     List[Any]					,      __A						:     List[Any]							):
			__A :			Any		    =			model_class(__A							)
			model.to(__A							)
			model.eval()

			with torch.no_grad():
				__A :			Dict		    =			model(**self._prepare_for_class(__A					,      __A							)							)

			__A :			Dict		    =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			__A :			Optional[Any]		    =			self.model_tester.num_stages
			self.assertEqual(len(__A							)					,      expected_num_stages + 1							)

			# Bit's feature maps are of shape (batch_size, num_channels, height, width)
			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]							)					,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,      )

		__A    ,				__A :			Tuple		    =			self.model_tester.prepare_config_and_inputs_for_common()
		__A :			int		    =			["""preactivation""", """bottleneck"""]
		for model_class in self.all_model_classes:
			for layer_type in layers_type:
				__A :			List[Any]		    =			layer_type
				__A :			List[Any]		    =			True
				check_hidden_states_output(__A					,      __A					,      __A							)

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

				check_hidden_states_output(__A					,      __A					,      __A							)




	@unittest.skip(reason="""Bit does not use feedforward chunking"""							)
	def        lowerCAmelCase_       (     self						:     Any							):
		pass




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			List[str]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*__A							)




	@slow
	def        lowerCAmelCase_       (     self						:     List[Any]							):
		for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__A :			List[Any]		    =			BitModel.from_pretrained(__A							)
			self.assertIsNotNone(__A							)






def 							__SCREAMING_SNAKE_CASE     (   )					->				int:
	__A :			Union[str, Any]		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image






@require_torch
@require_vision
class      lowerCamelCase_      (	unittest.TestCase	):




	@cached_property
	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		return (
		    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
		)




	@slow
	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			Optional[Any]		    =			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__A							)

		__A :			Optional[int]		    =			self.default_image_processor
		__A :			str		    =			prepare_img()
		__A :			int		    =			image_processor(images=__A					,      return_tensors="""pt"""							).to(__A							)

		# forward pass
		with torch.no_grad():
			__A :			Union[str, Any]		    =			model(**__A							)

		# verify the logits
		__A :			Optional[int]		    =			torch.Size((1, 1000)							)
		self.assertEqual(outputs.logits.shape					,      __A							)

		__A :			Tuple		    =			torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]							).to(__A							)

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






@require_torch
class      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				(BitBackbone,) if is_torch_available() else ()
	_lowercase		:   Dict     					=				BitConfig

	_lowercase		:   Tuple     					=				False




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Dict		    =			BitModelTester(self							)












"""simple docstring"""




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

import datasets
from datasets import logging


a		:					List[Any]	      =     '''\

'''

a		:					Optional[int]	      =     '''
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
'''

a		:					List[Any]	      =     '''
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		):






							def        __a  (  self )      ->					List[Any]:
								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        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = 16 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__=None )      ->					Tuple:
								if device is not None:
									assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
									if device == "gpu":
										a :						Optional[Any]      =							"cuda"
								else:
									a :						str      =							"cuda" if torch.cuda.is_available() else "cpu"

								a :						str      =							AutoModelForCausalLM.from_pretrained(lowerCAmelCase__ )
								a :						int      =							model.to(lowerCAmelCase__ )

								a :						str      =							AutoTokenizer.from_pretrained(lowerCAmelCase__ )

								# 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:
									a :						str      =							list(tokenizer.special_tokens_map_extended.values() )
									# check that the model already has at least one special token defined
									assert (
									    len(lowerCAmelCase__ ) > 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"
									a :						List[Any]      =							model.config.max_length - 1
								else:
									a :						Union[str, Any]      =							model.config.max_length

								a :						Union[str, Any]      =							tokenizer(
								    lowerCAmelCase__ ,   add_special_tokens=lowerCAmelCase__ ,   padding=lowerCAmelCase__ ,   truncation=lowerCAmelCase__ ,   max_length=lowerCAmelCase__ ,   return_tensors="pt" ,   return_attention_mask=lowerCAmelCase__ ,   ).to(lowerCAmelCase__ )

								a :						str      =							encodings["input_ids"]
								a :						Optional[int]      =							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."

								a :						List[Any]      =							[]
								a :						List[Any]      =							CrossEntropyLoss(reduction="none" )

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

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

									a :						Any      =							encoded_batch

									with torch.no_grad():
										a :						Optional[int]      =							model(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).logits

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

									a :						Dict      =							torch.expa(
									    (loss_fct(shift_logits.transpose(1 ,   2 ) ,   lowerCAmelCase__ ) * shift_attention_mask_batch).sum(1 )
									    / shift_attention_mask_batch.sum(1 ) )

									ppls += perplexity_batch.tolist()

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


