






'''simple docstring'''




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
		from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


__lowerCamelCase   :					List[str]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Tuple   							=					{
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''bloom'''
							a__   		=	['''past_key_values''']
							a__   		=	{
							    '''num_hidden_layers''': '''n_layer''',
							    '''num_attention_heads''': '''n_head''',
							}

							def __init__(		self			:Union[str, Any]       ,							lowerCAmelCase__			:List[Any]=250_880       ,							lowerCAmelCase__			:List[str]=64       ,							lowerCAmelCase__			:Optional[Any]=2       ,							lowerCAmelCase__			:Union[str, Any]=8       ,							lowerCAmelCase__			:Optional[Any]=1E-5       ,							lowerCAmelCase__			:List[str]=0.0_2       ,							lowerCAmelCase__			:Union[str, Any]=True       ,							lowerCAmelCase__			:Optional[Any]=1       ,							lowerCAmelCase__			:str=2       ,							lowerCAmelCase__			:Optional[int]=False       ,							lowerCAmelCase__			:str=0.0       ,							lowerCAmelCase__			:Tuple=0.0       ,							lowerCAmelCase__			:Optional[Any]=1       ,							lowerCAmelCase__			:Optional[int]=False       ,							**lowerCAmelCase__			:str       ,							)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       vocab_size
										# Backward compatibility with n_embed kwarg
										snake_case_				:					Any   =       kwargs.pop("n_embed"       ,							lowerCAmelCase__				)
										snake_case_				:					List[Any]   =       hidden_size if n_embed is None else n_embed
										snake_case_				:					str   =       n_layer
										snake_case_				:					Optional[Any]   =       n_head
										snake_case_				:					int   =       layer_norm_epsilon
										snake_case_				:					Dict   =       initializer_range
										snake_case_				:					Tuple   =       use_cache
										snake_case_				:					Any   =       pretraining_tp
										snake_case_				:					Any   =       apply_residual_connection_post_layernorm
										snake_case_				:					List[Any]   =       hidden_dropout
										snake_case_				:					Dict   =       attention_dropout

										snake_case_				:					Union[str, Any]   =       bos_token_id
										snake_case_				:					List[str]   =       eos_token_id
										snake_case_				:					Any   =       slow_but_exact

										super().__init__(bos_token_id=lowerCAmelCase__       ,							eos_token_id=lowerCAmelCase__       ,							**lowerCAmelCase__				)







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	version.parse('''1.12'''							)

							def __init__(		self			:Optional[int]       ,							lowerCAmelCase__			:PretrainedConfig       ,							lowerCAmelCase__			:str = "default"       ,							lowerCAmelCase__			:List[PatchingSpec] = None       ,							lowerCAmelCase__			:bool = False       ,							)				->  Dict:


										'''simple docstring'''




										super().__init__(lowerCAmelCase__       ,							task=lowerCAmelCase__       ,							patching_specs=lowerCAmelCase__       ,							use_past=lowerCAmelCase__				)
										if not getattr(self._config       ,							"pad_token_id"       ,							lowerCAmelCase__				):
													# TODO: how to do that better?
													snake_case_				:					List[Any]   =       0

							@property
							def 							_A	(		self			:Optional[int]				)				->  Mapping[str, Mapping[int, str]]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
										if self.use_past:
													# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
													self.fill_with_past_key_values_(lowerCAmelCase__       ,							direction="inputs"       ,							inverted_values_shape=lowerCAmelCase__				)
													snake_case_				:					int   =       {0: "batch", 1: "past_sequence + sequence"}
										else:
													snake_case_				:					int   =       {0: "batch", 1: "sequence"}

										return common_inputs

							@property
							def 							_A	(		self			:str				)				->  int:


										'''simple docstring'''




										return self._config.n_layer

							@property
							def 							_A	(		self			:int				)				->  int:


										'''simple docstring'''




										return self._config.n_head

							@property
							def 							_A	(		self			:Tuple				)				->  float:


										'''simple docstring'''




										return 1E-3

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:"PreTrainedTokenizer"       ,							lowerCAmelCase__			:int = -1       ,							lowerCAmelCase__			:int = -1       ,							lowerCAmelCase__			:bool = False       ,							lowerCAmelCase__			:Optional["TensorType"] = None       ,							)				->  Mapping[str, Any]:


										'''simple docstring'''




										snake_case_				:					int   =       super(lowerCAmelCase__       ,							self				).generate_dummy_inputs(
										    lowerCAmelCase__       ,							batch_size=lowerCAmelCase__       ,							seq_length=lowerCAmelCase__       ,							is_pair=lowerCAmelCase__       ,							framework=lowerCAmelCase__				)

										# We need to order the input in the way they appears in the forward()
										snake_case_				:					Any   =       OrderedDict({"input_ids": common_inputs["input_ids"]}				)

										# Need to add the past_keys
										if self.use_past:
													if not is_torch_available():
																raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
													else:
																import torch

																snake_case_,	snake_case_				:					Optional[Any]   =       common_inputs["input_ids"].shape
																# Not using the same length for past_key_values
																snake_case_				:					Any   =       seqlen + 2
																snake_case_				:					Union[str, Any]   =       self._config.hidden_size // self.num_attention_heads
																snake_case_				:					str   =       (
																    batch * self.num_attention_heads,
																    head_dim,
																    past_key_values_length,
																)
																snake_case_				:					str   =       (
																    batch * self.num_attention_heads,
																    past_key_values_length,
																    head_dim,
																)
																snake_case_				:					Dict   =       [
																    (torch.zeros(lowerCAmelCase__				), torch.zeros(lowerCAmelCase__				)) for _ in range(self.num_layers				)
																]

										snake_case_				:					str   =       common_inputs["attention_mask"]
										if self.use_past:
													snake_case_				:					Any   =       ordered_inputs["attention_mask"].dtype
													snake_case_				:					Union[str, Any]   =       torch.cat(
													    [ordered_inputs["attention_mask"], torch.ones(lowerCAmelCase__       ,							lowerCAmelCase__       ,							dtype=lowerCAmelCase__				)]       ,							dim=1				)

										return ordered_inputs






							@property
							def 							_A	(		self			:Optional[int]				)				->  int:


										'''simple docstring'''




										return 13












'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__lowerCamelCase   :					List[str]   							=					{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}







@is_pipeline_test
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							a__   		=	MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							a__   		=	TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														a__   		=	{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														a__   		=	{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Dict				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       ZeroShotClassificationPipeline(
										    model=lowerCAmelCase__       ,							tokenizer=lowerCAmelCase__       ,							candidate_labels=["polics", "health"]				)
										return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:Any				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels="politics"				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										# No kwarg
										snake_case_				:					List[Any]   =       classifier("Who are you voting for in 2020?"       ,							["politics"]				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										snake_case_				:					Dict   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels=["politics"]				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										snake_case_				:					int   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels="politics, public health"				)
										self.assertEqual(
										    lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}				)
										self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]				)				)       ,							1.0				)

										snake_case_				:					Optional[int]   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health"]				)
										self.assertEqual(
										    lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}				)
										self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]				)				)       ,							1.0				)

										snake_case_				:					str   =       classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels="politics"       ,							hypothesis_template="This text is about {}"				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										# https://github.com/huggingface/transformers/issues/13846
										snake_case_				:					Dict   =       classifier(["I am happy"]       ,							["positive", "negative"]				)
										self.assertEqual(
										    lowerCAmelCase__       ,							[
										        {"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}
										        for i in range(1				)
										    ]       ,							)
										snake_case_				:					Tuple   =       classifier(["I am happy", "I am sad"]       ,							["positive", "negative"]				)
										self.assertEqual(
										    lowerCAmelCase__       ,							[
										        {"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}
										        for i in range(2				)
										    ]       ,							)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(""       ,							candidate_labels="politics"				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(lowerCAmelCase__       ,							candidate_labels="politics"				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier("Who are you voting for in 2020?"       ,							candidate_labels=""				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier("Who are you voting for in 2020?"       ,							candidate_labels=lowerCAmelCase__				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(
													    "Who are you voting for in 2020?"       ,							candidate_labels="politics"       ,							hypothesis_template="Not formatting template"       ,							)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(
													    "Who are you voting for in 2020?"       ,							candidate_labels="politics"       ,							hypothesis_template=lowerCAmelCase__       ,							)

										self.run_entailment_id(lowerCAmelCase__				)

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:Pipeline				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					int   =       zero_shot_classifier.model.config
										snake_case_				:					Optional[int]   =       config.labelaid
										snake_case_				:					Tuple   =       zero_shot_classifier.entailment_id

										snake_case_				:					Optional[Any]   =       {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							-1				)

										snake_case_				:					Tuple   =       {"entailment": 0, "neutral": 1, "contradiction": 2}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							0				)

										snake_case_				:					str   =       {"ENTAIL": 0, "NON-ENTAIL": 1}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							0				)

										snake_case_				:					str   =       {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							2				)

										snake_case_				:					List[str]   =       original_labelaid
										self.assertEqual(lowerCAmelCase__       ,							zero_shot_classifier.entailment_id				)

							@require_torch
							def 							_A	(		self			:Tuple				)				->  Any:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       pipeline(
										    "zero-shot-classification"       ,							model="sshleifer/tiny-distilbert-base-cased-distilled-squad"       ,							framework="pt"       ,							)
										# There was a regression in 4.10 for this
										# Adding a test so we don't make the mistake again.
										# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
										zero_shot_classifier(
										    "Who are you voting for in 2020?" * 100       ,							candidate_labels=["politics", "public health", "science"]				)

							@require_torch
							def 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       pipeline(
										    "zero-shot-classification"       ,							model="sshleifer/tiny-distilbert-base-cased-distilled-squad"       ,							framework="pt"       ,							)
										snake_case_				:					int   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["science", "public health", "politics"],
										        "scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
										    }       ,							)

							@require_tf
							def 							_A	(		self			:Union[str, Any]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					List[str]   =       pipeline(
										    "zero-shot-classification"       ,							model="sshleifer/tiny-distilbert-base-cased-distilled-squad"       ,							framework="tf"       ,							)
										snake_case_				:					Optional[int]   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["science", "public health", "politics"],
										        "scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
										    }       ,							)

							@slow
							@require_torch
							def 							_A	(		self			:Union[str, Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					int   =       pipeline("zero-shot-classification"       ,							model="roberta-large-mnli"       ,							framework="pt"				)
										snake_case_				:					str   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["politics", "public health", "science"],
										        "scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
										    }       ,							)
										snake_case_				:					Optional[int]   =       zero_shot_classifier(
										    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
										    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
										    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
										    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
										    " machine translation tasks show these models to be superior in quality while being more parallelizable"
										    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
										    " English-to-German translation task, improving over the existing best results, including ensembles by"
										    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
										    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
										    " fraction of the training costs of the best models from the literature. We show that the Transformer"
										    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
										    " large and limited training data."       ,							candidate_labels=["machine learning", "statistics", "translation", "vision"]       ,							multi_label=lowerCAmelCase__       ,							)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": (
										            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
										            " networks in an encoder-decoder configuration. The best performing models also connect the"
										            " encoder and decoder through an attention mechanism. We propose a new simple network"
										            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
										            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
										            " superior in quality while being more parallelizable and requiring significantly less time to"
										            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
										            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
										            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
										            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
										            " costs of the best models from the literature. We show that the Transformer generalizes well to"
										            " other tasks by applying it successfully to English constituency parsing both with large and"
										            " limited training data."
										        ),
										        "labels": ["translation", "machine learning", "vision", "statistics"],
										        "scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
										    }       ,							)






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


										'''simple docstring'''




										snake_case_				:					int   =       pipeline("zero-shot-classification"       ,							model="roberta-large-mnli"       ,							framework="tf"				)
										snake_case_				:					Optional[Any]   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["politics", "public health", "science"],
										        "scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
										    }       ,							)
										snake_case_				:					Tuple   =       zero_shot_classifier(
										    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
										    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
										    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
										    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
										    " machine translation tasks show these models to be superior in quality while being more parallelizable"
										    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
										    " English-to-German translation task, improving over the existing best results, including ensembles by"
										    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
										    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
										    " fraction of the training costs of the best models from the literature. We show that the Transformer"
										    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
										    " large and limited training data."       ,							candidate_labels=["machine learning", "statistics", "translation", "vision"]       ,							multi_label=lowerCAmelCase__       ,							)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": (
										            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
										            " networks in an encoder-decoder configuration. The best performing models also connect the"
										            " encoder and decoder through an attention mechanism. We propose a new simple network"
										            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
										            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
										            " superior in quality while being more parallelizable and requiring significantly less time to"
										            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
										            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
										            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
										            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
										            " costs of the best models from the literature. We show that the Transformer generalizes well to"
										            " other tasks by applying it successfully to English constituency parsing both with large and"
										            " limited training data."
										        ),
										        "labels": ["translation", "machine learning", "vision", "statistics"],
										        "scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
										    }       ,							)




