





'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch
@is_pipeline_test
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				__a            =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__a            =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					Optional[Any]					,				__a     :					Dict					,				__a     :					List[str]					):
								_a								= TextaTextGenerationPipeline(model=__a					,				tokenizer=__a					)
								return generator, ["Something to write", "Something else"]




				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[Any]					,				__a     :					Any					):
								_a								= generator("Something there"					)
								self.assertEqual(__a					,				[{"generated_text": ANY(__a					)}]					)
								# These are encoder decoder, they don't just append to incoming string
								self.assertFalse(outputs[0]["generated_text"].startswith("Something there"					)					)

								_a								= generator(["This is great !", "Something else"]					,				num_return_sequences=2					,				do_sample=__a					)
								self.assertEqual(
								    __a					,				[
								        [{"generated_text": ANY(__a					)}, {"generated_text": ANY(__a					)}],
								        [{"generated_text": ANY(__a					)}, {"generated_text": ANY(__a					)}],
								    ]					,				)

								_a								= generator(
								    ["This is great !", "Something else"]					,				num_return_sequences=2					,				batch_size=2					,				do_sample=__a					)
								self.assertEqual(
								    __a					,				[
								        [{"generated_text": ANY(__a					)}, {"generated_text": ANY(__a					)}],
								        [{"generated_text": ANY(__a					)}, {"generated_text": ANY(__a					)}],
								    ]					,				)

								with self.assertRaises(__a					):
												generator(4					)




				@require_torch
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= pipeline("text2text-generation"					,				model="patrickvonplaten/t5-tiny-random"					,				framework="pt"					)
								# do_sample=False necessary for reproducibility
								_a								= generator("Something there"					,				do_sample=__a					)
								self.assertEqual(__a					,				[{"generated_text": ""}]					)

								_a								= 3
								_a								= generator(
								    "Something there"					,				num_return_sequences=__a					,				num_beams=__a					,				)
								_a								= [
								    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
								    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
								    {"generated_text": ""},
								]
								self.assertEqual(__a					,				__a					)

								_a								= generator("This is a test"					,				do_sample=__a					,				num_return_sequences=2					,				return_tensors=__a					)
								self.assertEqual(
								    __a					,				[
								        {"generated_token_ids": ANY(torch.Tensor					)},
								        {"generated_token_ids": ANY(torch.Tensor					)},
								    ]					,				)
								_a								= generator.model.config.eos_token_id
								_a								= "<pad>"
								_a								= generator(
								    ["This is a test", "This is a second test"]					,				do_sample=__a					,				num_return_sequences=2					,				batch_size=2					,				return_tensors=__a					,				)
								self.assertEqual(
								    __a					,				[
								        [
								            {"generated_token_ids": ANY(torch.Tensor					)},
								            {"generated_token_ids": ANY(torch.Tensor					)},
								        ],
								        [
								            {"generated_token_ids": ANY(torch.Tensor					)},
								            {"generated_token_ids": ANY(torch.Tensor					)},
								        ],
								    ]					,				)




				@require_tf
				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= pipeline("text2text-generation"					,				model="patrickvonplaten/t5-tiny-random"					,				framework="tf"					)
								# do_sample=False necessary for reproducibility
								_a								= generator("Something there"					,				do_sample=__a					)
								self.assertEqual(__a					,				[{"generated_text": ""}]					)







"""simple docstring"""





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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'longformer'





      def __init__(       self      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 1      ,						lowercase = 0      ,						lowercase = 2      ,						lowercase = 30_522      ,						lowercase = 768      ,						lowercase = 12      ,						lowercase = 12      ,						lowercase = 3_072      ,						lowercase = "gelu"      ,						lowercase = 0.1      ,						lowercase = 0.1      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 0.02      ,						lowercase = 1e-12      ,						lowercase = False      ,						**lowercase      ,						)		->     Optional[int]:
            super().__init__(pad_token_id=lowercase      ,						**lowercase )

            lowerCAmelCase  						=       attention_window
            lowerCAmelCase  						=       sep_token_id
            lowerCAmelCase  						=       bos_token_id
            lowerCAmelCase  						=       eos_token_id
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       hidden_act
            lowerCAmelCase  						=       intermediate_size
            lowerCAmelCase  						=       hidden_dropout_prob
            lowerCAmelCase  						=       attention_probs_dropout_prob
            lowerCAmelCase  						=       max_position_embeddings
            lowerCAmelCase  						=       type_vocab_size
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       layer_norm_eps
            lowerCAmelCase  						=       onnx_export







class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase = "default"      ,						lowercase = None )		->     Tuple:
            super().__init__(lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       True





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                  lowerCAmelCase  						=       {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  lowerCAmelCase  						=       {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                    ("""global_attention_mask""", dynamic_axis),
                ] )





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            lowerCAmelCase  						=       super().outputs
            if self.task == "default":
                  lowerCAmelCase  						=       {0: """batch"""}
            return outputs





      @property
      def     _snake_case			(       self )		->     float:
            return 1e-4





      @property
      def     _snake_case			(       self )		->     int:
            # needs to be >= 14 to support tril operator
            return max(super().default_onnx_opset      ,						14 )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = -1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       super().generate_dummy_inputs(
                preprocessor=lowercase      ,						batch_size=lowercase      ,						seq_length=lowercase      ,						is_pair=lowercase      ,						framework=lowercase )
            import torch

            # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
            # makes the export fail randomly
            lowerCAmelCase  						=       torch.zeros_like(inputs["""input_ids"""] )
            # make every second token global
            lowerCAmelCase  						=       1

            return inputs






