





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType

class        __SCREAMING_SNAKE_CASE					:






							def __init__(						self  , __lowerCAmelCase=2  , __lowerCAmelCase=3  , __lowerCAmelCase=64  , __lowerCAmelCase=None  ):
														UpperCamelCase__				     =			np.random.default_rng(__lowerCAmelCase  )
														UpperCamelCase__				     =			length
														UpperCamelCase__				     =			rng.normal(size=(length,)  ).astype(np.floataa  )
														UpperCamelCase__				     =			a * self.x + b + rng.normal(scale=0.1  , size=(length,)  ).astype(np.floataa  )






							def __len__(						self  ):
														return self.length







							def __getitem__(						self  , __lowerCAmelCase  ):
														return {"x": self.x[i], "y": self.y[i]}






class        __SCREAMING_SNAKE_CASE					(     torch.nn.Module       ):






							def __init__(						self  , __lowerCAmelCase=0  , __lowerCAmelCase=0  , __lowerCAmelCase=False  ):
														super().__init__()
														UpperCamelCase__				     =			torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														UpperCamelCase__				     =			torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														UpperCamelCase__				     =			True







							def 				_lowerCamelCase		(						self  , __lowerCAmelCase=None  ):
														if self.first_batch:
																					print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					UpperCamelCase__				     =			False
														return x * self.a[0] + self.b[0]






class        __SCREAMING_SNAKE_CASE					(     torch.nn.Module       ):






							def __init__(						self  , __lowerCAmelCase=0  , __lowerCAmelCase=0  , __lowerCAmelCase=False  ):
														super().__init__()
														UpperCamelCase__				     =			torch.nn.Parameter(torch.tensor(__lowerCAmelCase  ).float()  )
														UpperCamelCase__				     =			torch.nn.Parameter(torch.tensor(__lowerCAmelCase  ).float()  )
														UpperCamelCase__				     =			True







							def 				_lowerCamelCase		(						self  , __lowerCAmelCase=None  ):
														if self.first_batch:
																					print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					UpperCamelCase__				     =			False
														return x * self.a + self.b






def     _UpperCamelCase     (a__   :Dict   ,			a__   :int = 16       ):






							"""simple docstring"""





							from datasets import load_dataset
							from transformers import AutoTokenizer

							UpperCamelCase__				     =			AutoTokenizer.from_pretrained("""bert-base-cased"""       )
							UpperCamelCase__				     =			{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
							UpperCamelCase__				     =			load_dataset("""csv"""   ,			data_files=a__       )
							UpperCamelCase__				     =			datasets["""train"""].unique("""label"""       )

							UpperCamelCase__				     =			{v: i for i, v in enumerate(a__       )}

							def tokenize_function(a__   :Dict       ):
														# max_length=None => use the model max length (it's actually the default)
														UpperCamelCase__				     =			tokenizer(
														    examples["""sentence1"""]   ,			examples["""sentence2"""]   ,			truncation=a__   ,			max_length=a__   ,			padding="""max_length"""       )
														if "label" in examples:
																					UpperCamelCase__				     =			[label_to_id[l] for l in examples["""label"""]]
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							UpperCamelCase__				     =			datasets.map(
							    a__   ,			batched=a__   ,			remove_columns=["""sentence1""", """sentence2""", """label"""]   ,			)

							def collate_fn(a__   :Any       ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(a__   ,			padding="""max_length"""   ,			max_length=128   ,			return_tensors="""pt"""       )
														return tokenizer.pad(a__   ,			padding="""longest"""   ,			return_tensors="""pt"""       )

							# Instantiate dataloaders.
							UpperCamelCase__				     =			DataLoader(tokenized_datasets["""train"""]   ,			shuffle=a__   ,			collate_fn=a__   ,			batch_size=2       )
							UpperCamelCase__				     =			DataLoader(tokenized_datasets["""validation"""]   ,			shuffle=a__   ,			collate_fn=a__   ,			batch_size=1       )

							return train_dataloader, eval_dataloader











import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY

@is_pipeline_test
@require_torch_or_tf
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):
							snake_case     :      Union[str, Any]						     =       MODEL_FOR_CAUSAL_LM_MAPPING
							snake_case     :      List[str]						     =       TF_MODEL_FOR_CAUSAL_LM_MAPPING






							@require_torch
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""text-generation"""  , model="""sshleifer/tiny-ctrl"""  , framework="""pt"""  )
														# Using `do_sample=False` to force deterministic output
														UpperCamelCase__				     =			text_generator("""This is a test"""  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
														                """ oscope. FiliFili@@"""
														            )
														        }
														    ]  , )

														UpperCamelCase__				     =			text_generator(["""This is a test""", """This is a second test"""]  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {
														                """generated_text""": (
														                    """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
														                    """ oscope. FiliFili@@"""
														                )
														            }
														        ],
														        [
														            {
														                """generated_text""": (
														                    """This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
														                    """ oscope. oscope. FiliFili@@"""
														                )
														            }
														        ],
														    ]  , )

														UpperCamelCase__				     =			text_generator("""This is a test"""  , do_sample=__lowerCAmelCase  , num_return_sequences=2  , return_tensors=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														        {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														    ]  , )
														UpperCamelCase__				     =			text_generator.model.config.eos_token_id
														UpperCamelCase__				     =			"""<pad>"""
														UpperCamelCase__				     =			text_generator(
														    ["""This is a test""", """This is a second test"""]  , do_sample=__lowerCAmelCase  , num_return_sequences=2  , batch_size=2  , return_tensors=__lowerCAmelCase  , )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														        ],
														        [
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														        ],
														    ]  , )






							@require_tf
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""text-generation"""  , model="""sshleifer/tiny-ctrl"""  , framework="""tf"""  )

														# Using `do_sample=False` to force deterministic output
														UpperCamelCase__				     =			text_generator("""This is a test"""  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
														                """ please,"""
														            )
														        }
														    ]  , )

														UpperCamelCase__				     =			text_generator(["""This is a test""", """This is a second test"""]  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {
														                """generated_text""": (
														                    """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
														                    """ please,"""
														                )
														            }
														        ],
														        [
														            {
														                """generated_text""": (
														                    """This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
														                    """ Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
														                )
														            }
														        ],
														    ]  , )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			TextGenerationPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														return text_generator, ["This is a test", "Another test"]






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			"""Hello I believe in"""
														UpperCamelCase__				     =			pipeline("""text-generation"""  , model="""hf-internal-testing/tiny-random-gpt2"""  )
														UpperCamelCase__				     =			text_generator(__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}]  , )

														UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , stop_sequence=""" fe"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": """Hello I believe in fe"""}]  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			text_generator.model
														UpperCamelCase__				     =			text_generator.tokenizer

														UpperCamelCase__				     =			text_generator("""This is a test"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""  )  )

														UpperCamelCase__				     =			text_generator("""This is a test"""  , return_full_text=__lowerCAmelCase  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertNotIn("""This is a test"""  , outputs[0]["""generated_text"""]  )

														UpperCamelCase__				     =			pipeline(task="""text-generation"""  , model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  , return_full_text=__lowerCAmelCase  )
														UpperCamelCase__				     =			text_generator("""This is a test"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertNotIn("""This is a test"""  , outputs[0]["""generated_text"""]  )

														UpperCamelCase__				     =			text_generator("""This is a test"""  , return_full_text=__lowerCAmelCase  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""  )  )

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

														if text_generator.tokenizer.pad_token is not None:
																					UpperCamelCase__				     =			text_generator(
																					    ["""This is great !""", """Something else"""]  , num_return_sequences=2  , batch_size=2  , do_sample=__lowerCAmelCase  )
																					self.assertEqual(
																					    __lowerCAmelCase  , [
																					        [{"""generated_text""": ANY(__lowerCAmelCase  )}, {"""generated_text""": ANY(__lowerCAmelCase  )}],
																					        [{"""generated_text""": ANY(__lowerCAmelCase  )}, {"""generated_text""": ANY(__lowerCAmelCase  )}],
																					    ]  , )

														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_full_text=__lowerCAmelCase  , return_text=__lowerCAmelCase  )
														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_full_text=__lowerCAmelCase  , return_tensors=__lowerCAmelCase  )
														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_text=__lowerCAmelCase  , return_tensors=__lowerCAmelCase  )

														# Empty prompt is slighly special
														# it requires BOS token to exist.
														# Special case for Pegasus which will always append EOS so will
														# work even without BOS.
														if (
														    text_generator.tokenizer.bos_token_id is not None
														    or "Pegasus" in tokenizer.__class__.__name__
														    or "Git" in model.__class__.__name__
														):
																					UpperCamelCase__				     =			text_generator(""""""  )
																					self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														else:
																					with self.assertRaises((ValueError, AssertionError)  ):
																												UpperCamelCase__				     =			text_generator(""""""  )

														if text_generator.framework == "tf":
																					# TF generation does not support max_new_tokens, and it's impossible
																					# to control long generation with only max_length without
																					# fancy calculation, dismissing tests for now.
																					return
														# We don't care about infinite range models.
														# They already work.
														# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
														UpperCamelCase__				     =			["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
														if (
														    tokenizer.model_max_length < 10000
														    and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
														):
																					# Handling of large generations
																					with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)  ):
																												text_generator("""This is a test""" * 500  , max_new_tokens=20  )

																					UpperCamelCase__				     =			text_generator("""This is a test""" * 500  , handle_long_generation="""hole"""  , max_new_tokens=20  )
																					# Hole strategy cannot work
																					with self.assertRaises(__lowerCAmelCase  ):
																												text_generator(
																												    """This is a test""" * 500  , handle_long_generation="""hole"""  , max_new_tokens=tokenizer.model_max_length + 10  , )






							@require_torch
							@require_accelerate
							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														import torch

														# Classic `model_kwargs`
														UpperCamelCase__				     =			pipeline(
														    model="""hf-internal-testing/tiny-random-bloom"""  , model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa}  , )
														self.assertEqual(pipe.model.device  , torch.device(0  )  )
														self.assertEqual(pipe.model.lm_head.weight.dtype  , torch.bfloataa  )
														UpperCamelCase__				     =			pipe("""This is a test"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test test test test test test test test test test test test test test test test"""
														                """ test"""
														            )
														        }
														    ]  , )

														# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device_map="""auto"""  , torch_dtype=torch.bfloataa  )
														self.assertEqual(pipe.model.device  , torch.device(0  )  )
														self.assertEqual(pipe.model.lm_head.weight.dtype  , torch.bfloataa  )
														UpperCamelCase__				     =			pipe("""This is a test"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test test test test test test test test test test test test test test test test"""
														                """ test"""
														            )
														        }
														    ]  , )

														# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device_map="""auto"""  )
														self.assertEqual(pipe.model.device  , torch.device(0  )  )
														self.assertEqual(pipe.model.lm_head.weight.dtype  , torch.floataa  )
														UpperCamelCase__				     =			pipe("""This is a test"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test test test test test test test test test test test test test test test test"""
														                """ test"""
														            )
														        }
														    ]  , )






							@require_torch
							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														import torch

														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device=0  , torch_dtype=torch.floataa  )
														pipe("""This is a test"""  )






							@require_torch
							@require_accelerate
							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														import torch

														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device_map="""auto"""  , torch_dtype=torch.floataa  )
														pipe("""This is a test"""  , do_sample=__lowerCAmelCase  , top_p=0.5  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			"""Hello world"""
														UpperCamelCase__				     =			pipeline("""text-generation"""  , model="""hf-internal-testing/tiny-random-gpt2"""  )
														if text_generator.model.framework == "tf":
																					UpperCamelCase__				     =			logging.get_logger("""transformers.generation.tf_utils"""  )
														else:
																					UpperCamelCase__				     =			logging.get_logger("""transformers.generation.utils"""  )
														UpperCamelCase__				     =			"""Both `max_new_tokens`"""  # The beggining of the message to be checked in this test

														# Both are set by the user -> log warning
														with CaptureLogger(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_length=10  , max_new_tokens=1  )
														self.assertIn(__lowerCAmelCase  , cl.out  )

														# The user only sets one -> no warning
														with CaptureLogger(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_new_tokens=1  )
														self.assertNotIn(__lowerCAmelCase  , cl.out  )

														with CaptureLogger(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_length=10  )
														self.assertNotIn(__lowerCAmelCase  , cl.out  )




