



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 			_snake_case     (   unittest.TestCase):
				UpperCamelCase__			:   Optional[Any]       						=MODEL_FOR_CAUSAL_LM_MAPPING
				UpperCamelCase__			:   Dict       						=TF_MODEL_FOR_CAUSAL_LM_MAPPING




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

							lowercase__   					=			text_generator(["This is a test", "This is a second test"]  )
							self.assertEqual(
							    __lowercase,   [
							        [
							            {
							                "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@@"
							                )
							            }
							        ],
							    ],   )

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




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

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

							lowercase__   					=			text_generator(["This is a test", "This is a second test"],   do_sample=__lowercase  )
							self.assertEqual(
							    __lowercase,   [
							        [
							            {
							                "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 							A__ (  self   :  Optional[Any],   __lowercase   :  Optional[Any],   __lowercase   :  List[str],   __lowercase   :  List[str]  ):
							lowercase__   					=			TextGenerationPipeline(model=__lowercase,   tokenizer=__lowercase  )
							return text_generator, ["This is a test", "Another test"]




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

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




				def 							A__ (  self   :  Any,   __lowercase   :  int,   __lowercase   :  List[Any]  ):
							lowercase__   					=			text_generator.model
							lowercase__   					=			text_generator.tokenizer

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

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

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

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

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

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

							with self.assertRaises(__lowercase  ):
										lowercase__   					=			text_generator("test",   return_full_text=__lowercase,   return_text=__lowercase  )
							with self.assertRaises(__lowercase  ):
										lowercase__   					=			text_generator("test",   return_full_text=__lowercase,   return_tensors=__lowercase  )
							with self.assertRaises(__lowercase  ):
										lowercase__   					=			text_generator("test",   return_text=__lowercase,   return_tensors=__lowercase  )

							# 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__
							):
										lowercase__   					=			text_generator(""  )
										self.assertEqual(__lowercase,   [{"generated_text": ANY(__lowercase  )}]  )
							else:
										with self.assertRaises((ValueError, AssertionError)  ):
													lowercase__   					=			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.
							lowercase__   					=			["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
							if (
							    tokenizer.model_max_length < 1_0000
							    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  )

										lowercase__   					=			text_generator("This is a test" * 500,   handle_long_generation="hole",   max_new_tokens=20  )
										# Hole strategy cannot work
										with self.assertRaises(__lowercase  ):
													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 							A__ (  self   :  Optional[int]  ):
							import torch

							# Classic `model_kwargs`
							lowercase__   					=			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  )
							lowercase__   					=			pipe("This is a test"  )
							self.assertEqual(
							    __lowercase,   [
							        {
							            "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.)
							lowercase__   					=			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  )
							lowercase__   					=			pipe("This is a test"  )
							self.assertEqual(
							    __lowercase,   [
							        {
							            "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
							lowercase__   					=			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  )
							lowercase__   					=			pipe("This is a test"  )
							self.assertEqual(
							    __lowercase,   [
							        {
							            "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 							A__ (  self   :  Union[str, Any]  ):
							import torch

							lowercase__   					=			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 							A__ (  self   :  Tuple  ):
							import torch

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




				def 							A__ (  self   :  Optional[Any]  ):
							lowercase__   					=			"Hello world"
							lowercase__   					=			pipeline("text-generation",   model="hf-internal-testing/tiny-random-gpt2"  )
							if text_generator.model.framework == "tf":
										lowercase__   					=			logging.get_logger("transformers.generation.tf_utils"  )
							else:
										lowercase__   					=			logging.get_logger("transformers.generation.utils"  )
							lowercase__   					=			"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(__lowercase  ) as cl:
										lowercase__   					=			text_generator(__lowercase,   max_length=10,   max_new_tokens=1  )
							self.assertIn(__lowercase,   cl.out  )

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

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









import fire

from utils import calculate_rouge, save_json



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					**SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()]
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()][: len(SCREAMING_SNAKE_CASE_					)]
			lowercase__   					=			calculate_rouge(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					**SCREAMING_SNAKE_CASE_					)
			if save_path is not None:
						save_json(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					indent=SCREAMING_SNAKE_CASE_					)
			return metrics  # these print nicely


if __name__ == "__main__":
							fire.Fire(calculate_rouge_path)




