




from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase       :	int									=     {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}


if TYPE_CHECKING:
 from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
 import sys

 lowerCamelCase       :	int									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









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 						A(		unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		MODEL_FOR_CAUSAL_LM_MAPPING
 UpperCamelCase		     =		TF_MODEL_FOR_CAUSAL_LM_MAPPING

 @require_torch
 def        a__				(       self      :						Any						)     ->			Union[str, Any]:

       """simple docstring"""






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

       lowerCamelCase_	  =					text_generator(['This is a test', 'This is a second test']						)
       self.assertEqual(
           A_		,							[
               [
                   {
                       '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@@'
                       )
                   }
               ],
           ]		,							)

       lowerCamelCase_	  =					text_generator('This is a test'		,							do_sample=A_		,							num_return_sequences=2		,							return_tensors=A_						)
       self.assertEqual(
           A_		,							[
               {'generated_token_ids': ANY(A_						)},
               {'generated_token_ids': ANY(A_						)},
           ]		,							)
       lowerCamelCase_	  =					text_generator.model.config.eos_token_id
       lowerCamelCase_	  =					'<pad>'
       lowerCamelCase_	  =					text_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(A_						)},
                   {'generated_token_ids': ANY(A_						)},
               ],
               [
                   {'generated_token_ids': ANY(A_						)},
                   {'generated_token_ids': ANY(A_						)},
               ],
           ]		,							)

 @require_tf
 def        a__				(       self      :						Optional[int]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					pipeline(task='text-generation'		,							model='sshleifer/tiny-ctrl'		,							framework='tf'						)

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

       lowerCamelCase_	  =					text_generator(['This is a test', 'This is a second test']		,							do_sample=A_						)
       self.assertEqual(
           A_		,							[
               [
                   {
                       '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[int]		,							A_      :						Dict		,							A_      :						int		,							A_      :						List[str]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					TextGenerationPipeline(model=A_		,							tokenizer=A_						)
       return text_generator, ["This is a test", "Another test"]

 def        a__				(       self      :						Dict						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					'Hello I believe in'
       lowerCamelCase_	  =					pipeline('text-generation'		,							model='hf-internal-testing/tiny-random-gpt2'						)
       lowerCamelCase_	  =					text_generator(A_						)
       self.assertEqual(
           A_		,							[{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}]		,							)

       lowerCamelCase_	  =					text_generator(A_		,							stop_sequence=' fe'						)
       self.assertEqual(A_		,							[{'generated_text': 'Hello I believe in fe'}]						)

 def        a__				(       self      :						Any		,							A_      :						Optional[Any]		,							A_      :						Union[str, Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					text_generator.model
       lowerCamelCase_	  =					text_generator.tokenizer

       lowerCamelCase_	  =					text_generator('This is a test'						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'						)						)

       lowerCamelCase_	  =					text_generator('This is a test'		,							return_full_text=A_						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertNotIn('This is a test'		,							outputs[0]['generated_text']						)

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

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

       lowerCamelCase_	  =					text_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_						)}],
           ]		,							)

       if text_generator.tokenizer.pad_token is not None:
             lowerCamelCase_	  =					text_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_						):
             lowerCamelCase_	  =					text_generator('test'		,							return_full_text=A_		,							return_text=A_						)
       with self.assertRaises(A_						):
             lowerCamelCase_	  =					text_generator('test'		,							return_full_text=A_		,							return_tensors=A_						)
       with self.assertRaises(A_						):
             lowerCamelCase_	  =					text_generator('test'		,							return_text=A_		,							return_tensors=A_						)

       # 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__
       ):
             lowerCamelCase_	  =					text_generator(''						)
             self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       else:
             with self.assertRaises((ValueError, AssertionError)						):
                   lowerCamelCase_	  =					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.
       lowerCamelCase_	  =					['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						)

             lowerCamelCase_	  =					text_generator('This is a test' * 500		,							handle_long_generation='hole'		,							max_new_tokens=20						)
             # Hole strategy cannot work
             with self.assertRaises(A_						):
                   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      :						Union[str, Any]						)     ->			Any:

       """simple docstring"""






       import torch

       # Classic `model_kwargs`
       lowerCamelCase_	  =					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						)
       lowerCamelCase_	  =					pipe('This is a test'						)
       self.assertEqual(
           A_		,							[
               {
                   '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.)
       lowerCamelCase_	  =					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						)
       lowerCamelCase_	  =					pipe('This is a test'						)
       self.assertEqual(
           A_		,							[
               {
                   '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
       lowerCamelCase_	  =					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						)
       lowerCamelCase_	  =					pipe('This is a test'						)
       self.assertEqual(
           A_		,							[
               {
                   '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      :						int						)     ->			str:

       """simple docstring"""






       import torch

       lowerCamelCase_	  =					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      :						List[Any]						)     ->			Dict:

       """simple docstring"""






       import torch

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





 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					'Hello world'
       lowerCamelCase_	  =					pipeline('text-generation'		,							model='hf-internal-testing/tiny-random-gpt2'						)
       if text_generator.model.framework == "tf":
             lowerCamelCase_	  =					logging.get_logger('transformers.generation.tf_utils'						)
       else:
             lowerCamelCase_	  =					logging.get_logger('transformers.generation.utils'						)
       lowerCamelCase_	  =					'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(A_						) as cl:
             lowerCamelCase_	  =					text_generator(A_		,							max_length=10		,							max_new_tokens=1						)
       self.assertIn(A_		,							cl.out						)

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

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



