


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   lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    A_  :     List[Any]			= MODEL_FOR_CAUSAL_LM_MAPPING
    A_  :     Optional[int]			= TF_MODEL_FOR_CAUSAL_LM_MAPPING





    @require_torch
    def 					_lowerCamelCase (	self				:   List[str]	):





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

       UpperCAmelCase_							:      Any						=   text_generator(['''This is a test''', '''This is a second test''']	)
       self.assertEqual(
           __snake_case       ,   [
               [
                   {
                       '''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_							:      Optional[Any]						=   text_generator('''This is a test'''       ,   do_sample=__snake_case       ,   num_return_sequences=2       ,   return_tensors=__snake_case	)
       self.assertEqual(
           __snake_case       ,   [
               {'''generated_token_ids''': ANY(__snake_case	)},
               {'''generated_token_ids''': ANY(__snake_case	)},
           ]       ,   )
       UpperCAmelCase_							:      Any						=   text_generator.model.config.eos_token_id
       UpperCAmelCase_							:      Union[str, Any]						=   '''<pad>'''
       UpperCAmelCase_							:      Dict						=   text_generator(
           ['''This is a test''', '''This is a second test''']       ,   do_sample=__snake_case       ,   num_return_sequences=2       ,   batch_size=2       ,   return_tensors=__snake_case       ,   )
       self.assertEqual(
           __snake_case       ,   [
               [
                   {'''generated_token_ids''': ANY(__snake_case	)},
                   {'''generated_token_ids''': ANY(__snake_case	)},
               ],
               [
                   {'''generated_token_ids''': ANY(__snake_case	)},
                   {'''generated_token_ids''': ANY(__snake_case	)},
               ],
           ]       ,   )





    @require_tf
    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   pipeline(task='''text-generation'''       ,   model='''sshleifer/tiny-ctrl'''       ,   framework='''tf'''	)

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

       UpperCAmelCase_							:      List[str]						=   text_generator(['''This is a test''', '''This is a second test''']       ,   do_sample=__snake_case	)
       self.assertEqual(
           __snake_case       ,   [
               [
                   {
                       '''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				:   Tuple       ,   __snake_case				:   List[Any]       ,   __snake_case				:   Tuple       ,   __snake_case				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   TextGenerationPipeline(model=__snake_case       ,   tokenizer=__snake_case	)
       return text_generator, ["This is a test", "Another test"]





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   '''Hello I believe in'''
       UpperCAmelCase_							:      Union[str, Any]						=   pipeline('''text-generation'''       ,   model='''hf-internal-testing/tiny-random-gpt2'''	)
       UpperCAmelCase_							:      str						=   text_generator(__snake_case	)
       self.assertEqual(
           __snake_case       ,   [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]       ,   )

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





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   text_generator.model
       UpperCAmelCase_							:      int						=   text_generator.tokenizer

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

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

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

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

       UpperCAmelCase_							:      Dict						=   text_generator(['''This is great !''', '''Something else''']       ,   num_return_sequences=2       ,   do_sample=__snake_case	)
       self.assertEqual(
           __snake_case       ,   [
               [{'''generated_text''': ANY(__snake_case	)}, {'''generated_text''': ANY(__snake_case	)}],
               [{'''generated_text''': ANY(__snake_case	)}, {'''generated_text''': ANY(__snake_case	)}],
           ]       ,   )

       if text_generator.tokenizer.pad_token is not None:
          UpperCAmelCase_							:      Optional[Any]						=   text_generator(
              ['''This is great !''', '''Something else''']       ,   num_return_sequences=2       ,   batch_size=2       ,   do_sample=__snake_case	)
          self.assertEqual(
              __snake_case       ,   [
                  [{'''generated_text''': ANY(__snake_case	)}, {'''generated_text''': ANY(__snake_case	)}],
                  [{'''generated_text''': ANY(__snake_case	)}, {'''generated_text''': ANY(__snake_case	)}],
              ]       ,   )

       with self.assertRaises(__snake_case	):
          UpperCAmelCase_							:      Tuple						=   text_generator('''test'''       ,   return_full_text=__snake_case       ,   return_text=__snake_case	)
       with self.assertRaises(__snake_case	):
          UpperCAmelCase_							:      Tuple						=   text_generator('''test'''       ,   return_full_text=__snake_case       ,   return_tensors=__snake_case	)
       with self.assertRaises(__snake_case	):
          UpperCAmelCase_							:      Tuple						=   text_generator('''test'''       ,   return_text=__snake_case       ,   return_tensors=__snake_case	)

       # 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_							:      Any						=   text_generator(''''''	)
          self.assertEqual(__snake_case       ,   [{'''generated_text''': ANY(__snake_case	)}]	)
       else:
          with self.assertRaises((ValueError, AssertionError)	):
             UpperCAmelCase_							:      str						=   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_							:      Dict						=   ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
       if (
           tokenizer.model_max_length < 10_000
           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_							:      Union[str, Any]						=   text_generator('''This is a test''' * 500       ,   handle_long_generation='''hole'''       ,   max_new_tokens=20	)
          # Hole strategy cannot work
          with self.assertRaises(__snake_case	):
             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				:   Union[str, Any]	):





       '''simple docstring'''
       import torch

       # Classic `model_kwargs`
       UpperCAmelCase_							:      int						=   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_							:      Union[str, Any]						=   pipe('''This is a test'''	)
       self.assertEqual(
           __snake_case       ,   [
               {
                   '''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_							:      Optional[Any]						=   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_							:      Optional[Any]						=   pipe('''This is a test'''	)
       self.assertEqual(
           __snake_case       ,   [
               {
                   '''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_							:      int						=   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_							:      Any						=   pipe('''This is a test'''	)
       self.assertEqual(
           __snake_case       ,   [
               {
                   '''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				:   Optional[int]	):





       '''simple docstring'''
       import torch

       UpperCAmelCase_							:      int						=   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				:   Dict	):





       '''simple docstring'''
       import torch

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





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   '''Hello world'''
       UpperCAmelCase_							:      List[str]						=   pipeline('''text-generation'''       ,   model='''hf-internal-testing/tiny-random-gpt2'''	)
       if text_generator.model.framework == "tf":
          UpperCAmelCase_							:      List[str]						=   logging.get_logger('''transformers.generation.tf_utils'''	)
       else:
          UpperCAmelCase_							:      Union[str, Any]						=   logging.get_logger('''transformers.generation.utils'''	)
       UpperCAmelCase_							:      List[Any]						=   '''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(__snake_case	) as cl:
          UpperCAmelCase_							:      Optional[Any]						=   text_generator(__snake_case       ,   max_length=10       ,   max_new_tokens=1	)
       self.assertIn(__snake_case       ,   cl.out	)

       # The user only sets one -> no warning
       with CaptureLogger(__snake_case	) as cl:
          UpperCAmelCase_							:      List[Any]						=   text_generator(__snake_case       ,   max_new_tokens=1	)
       self.assertNotIn(__snake_case       ,   cl.out	)

       with CaptureLogger(__snake_case	) as cl:
          UpperCAmelCase_							:      List[Any]						=   text_generator(__snake_case       ,   max_length=10	)
       self.assertNotIn(__snake_case       ,   cl.out	)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase				:  str      					=							{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase				:  Any      					=							[
           'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
           'XCLIPModel',
           'XCLIPPreTrainedModel',
           'XCLIPTextModel',
           'XCLIPVisionModel',
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       __UpperCamelCase				:  List[str]      					=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)