




import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard







@slow
@require_flax
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						int						)     ->			Tuple:

       """simple docstring"""






       super().tearDown()
       gc.collect()

 def        a__				(       self      :						Tuple						)     ->			str:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					FlaxStableDiffusionPipeline.from_pretrained(
           'stabilityai/stable-diffusion-2'		,							revision='bf16'		,							dtype=jnp.bfloataa		,							)

       lowerCamelCase_	  =					'A painting of a squirrel eating a burger'
       lowerCamelCase_	  =					jax.device_count()
       lowerCamelCase_	  =					num_samples * [prompt]
       lowerCamelCase_	  =					sd_pipe.prepare_inputs(A_						)

       lowerCamelCase_	  =					replicate(A_						)
       lowerCamelCase_	  =					shard(A_						)

       lowerCamelCase_	  =					jax.random.PRNGKey(0						)
       lowerCamelCase_	  =					jax.random.split(A_		,							jax.device_count()						)

       lowerCamelCase_	  =					sd_pipe(A_		,							A_		,							A_		,							num_inference_steps=25		,							jit=A_						)[0]
       assert images.shape == (jax.device_count(), 1, 768, 768, 3)

       lowerCamelCase_	  =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
       lowerCamelCase_	  =					images[0, 253:256, 253:256, -1]

       lowerCamelCase_	  =					jnp.asarray(jax.device_get(image_slice.flatten()						)						)
       lowerCamelCase_	  =					jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]						)
       print(f"""output_slice: {output_slice}"""						)
       assert jnp.abs(output_slice - expected_slice						).max() < 1E-2





 def        a__				(       self      :						Any						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					'stabilityai/stable-diffusion-2'
       lowerCamelCase_  ,       lowerCamelCase_	  =					FlaxDPMSolverMultistepScheduler.from_pretrained(A_		,							subfolder='scheduler'						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					FlaxStableDiffusionPipeline.from_pretrained(
           A_		,							scheduler=A_		,							revision='bf16'		,							dtype=jnp.bfloataa		,							)
       lowerCamelCase_	  =					scheduler_params

       lowerCamelCase_	  =					'A painting of a squirrel eating a burger'
       lowerCamelCase_	  =					jax.device_count()
       lowerCamelCase_	  =					num_samples * [prompt]
       lowerCamelCase_	  =					sd_pipe.prepare_inputs(A_						)

       lowerCamelCase_	  =					replicate(A_						)
       lowerCamelCase_	  =					shard(A_						)

       lowerCamelCase_	  =					jax.random.PRNGKey(0						)
       lowerCamelCase_	  =					jax.random.split(A_		,							jax.device_count()						)

       lowerCamelCase_	  =					sd_pipe(A_		,							A_		,							A_		,							num_inference_steps=25		,							jit=A_						)[0]
       assert images.shape == (jax.device_count(), 1, 768, 768, 3)

       lowerCamelCase_	  =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
       lowerCamelCase_	  =					images[0, 253:256, 253:256, -1]

       lowerCamelCase_	  =					jnp.asarray(jax.device_get(image_slice.flatten()						)						)
       lowerCamelCase_	  =					jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]						)
       print(f"""output_slice: {output_slice}"""						)
       assert jnp.abs(output_slice - expected_slice						).max() < 1E-2









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						)



