




import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


lowerCamelCase       :	List[Any]									=     {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 131_072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
}

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: str  ):


      '''simple docstring'''



      return torch.atana(lowercase			,							lowercase  ) / math.pi * 2

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Any  ):


      '''simple docstring'''



      lowerCamelCase_	  =					torch.sin(t * math.pi / 2  ) ** 2
      lowerCamelCase_	  =					(1 - sigma**2) ** 0.5
      return alpha_sigma_to_t(lowercase			,							lowercase  )







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 pass







class 						A(		nn.Module     ):



 '''simple docstring'''


 def __init__(       self      :						Optional[int]		,							A_      :						int						)     ->			List[str]:

       """simple docstring"""






       super().__init__()

       lowerCamelCase_	  =					DiffusionAttnUnetaD(A_		,							n_attn_layers=4						)
       lowerCamelCase_	  =					deepcopy(self.diffusion						)
       lowerCamelCase_	  =					torch.quasirandom.SobolEngine(1		,							scramble=A_						)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					MODELS_MAP[model_name]['url']
      os.system(f"""wget {url} ./"""  )

      return f"""./{model_name}.ckpt"""


lowerCamelCase       :	str									=     {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
lowerCamelCase       :	str									=     {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
lowerCamelCase       :	List[str]									=     {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
lowerCamelCase       :	Union[str, Any]									=     {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

lowerCamelCase       :	str									=     {
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

lowerCamelCase       :	Tuple									=     {
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      if name.startswith('skip'  ):
            return name.replace('skip'			,							RES_CONV_MAP['skip']  )

      # name has to be of format main.{digit}
      if not name.startswith('main.'  ):
            raise ValueError(f"""ResConvBlock error with {name}"""  )

      return name.replace(name[:6]			,							RES_CONV_MAP[name[:6]]  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''simple docstring'''



      for key, value in ATTN_MAP.items():
            if name.startswith(lowercase  ) and not isinstance(lowercase			,							lowercase  ):
                  return name.replace(lowercase			,							lowercase  )
            elif name.startswith(lowercase  ):
                  return [name.replace(lowercase			,							lowercase  ) for v in value]
      raise ValueError(f"""Attn error with {name}"""  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Any			,							lowercase			: Union[str, Any]=13  ):


      '''simple docstring'''



      lowerCamelCase_	  =					input_string

      if string.split('.'  )[0] == "timestep_embed":
            return string.replace('timestep_embed'			,							'time_proj'  )

      lowerCamelCase_	  =					0
      if string.startswith('net.3.'  ):
            depth += 1
            lowerCamelCase_	  =					string[6:]
      elif string.startswith('net.'  ):
            lowerCamelCase_	  =					string[4:]

      while string.startswith('main.7.'  ):
            depth += 1
            lowerCamelCase_	  =					string[7:]

      if string.startswith('main.'  ):
            lowerCamelCase_	  =					string[5:]

      # mid block
      if string[:2].isdigit():
            lowerCamelCase_	  =					string[:2]
            lowerCamelCase_	  =					string[2:]
      else:
            lowerCamelCase_	  =					string[0]
            lowerCamelCase_	  =					string[1:]

      if depth == max_depth:
            lowerCamelCase_	  =					MID_NUM_TO_LAYER[layer_num]
            lowerCamelCase_	  =					'mid_block'
      elif depth > 0 and int(lowercase  ) < 7:
            lowerCamelCase_	  =					DOWN_NUM_TO_LAYER[layer_num]
            lowerCamelCase_	  =					f"""down_blocks.{depth}"""
      elif depth > 0 and int(lowercase  ) > 7:
            lowerCamelCase_	  =					UP_NUM_TO_LAYER[layer_num]
            lowerCamelCase_	  =					f"""up_blocks.{max_depth - depth - 1}"""
      elif depth == 0:
            lowerCamelCase_	  =					DEPTH_0_TO_LAYER[layer_num]
            lowerCamelCase_	  =					f"""up_blocks.{max_depth - 1}""" if int(lowercase  ) > 3 else 'down_blocks.0'

      if not string_left.startswith('.'  ):
            raise ValueError(f"""Naming error with {input_string} and string_left: {string_left}."""  )

      lowerCamelCase_	  =					string_left[1:]

      if "resnets" in new_layer:
            lowerCamelCase_	  =					convert_resconv_naming(lowercase  )
      elif "attentions" in new_layer:
            lowerCamelCase_	  =					convert_attn_naming(lowercase  )
            lowerCamelCase_	  =					new_string_left

      if not isinstance(lowercase			,							lowercase  ):
            lowerCamelCase_	  =					prefix + '.' + new_layer + '.' + string_left
      else:
            lowerCamelCase_	  =					[prefix + '.' + new_layer + '.' + s for s in string_left]
      return new_string

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{}
      for k, v in state_dict.items():
            if k.endswith('kernel'  ):
                  # up- and downsample layers, don't have trainable weights
                  continue

            lowerCamelCase_	  =					rename(lowercase  )

            # check if we need to transform from Conv => Linear for attention
            if isinstance(lowercase			,							lowercase  ):
                  lowerCamelCase_	  =					transform_conv_attns(lowercase			,							lowercase			,							lowercase  )
            else:
                  lowerCamelCase_	  =					v

      return new_state_dict

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: Optional[Any]			,							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      if len(lowercase  ) == 1:
            if len(v.shape  ) == 3:
                  # weight
                  lowerCamelCase_	  =					v[:, :, 0]
            else:
                  # bias
                  lowerCamelCase_	  =					v
      else:
            # qkv matrices
            lowerCamelCase_	  =					v.shape[0]
            lowerCamelCase_	  =					trippled_shape // 3
            for i in range(3  ):
                  if len(v.shape  ) == 3:
                        lowerCamelCase_	  =					v[i * single_shape : (i + 1) * single_shape, :, 0]
                  else:
                        lowerCamelCase_	  =					v[i * single_shape : (i + 1) * single_shape]
      return new_state_dict

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

      lowerCamelCase_	  =					args.model_path.split('/'  )[-1].split('.'  )[0]
      if not os.path.isfile(args.model_path  ):
            assert (
                model_name == args.model_path
            ), f"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
            lowerCamelCase_	  =					download(lowercase  )

      lowerCamelCase_	  =					MODELS_MAP[model_name]['sample_rate']
      lowerCamelCase_	  =					MODELS_MAP[model_name]['sample_size']

      lowerCamelCase_	  =					Object()
      lowerCamelCase_	  =					sample_size
      lowerCamelCase_	  =					sample_rate
      lowerCamelCase_	  =					0

      lowerCamelCase_	  =					UNetaDModel(sample_size=lowercase			,							sample_rate=lowercase  )
      lowerCamelCase_	  =					diffusers_model.state_dict()

      lowerCamelCase_	  =					DiffusionUncond(lowercase  )
      orig_model.load_state_dict(torch.load(args.model_path			,							map_location=lowercase  )['state_dict']  )
      lowerCamelCase_	  =					orig_model.diffusion_ema.eval()
      lowerCamelCase_	  =					orig_model.state_dict()
      lowerCamelCase_	  =					rename_orig_weights(lowercase  )

      lowerCamelCase_	  =					set(renamed_state_dict.keys()  ) - set(diffusers_state_dict.keys()  )
      lowerCamelCase_	  =					set(diffusers_state_dict.keys()  ) - set(renamed_state_dict.keys()  )

      assert len(lowercase  ) == 0, f"""Problem with {renamed_minus_diffusers}"""
      assert all(k.endswith('kernel'  ) for k in list(lowercase  )  ), f"""Problem with {diffusers_minus_renamed}"""

      for key, value in renamed_state_dict.items():
            assert (
                diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
            ), f"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
            if key == "time_proj.weight":
                  lowerCamelCase_	  =					value.squeeze()

            lowerCamelCase_	  =					value

      diffusers_model.load_state_dict(lowercase  )

      lowerCamelCase_	  =					1_00
      lowerCamelCase_	  =					33

      lowerCamelCase_	  =					IPNDMScheduler(num_train_timesteps=lowercase  )

      lowerCamelCase_	  =					torch.manual_seed(lowercase  )
      lowerCamelCase_	  =					torch.randn([1, 2, config.sample_size]			,							generator=lowercase  ).to(lowercase  )

      lowerCamelCase_	  =					torch.linspace(1			,							0			,							steps + 1			,							device=lowercase  )[:-1]
      lowerCamelCase_	  =					get_crash_schedule(lowercase  )

      lowerCamelCase_	  =					DanceDiffusionPipeline(unet=lowercase			,							scheduler=lowercase  )

      lowerCamelCase_	  =					torch.manual_seed(33  )
      lowerCamelCase_	  =					pipe(num_inference_steps=lowercase			,							generator=lowercase  ).audios

      lowerCamelCase_	  =					sampling.iplms_sample(lowercase			,							lowercase			,							lowercase			,							{}  )
      lowerCamelCase_	  =					generated.clamp(-1			,							1  )

      lowerCamelCase_	  =					(generated - audio).abs().sum()
      lowerCamelCase_	  =					(generated - audio).abs().max()

      if args.save:
            pipe.save_pretrained(args.checkpoint_path  )

      print('Diff sum'			,							lowercase  )
      print('Diff max'			,							lowercase  )

      assert diff_max < 1e-3, f"""Diff max: {diff_max} is too much :-/"""

      print(f"""Conversion for {model_name} successful!"""  )


if __name__ == "__main__":
 lowerCamelCase       :	Union[str, Any]									=     argparse.ArgumentParser()

 parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
 parser.add_argument(
     "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
 )
 parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
 lowerCamelCase       :	Dict									=     parser.parse_args()

 main(args)










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						)



