

'''simple docstring'''




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def __init__(   self		:   Optional[Any]			,				a__		:   List[Any]			,				a__		:   List[str]			,				a__		:   Union[str, Any]			,				a__		:   List[Any] = None			,				):
      super().__init__()
      self.register_modules(transformer=lowerCamelCase_			,				vae=lowerCamelCase_			,				scheduler=lowerCamelCase_      )

      # create a imagenet -> id dictionary for easier use
      __magic_name__						       =    {}
      if idalabel is not None:
           for key, value in idalabel.items():
                for label in value.split(''','''      ):
                     __magic_name__						       =    int(lowerCamelCase_      )
           __magic_name__						       =    dict(sorted(self.labels.items()      )      )



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Dict      ):

      if not isinstance(lowerCamelCase_			,				lowerCamelCase_      ):
           __magic_name__						       =    list(lowerCamelCase_      )

      for l in label:
           if l not in self.labels:
                raise ValueError(
                    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''      )

      return [self.labels[l] for l in label]



 @torch.no_grad()
 def __call__(   self		:   List[str]			,				a__		:   Optional[Any]			,				a__		:   Tuple = 4.0			,				a__		:   str = None			,				a__		:   Dict = 50			,				a__		:   List[str] = "pil"			,				a__		:   List[str] = True			,				):
      __magic_name__						       =    len(lowerCamelCase_      )
      __magic_name__						       =    self.transformer.config.sample_size
      __magic_name__						       =    self.transformer.config.in_channels

      __magic_name__						       =    randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)			,				generator=lowerCamelCase_			,				device=self.device			,				dtype=self.transformer.dtype			,				)
      __magic_name__						       =    torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

      __magic_name__						       =    torch.tensor(lowerCamelCase_			,				device=self.device      ).reshape(-1      )
      __magic_name__						       =    torch.tensor([1000] * batch_size			,				device=self.device      )
      __magic_name__						       =    torch.cat([class_labels, class_null]			,				0      ) if guidance_scale > 1 else class_labels

      # set step values
      self.scheduler.set_timesteps(lowerCamelCase_      )

      for t in self.progress_bar(self.scheduler.timesteps      ):
           if guidance_scale > 1:
                __magic_name__						       =    latent_model_input[: len(lowerCamelCase_      ) // 2]
                __magic_name__						       =    torch.cat([half, half]			,				dim=0      )
           __magic_name__						       =    self.scheduler.scale_model_input(lowerCamelCase_			,				lowerCamelCase_      )

           __magic_name__						       =    t
           if not torch.is_tensor(lowerCamelCase_      ):
                # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                # This would be a good case for the `match` statement (Python 3.10+)
                __magic_name__						       =    latent_model_input.device.type == '''mps'''
                if isinstance(lowerCamelCase_			,				lowerCamelCase_      ):
                     __magic_name__						       =    torch.floataa if is_mps else torch.floataa
                else:
                     __magic_name__						       =    torch.intaa if is_mps else torch.intaa
                __magic_name__						       =    torch.tensor([timesteps]			,				dtype=lowerCamelCase_			,				device=latent_model_input.device      )
           elif len(timesteps.shape      ) == 0:
                __magic_name__						       =    timesteps[None].to(latent_model_input.device      )
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           __magic_name__						       =    timesteps.expand(latent_model_input.shape[0]      )
           # predict noise model_output
           __magic_name__						       =    self.transformer(
               lowerCamelCase_			,				timestep=lowerCamelCase_			,				class_labels=lowerCamelCase_      ).sample

           # perform guidance
           if guidance_scale > 1:
                __magic_name__       ,  __magic_name__						       =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                __magic_name__       ,  __magic_name__						       =    torch.split(lowerCamelCase_			,				len(lowerCamelCase_      ) // 2			,				dim=0      )

                __magic_name__						       =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                __magic_name__						       =    torch.cat([half_eps, half_eps]			,				dim=0      )

                __magic_name__						       =    torch.cat([eps, rest]			,				dim=1      )

           # learned sigma
           if self.transformer.config.out_channels // 2 == latent_channels:
                __magic_name__       ,  __magic_name__						       =    torch.split(lowerCamelCase_			,				lowerCamelCase_			,				dim=1      )
           else:
                __magic_name__						       =    noise_pred

           # compute previous image: x_t -> x_t-1
           __magic_name__						       =    self.scheduler.step(lowerCamelCase_			,				lowerCamelCase_			,				lowerCamelCase_      ).prev_sample

      if guidance_scale > 1:
           __magic_name__       ,  __magic_name__						       =    latent_model_input.chunk(2			,				dim=0      )
      else:
           __magic_name__						       =    latent_model_input

      __magic_name__						       =    1 / self.vae.config.scaling_factor * latents
      __magic_name__						       =    self.vae.decode(lowerCamelCase_      ).sample

      __magic_name__						       =    (samples / 2 + 0.5).clamp(0			,				1      )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      __magic_name__						       =    samples.cpu().permute(0			,				2			,				3			,				1      ).float().numpy()

      if output_type == "pil":
           __magic_name__						       =    self.numpy_to_pil(lowerCamelCase_      )

      if not return_dict:
           return (samples,)

      return ImagePipelineOutput(images=lowerCamelCase_      )









'''simple docstring'''




from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTransfoXLForSequenceClassification,
          TFTransfoXLLMHeadModel,
          TFTransfoXLModel,
      )






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[str]			,				a__		:   int			,				):
      __magic_name__						       =    parent
      __magic_name__						       =    13
      __magic_name__						       =    7
      __magic_name__						       =    30
      __magic_name__						       =    self.seq_length + self.mem_len
      __magic_name__						       =    15
      __magic_name__						       =    True
      __magic_name__						       =    True
      __magic_name__						       =    99
      __magic_name__						       =    [10, 50, 80]
      __magic_name__						       =    32
      __magic_name__						       =    32
      __magic_name__						       =    4
      __magic_name__						       =    8
      __magic_name__						       =    128
      __magic_name__						       =    2
      __magic_name__						       =    2
      __magic_name__						       =    None
      __magic_name__						       =    1
      __magic_name__						       =    0
      __magic_name__						       =    3
      __magic_name__						       =    self.vocab_size - 1
      __magic_name__						       =    0.01



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size      )
      __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size      )

      __magic_name__						       =    None
      if self.use_labels:
           __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size      )

      __magic_name__						       =    TransfoXLConfig(
          vocab_size=self.vocab_size			,				mem_len=self.mem_len			,				clamp_len=self.clamp_len			,				cutoffs=self.cutoffs			,				d_model=self.hidden_size			,				d_embed=self.d_embed			,				n_head=self.num_attention_heads			,				d_head=self.d_head			,				d_inner=self.d_inner			,				div_val=self.div_val			,				n_layer=self.num_hidden_layers			,				eos_token_id=self.eos_token_id			,				pad_token_id=self.vocab_size - 1			,				init_range=self.init_range			,				num_labels=self.num_labels			,				)

      return (config, input_ids_a, input_ids_a, lm_labels)



 def 			snake_case__  (   self		:   str      ):
      random.seed(self.seed      )
      tf.random.set_seed(self.seed      )



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Tuple			,				a__		:   Any			,				a__		:   Union[str, Any]			,				a__		:   int      ):
      __magic_name__						       =    TFTransfoXLModel(a__      )

      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      __magic_name__						       =    {'''input_ids''': input_ids_a, '''mems''': mems_a}

      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      self.parent.assertEqual(hidden_states_a.shape			,				(self.batch_size, self.seq_length, self.hidden_size)      )
      self.parent.assertEqual(hidden_states_a.shape			,				(self.batch_size, self.seq_length, self.hidden_size)      )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]			,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,				)
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]			,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,				)



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   int			,				a__		:   List[str]			,				a__		:   List[str]			,				a__		:   Dict      ):
      __magic_name__						       =    TFTransfoXLLMHeadModel(a__      )

      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      __magic_name__						       =    {'''input_ids''': input_ids_a, '''labels''': lm_labels}
      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      __magic_name__       ,  __magic_name__						       =    model([input_ids_a, mems_a]      ).to_tuple()

      __magic_name__						       =    {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      self.parent.assertEqual(lm_logits_a.shape			,				(self.batch_size, self.seq_length, self.vocab_size)      )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]			,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,				)

      self.parent.assertEqual(lm_logits_a.shape			,				(self.batch_size, self.seq_length, self.vocab_size)      )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]			,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,				)



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   str			,				a__		:   Optional[int]			,				a__		:   Dict			,				a__		:   List[Any]      ):
      __magic_name__						       =    TFTransfoXLForSequenceClassification(a__      )
      __magic_name__						       =    model(a__      )
      self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)      )



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    self.prepare_config_and_inputs()
      ((__magic_name__)       ,  (__magic_name__)       ,  (__magic_name__)       ,  (__magic_name__))						       =    config_and_inputs
      __magic_name__						       =    {'''input_ids''': input_ids_a}
      return config, inputs_dict






@require_tf
class   _SCREAMING_SNAKE_CASE   (   __a		,__a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					(
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					() if is_tf_available() else ()
 __SCREAMING_SNAKE_CASE					:Any      	=					(
     {
         """feature-extraction""": TFTransfoXLModel,
         """text-classification""": TFTransfoXLForSequenceClassification,
         """text-generation""": TFTransfoXLLMHeadModel,
         """zero-shot""": TFTransfoXLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
 __SCREAMING_SNAKE_CASE					:List[str]      	=					False
 __SCREAMING_SNAKE_CASE					:str      	=					False
 __SCREAMING_SNAKE_CASE					:str      	=					False
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					False



 def 			snake_case__  (   self		:   int			,				a__		:   int			,				a__		:   List[str]			,				a__		:   Any			,				a__		:   Optional[Any]			,				a__		:   int      ):
      if pipeline_test_casse_name == "TextGenerationPipelineTests":
           # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
           # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
           # tokenizer.
           return True

      return False



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    TFTransfoXLModelTester(self      )
      __magic_name__						       =    ConfigTester(self			,				config_class=a__			,				d_embed=37      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      self.config_tester.run_common_tests()



 def 			snake_case__  (   self		:   int      ):
      self.model_tester.set_seed()
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      self.model_tester.set_seed()
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*a__      )



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__						       =    [TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
           __magic_name__						       =    model_class(a__      )
           assert isinstance(model.get_input_embeddings()			,				tf.keras.layers.Layer      )
           if model_class in list_other_models_with_output_ebd:
                __magic_name__						       =    model.get_output_embeddings()
                assert isinstance(a__			,				tf.keras.layers.Layer      )
                __magic_name__						       =    model.get_bias()
                assert name is None
           else:
                __magic_name__						       =    model.get_output_embeddings()
                assert x is None
                __magic_name__						       =    model.get_bias()
                assert name is None



 def 			snake_case__  (   self		:   Dict      ):
      # TODO JP: Make TransfoXL XLA compliant
      pass



 @slow
 def 			snake_case__  (   self		:   List[Any]      ):
      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    TFTransfoXLModel.from_pretrained(a__      )
           self.assertIsNotNone(a__      )



 @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''      )
 def 			snake_case__  (   self		:   Any      ):
      pass






@require_tf
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 @unittest.skip('''Skip test until #12651 is resolved.'''      )
 @slow
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
      # fmt: off
      __magic_name__						       =    tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]			,				dtype=tf.intaa      )  # noqa: E231
      # fmt: on
      #  In 1991 , the remains of Russian Tsar Nicholas II and his family
      #  ( except for Alexei and Maria ) are discovered .
      #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
      #  remainder of the story . 1883 Western Siberia ,
      #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
      #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
      #  father initially slaps him for making such an accusation , Rasputin watches as the
      #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
      #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
      #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

      # fmt: off
      __magic_name__						       =    [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
      # fmt: on
      #  In 1991, the remains of Russian Tsar Nicholas II and his family (
      #  except for Alexei and Maria ) are discovered. The voice of young son,
      #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
      #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
      #  and a group of men to perform magic. Rasputin has a vision and
      #  denounces one of the men as a horse thief. Although his father initially
      #  slaps him for making such an accusation, Rasputin watches as the man
      #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
      #  of the Virgin Mary, prompting him to become a priest.
      #  Rasputin quickly becomes famous, with people, even a bishop, begging for
      #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
      # Nicholas II and his family were discovered. The voice of <unk> young son,
      # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

      __magic_name__						       =    model.generate(a__			,				max_length=200			,				do_sample=a__      )
      self.assertListEqual(output_ids[0].numpy().tolist()			,				a__      )






