




'''simple docstring'''




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     a_   :	Tuple							   = """platform"""

     import jax
     import jax.numpy as jnp

     from transformers import AutoTokenizer
     from transformers.models.mbart.modeling_flax_mbart import (
         FlaxMBartForConditionalGeneration,
         FlaxMBartForQuestionAnswering,
         FlaxMBartForSequenceClassification,
         FlaxMBartModel,
         shift_tokens_right,
     )





def   a_							(   __snake_case							:    Dict     , __snake_case							:    Optional[Any]     , __snake_case							:    List[Any]=None     , __snake_case							:    Tuple=None     , __snake_case							:    Any=None     , __snake_case							:    Optional[Any]=None     , __snake_case							:    List[str]=None     , __snake_case							:    Any=None     , )							->		Tuple:



       """simple docstring"""
       if attention_mask is None:
              lowerCamelCase_							=np.where(input_ids != config.pad_token_id     , 1     , 0     )
       if decoder_attention_mask is None:
              lowerCamelCase_							=np.where(decoder_input_ids != config.pad_token_id     , 1     , 0     )
       if head_mask is None:
              lowerCamelCase_							=np.ones((config.encoder_layers, config.encoder_attention_heads)     )
       if decoder_head_mask is None:
              lowerCamelCase_							=np.ones((config.decoder_layers, config.decoder_attention_heads)     )
       if cross_attn_head_mask is None:
              lowerCamelCase_							=np.ones((config.decoder_layers, config.decoder_attention_heads)     )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }


class 						__UpperCamelCase (				unittest.TestCase       ):






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase=13,	lowerCAmelCase=7,	lowerCAmelCase=True,	lowerCAmelCase=False,	lowerCAmelCase=99,	lowerCAmelCase=16,	lowerCAmelCase=2,	lowerCAmelCase=4,	lowerCAmelCase=4,	lowerCAmelCase="gelu",	lowerCAmelCase=0.1,	lowerCAmelCase=0.1,	lowerCAmelCase=32,	lowerCAmelCase=2,	lowerCAmelCase=1,	lowerCAmelCase=0,	lowerCAmelCase=2,	lowerCAmelCase=0.0_2,	):



              """simple docstring"""







              lowerCamelCase_							=parent
              lowerCamelCase_							=batch_size
              lowerCamelCase_							=seq_length
              lowerCamelCase_							=is_training
              lowerCamelCase_							=use_labels
              lowerCamelCase_							=vocab_size
              lowerCamelCase_							=hidden_size
              lowerCamelCase_							=num_hidden_layers
              lowerCamelCase_							=num_attention_heads
              lowerCamelCase_							=intermediate_size
              lowerCamelCase_							=hidden_act
              lowerCamelCase_							=hidden_dropout_prob
              lowerCamelCase_							=attention_probs_dropout_prob
              lowerCamelCase_							=max_position_embeddings
              lowerCamelCase_							=eos_token_id
              lowerCamelCase_							=pad_token_id
              lowerCamelCase_							=bos_token_id
              lowerCamelCase_							=decoder_start_token_id
              lowerCamelCase_							=initializer_range






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=np.clip(ids_tensor([self.batch_size, self.seq_length - 1],	self.vocab_size						),	3,	self.vocab_size						)
              lowerCamelCase_							=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),	dtype=np.intaa						)),	-1						)

              lowerCamelCase_							=shift_tokens_right(lowerCAmelCase,	1						)

              lowerCamelCase_							=MBartConfig(
                  vocab_size=self.vocab_size,	d_model=self.hidden_size,	encoder_layers=self.num_hidden_layers,	decoder_layers=self.num_hidden_layers,	encoder_attention_heads=self.num_attention_heads,	decoder_attention_heads=self.num_attention_heads,	encoder_ffn_dim=self.intermediate_size,	decoder_ffn_dim=self.intermediate_size,	dropout=self.hidden_dropout_prob,	attention_dropout=self.attention_probs_dropout_prob,	max_position_embeddings=self.max_position_embeddings,	eos_token_id=self.eos_token_id,	bos_token_id=self.bos_token_id,	pad_token_id=self.pad_token_id,	decoder_start_token_id=self.decoder_start_token_id,	initializer_range=self.initializer_range,	use_cache=lowerCAmelCase,	)
              lowerCamelCase_							=prepare_mbart_inputs_dict(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
              return config, inputs_dict






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.prepare_config_and_inputs()
              return config, inputs_dict






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=20
              lowerCamelCase_							=model_class_name(lowerCAmelCase						)

              lowerCamelCase_							=model.encode(inputs_dict['''input_ids''']						)

              lowerCamelCase_,   lowerCamelCase_							=(
                  inputs_dict['''decoder_input_ids'''],
                  inputs_dict['''decoder_attention_mask'''],
              )

              lowerCamelCase_							=model.init_cache(decoder_input_ids.shape[0],	lowerCAmelCase,	lowerCAmelCase						)
              lowerCamelCase_							=jnp.ones((decoder_input_ids.shape[0], max_decoder_length),	dtype='''i4'''						)

              lowerCamelCase_							=jnp.broadcast_to(
                  jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :],	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),	)
              lowerCamelCase_							=model.decode(
                  decoder_input_ids[:, :-1],	lowerCAmelCase,	decoder_attention_mask=lowerCAmelCase,	past_key_values=lowerCAmelCase,	decoder_position_ids=lowerCAmelCase,	)

              lowerCamelCase_							=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],	dtype='''i4'''						)
              lowerCamelCase_							=model.decode(
                  decoder_input_ids[:, -1:],	lowerCAmelCase,	decoder_attention_mask=lowerCAmelCase,	past_key_values=outputs_cache.past_key_values,	decoder_position_ids=lowerCAmelCase,	)

              lowerCamelCase_							=model.decode(lowerCAmelCase,	lowerCAmelCase						)

              lowerCamelCase_							=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
              self.parent.assertTrue(diff < 1e-3,	msg=f'''Max diff is {diff}'''						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=20
              lowerCamelCase_							=model_class_name(lowerCAmelCase						)

              lowerCamelCase_							=model.encode(inputs_dict['''input_ids''']						)

              lowerCamelCase_,   lowerCamelCase_							=(
                  inputs_dict['''decoder_input_ids'''],
                  inputs_dict['''decoder_attention_mask'''],
              )

              lowerCamelCase_							=jnp.concatenate(
                  [
                      decoder_attention_mask,
                      jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
                  ],	axis=-1,	)

              lowerCamelCase_							=model.init_cache(decoder_input_ids.shape[0],	lowerCAmelCase,	lowerCAmelCase						)
              lowerCamelCase_							=jnp.broadcast_to(
                  jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :],	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),	)

              lowerCamelCase_							=model.decode(
                  decoder_input_ids[:, :-1],	lowerCAmelCase,	decoder_attention_mask=lowerCAmelCase,	past_key_values=lowerCAmelCase,	decoder_position_ids=lowerCAmelCase,	)
              lowerCamelCase_							=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],	dtype='''i4'''						)
              lowerCamelCase_							=model.decode(
                  decoder_input_ids[:, -1:],	lowerCAmelCase,	past_key_values=outputs_cache.past_key_values,	decoder_attention_mask=lowerCAmelCase,	decoder_position_ids=lowerCAmelCase,	)

              lowerCamelCase_							=model.decode(lowerCAmelCase,	lowerCAmelCase,	decoder_attention_mask=lowerCAmelCase						)

              lowerCamelCase_							=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
              self.parent.assertTrue(diff < 1e-3,	msg=f'''Max diff is {diff}'''						)





@require_flax
class 						__UpperCamelCase (				unittest.TestCase       ):
       lowercase			:					Dict   		=99






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=np.array(
                  [
                      [71, 82, 18, 33, 46, 91, 2],
                      [68, 34, 26, 58, 30, 82, 2],
                      [5, 97, 17, 39, 94, 40, 2],
                      [76, 83, 94, 25, 70, 78, 2],
                      [87, 59, 41, 35, 48, 66, 2],
                      [55, 13, 16, 58, 5, 2, 1],  # note padding
                      [64, 27, 31, 51, 12, 75, 2],
                      [52, 64, 86, 17, 83, 39, 2],
                      [48, 61, 9, 24, 71, 82, 2],
                      [26, 1, 60, 48, 22, 13, 2],
                      [21, 5, 62, 28, 14, 76, 2],
                      [45, 98, 37, 86, 59, 48, 2],
                      [70, 70, 50, 9, 28, 0, 2],
                  ],	dtype=np.intaa,	)

              lowerCamelCase_							=input_ids.shape[0]
              lowerCamelCase_							=MBartConfig(
                  vocab_size=self.vocab_size,	d_model=24,	encoder_layers=2,	decoder_layers=2,	encoder_attention_heads=2,	decoder_attention_heads=2,	encoder_ffn_dim=32,	decoder_ffn_dim=32,	max_position_embeddings=48,	eos_token_id=2,	pad_token_id=1,	bos_token_id=0,	)
              return config, input_ids, batch_size






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._get_config_and_data()
              lowerCamelCase_							=FlaxMBartForSequenceClassification(lowerCAmelCase						)
              lowerCamelCase_							=model(input_ids=lowerCAmelCase,	decoder_input_ids=lowerCAmelCase						)
              lowerCamelCase_							=(batch_size, config.num_labels)
              self.assertEqual(outputs['''logits'''].shape,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._get_config_and_data()
              lowerCamelCase_							=FlaxMBartForQuestionAnswering(lowerCAmelCase						)
              lowerCamelCase_							=model(input_ids=lowerCAmelCase						)

              self.assertEqual(outputs['''start_logits'''].shape,	input_ids.shape						)
              self.assertEqual(outputs['''end_logits'''].shape,	input_ids.shape						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._get_config_and_data()
              lowerCamelCase_							=FlaxMBartForConditionalGeneration(lowerCAmelCase						)
              lowerCamelCase_							=lm_model(input_ids=lowerCAmelCase						)
              lowerCamelCase_							=(batch_size, input_ids.shape[1], config.vocab_size)
              self.assertEqual(outputs['''logits'''].shape,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=MBartConfig(
                  vocab_size=self.vocab_size,	d_model=14,	encoder_layers=2,	decoder_layers=2,	encoder_attention_heads=2,	decoder_attention_heads=2,	encoder_ffn_dim=8,	decoder_ffn_dim=8,	max_position_embeddings=48,	)
              lowerCamelCase_							=FlaxMBartForConditionalGeneration(lowerCAmelCase						)
              lowerCamelCase_							=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],	dtype=np.intaa						)
              lowerCamelCase_							=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],	dtype=np.intaa						)
              lowerCamelCase_							=lm_model(input_ids=lowerCAmelCase,	decoder_input_ids=lowerCAmelCase						)
              lowerCamelCase_							=(*summary.shape, config.vocab_size)
              self.assertEqual(outputs['''logits'''].shape,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],	dtype=np.intaa						)
              lowerCamelCase_							=shift_tokens_right(lowerCAmelCase,	1						)
              lowerCamelCase_							=np.equal(lowerCAmelCase,	1						).astype(np.floataa						).sum()
              lowerCamelCase_							=np.equal(lowerCAmelCase,	1						).astype(np.floataa						).sum()
              self.assertEqual(shifted.shape,	input_ids.shape						)
              self.assertEqual(lowerCAmelCase,	n_pad_before - 1						)
              self.assertTrue(np.equal(shifted[:, 0],	2						).all()						)





@require_flax
class 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase    ,      lowerCamelCase__       ):
       lowercase			:					Any   		=True
       lowercase			:					Union[str, Any]   		=(
           (
               FlaxMBartModel,
               FlaxMBartForConditionalGeneration,
               FlaxMBartForSequenceClassification,
               FlaxMBartForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       lowercase			:					Union[str, Any]   		=(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=FlaxMBartModelTester(self						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              for model_class in self.all_model_classes:
                     self.model_tester.check_use_cache_forward(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              for model_class in self.all_model_classes:
                     self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__						):
                            lowerCamelCase_							=self._prepare_for_class(lowerCAmelCase,	lowerCAmelCase						)
                            lowerCamelCase_							=model_class(lowerCAmelCase						)

                            @jax.jit
                            def encode_jitted(lowerCAmelCase,	lowerCAmelCase=None,	**lowerCAmelCase						):
                                   return model.encode(input_ids=lowerCAmelCase,	attention_mask=lowerCAmelCase						)

                            with self.subTest('''JIT Enabled'''						):
                                   lowerCamelCase_							=encode_jitted(**lowerCAmelCase						).to_tuple()

                            with self.subTest('''JIT Disabled'''						):
                                   with jax.disable_jit():
                                          lowerCamelCase_							=encode_jitted(**lowerCAmelCase						).to_tuple()

                            self.assertEqual(len(lowerCAmelCase						),	len(lowerCAmelCase						)						)
                            for jitted_output, output in zip(lowerCAmelCase,	lowerCAmelCase						):
                                   self.assertEqual(jitted_output.shape,	output.shape						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__						):
                            lowerCamelCase_							=model_class(lowerCAmelCase						)
                            lowerCamelCase_							=model.encode(inputs_dict['''input_ids'''],	inputs_dict['''attention_mask''']						)

                            lowerCamelCase_							={
                                '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                                '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                                '''encoder_outputs''': encoder_outputs,
                            }

                            @jax.jit
                            def decode_jitted(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):
                                   return model.decode(
                                       decoder_input_ids=lowerCAmelCase,	decoder_attention_mask=lowerCAmelCase,	encoder_outputs=lowerCAmelCase,	)

                            with self.subTest('''JIT Enabled'''						):
                                   lowerCamelCase_							=decode_jitted(**lowerCAmelCase						).to_tuple()

                            with self.subTest('''JIT Disabled'''						):
                                   with jax.disable_jit():
                                          lowerCamelCase_							=decode_jitted(**lowerCAmelCase						).to_tuple()

                            self.assertEqual(len(lowerCAmelCase						),	len(lowerCAmelCase						)						)
                            for jitted_output, output in zip(lowerCAmelCase,	lowerCAmelCase						):
                                   self.assertEqual(jitted_output.shape,	output.shape						)






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







              for model_class_name in self.all_model_classes:
                     lowerCamelCase_							=model_class_name.from_pretrained('''facebook/mbart-large-cc25''',	from_pt=lowerCAmelCase						)
                     # FlaxMBartForSequenceClassification expects eos token in input_ids
                     lowerCamelCase_							=np.ones((1, 1)						) * model.config.eos_token_id
                     lowerCamelCase_							=model(lowerCAmelCase						)
                     self.assertIsNotNone(lowerCAmelCase						)





@require_flax
@require_sentencepiece
@require_tokenizers
class 						__UpperCamelCase (				unittest.TestCase       ):
       lowercase			:					Tuple   		=[
           ' UN Chief Says There Is No Military Solution in Syria',
       ]
       lowercase			:					str   		=[
           'Şeful ONU declară că nu există o soluţie militară în Siria',
       ]
       lowercase			:					Optional[int]   		='facebook/mbart-large-en-ro'






       @cached_property
       def   lowercase__  (							self						):



              """simple docstring"""







              return AutoTokenizer.from_pretrained(self.model_name						)






       @cached_property
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,	from_pt=lowerCAmelCase						)
              return model






       def   lowercase__  (							self,	**lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.translate_src_text(**lowerCAmelCase						)
              self.assertListEqual(self.expected_text,	lowerCAmelCase						)






       def   lowercase__  (							self,	**lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.tokenizer(self.src_text,	**lowerCAmelCase,	return_tensors='''np'''						)
              lowerCamelCase_							=self.model.generate(
                  model_inputs.input_ids,	attention_mask=model_inputs.attention_mask,	decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO'''],	early_stopping=lowerCAmelCase,	num_beams=2,	).sequences
              lowerCamelCase_							=self.tokenizer.batch_decode(lowerCAmelCase,	skip_special_tokens=lowerCAmelCase						)
              return generated_words






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







              self._assert_generated_batch_equal_expected()










'''simple docstring'''




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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


a_   :	Optional[Any]							   = logging.get_logger(__name__)  # pylint: disable=invalid-name





def   a_							(   self							:    Union[str, Any]     , __snake_case							:    List[str]     , __snake_case							:    List[str]     , __snake_case							:    Dict     , __snake_case							:    str     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=image.to(device=__snake_case     )
       lowerCamelCase_							=image  # take image as image_embeddings
       lowerCamelCase_							=image_embeddings.unsqueeze(1     )

       # duplicate image embeddings for each generation per prompt, using mps friendly method
       lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=image_embeddings.shape
       lowerCamelCase_							=image_embeddings.repeat(1     , __snake_case     , 1     )
       lowerCamelCase_							=image_embeddings.view(bs_embed * num_images_per_prompt     , __snake_case     , -1     )

       if do_classifier_free_guidance:
              lowerCamelCase_							=torch.zeros_like(__snake_case     )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              lowerCamelCase_							=torch.cat([uncond_embeddings, image_embeddings]     )

       return image_embeddings


class 						__UpperCamelCase (				lowerCamelCase__       ):






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	):



              """simple docstring"""







              super().__init__()

              lowerCamelCase_							={'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

              lowerCamelCase_							=decoder_pipe_kwargs.get('''torch_dtype''',	lowerCAmelCase						) or prior.dtype

              lowerCamelCase_							=StableDiffusionImageVariationPipeline.from_pretrained(
                  '''lambdalabs/sd-image-variations-diffusers''',	**lowerCAmelCase						)

              # replace `_encode_image` method
              lowerCamelCase_							=types.MethodType(_encode_image,	self.decoder_pipe						)

              self.register_modules(
                  prior=lowerCAmelCase,	tokenizer=lowerCAmelCase,	text_encoder=lowerCAmelCase,	prior_scheduler=lowerCAmelCase,	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	lowerCAmelCase = None,	):



              """simple docstring"""







              if text_model_output is None:
                     lowerCamelCase_							=len(lowerCAmelCase						) if isinstance(lowerCAmelCase,	lowerCAmelCase						) else 1
                     # get prompt text embeddings
                     lowerCamelCase_							=self.tokenizer(
                         lowerCAmelCase,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	return_tensors='''pt''',	)
                     lowerCamelCase_							=text_inputs.input_ids
                     lowerCamelCase_							=text_inputs.attention_mask.bool().to(lowerCAmelCase						)

                     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                            lowerCamelCase_							=self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]						)
                            logger.warning(
                                '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                                f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''						)
                            lowerCamelCase_							=text_input_ids[:, : self.tokenizer.model_max_length]

                     lowerCamelCase_							=self.text_encoder(text_input_ids.to(lowerCAmelCase						)						)

                     lowerCamelCase_							=text_encoder_output.text_embeds
                     lowerCamelCase_							=text_encoder_output.last_hidden_state

              else:
                     lowerCamelCase_							=text_model_output[0].shape[0]
                     lowerCamelCase_,   lowerCamelCase_							=text_model_output[0], text_model_output[1]
                     lowerCamelCase_							=text_attention_mask

              lowerCamelCase_							=text_embeddings.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_encoder_hidden_states.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_mask.repeat_interleave(lowerCAmelCase,	dim=0						)

              if do_classifier_free_guidance:
                     lowerCamelCase_							=[''''''] * batch_size

                     lowerCamelCase_							=self.tokenizer(
                         lowerCAmelCase,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	truncation=lowerCAmelCase,	return_tensors='''pt''',	)
                     lowerCamelCase_							=uncond_input.attention_mask.bool().to(lowerCAmelCase						)
                     lowerCamelCase_							=self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase						)						)

                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.text_embeds
                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.last_hidden_state

                     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

                     lowerCamelCase_							=uncond_embeddings.shape[1]
                     lowerCamelCase_							=uncond_embeddings.repeat(1,	lowerCAmelCase						)
                     lowerCamelCase_							=uncond_embeddings.view(batch_size * num_images_per_prompt,	lowerCAmelCase						)

                     lowerCamelCase_							=uncond_text_encoder_hidden_states.shape[1]
                     lowerCamelCase_							=uncond_text_encoder_hidden_states.repeat(1,	lowerCAmelCase,	1						)
                     lowerCamelCase_							=uncond_text_encoder_hidden_states.view(
                         batch_size * num_images_per_prompt,	lowerCAmelCase,	-1						)
                     lowerCamelCase_							=uncond_text_mask.repeat_interleave(lowerCAmelCase,	dim=0						)

                     # done duplicates

                     # For classifier free guidance, we need to do two forward passes.
                     # Here we concatenate the unconditional and text embeddings into a single batch
                     # to avoid doing two forward passes
                     lowerCamelCase_							=torch.cat([uncond_embeddings, text_embeddings]						)
                     lowerCamelCase_							=torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

                     lowerCamelCase_							=torch.cat([uncond_text_mask, text_mask]						)

              return text_embeddings, text_encoder_hidden_states, text_mask






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              if self.device != torch.device('''meta'''						) or not hasattr(self.prior,	'''_hf_hook'''						):
                     return self.device
              for module in self.prior.modules():
                     if (
                         hasattr(lowerCAmelCase,	'''_hf_hook'''						)
                         and hasattr(module._hf_hook,	'''execution_device'''						)
                         and module._hf_hook.execution_device is not None
                     ):
                            return torch.device(module._hf_hook.execution_device						)
              return self.device






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              if latents is None:
                     lowerCamelCase_							=randn_tensor(lowerCAmelCase,	generator=lowerCAmelCase,	device=lowerCAmelCase,	dtype=lowerCAmelCase						)
              else:
                     if latents.shape != shape:
                            raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''						)
                     lowerCamelCase_							=latents.to(lowerCAmelCase						)

              lowerCamelCase_							=latents * scheduler.init_noise_sigma
              return latents






       def   lowercase__  (							self,	lowerCAmelCase = None						):



              """simple docstring"""







              self.decoder_pipe.to(lowerCAmelCase						)
              super().to(lowerCAmelCase						)






       @torch.no_grad()
       def __call__(							self,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = 1,	lowerCAmelCase = 25,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = 4.0,	lowerCAmelCase = 8.0,	lowerCAmelCase = 50,	lowerCAmelCase = 1,	lowerCAmelCase = 0.0,	lowerCAmelCase = "pil",	lowerCAmelCase = True,	):



              """simple docstring"""







              if prompt is not None:
                     if isinstance(lowerCAmelCase,	lowerCAmelCase						):
                            lowerCamelCase_							=1
                     elif isinstance(lowerCAmelCase,	lowerCAmelCase						):
                            lowerCamelCase_							=len(lowerCAmelCase						)
                     else:
                            raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase						)}'''						)
              else:
                     lowerCamelCase_							=text_model_output[0].shape[0]

              lowerCamelCase_							=self._execution_device

              lowerCamelCase_							=batch_size * num_images_per_prompt

              lowerCamelCase_							=prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._encode_prompt(
                  lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)

              # prior

              self.prior_scheduler.set_timesteps(lowerCAmelCase,	device=lowerCAmelCase						)
              lowerCamelCase_							=self.prior_scheduler.timesteps

              lowerCamelCase_							=self.prior.config.embedding_dim

              lowerCamelCase_							=self.prepare_latents(
                  (batch_size, embedding_dim),	text_embeddings.dtype,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	self.prior_scheduler,	)

              for i, t in enumerate(self.progress_bar(lowerCAmelCase						)						):
                     # expand the latents if we are doing classifier free guidance
                     lowerCamelCase_							=torch.cat([prior_latents] * 2						) if do_classifier_free_guidance else prior_latents

                     lowerCamelCase_							=self.prior(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	proj_embedding=lowerCAmelCase,	encoder_hidden_states=lowerCAmelCase,	attention_mask=lowerCAmelCase,	).predicted_image_embedding

                     if do_classifier_free_guidance:
                            lowerCamelCase_,   lowerCamelCase_							=predicted_image_embedding.chunk(2						)
                            lowerCamelCase_							=predicted_image_embedding_uncond + prior_guidance_scale * (
                                predicted_image_embedding_text - predicted_image_embedding_uncond
                            )

                     if i + 1 == prior_timesteps_tensor.shape[0]:
                            lowerCamelCase_							=None
                     else:
                            lowerCamelCase_							=prior_timesteps_tensor[i + 1]

                     lowerCamelCase_							=self.prior_scheduler.step(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	sample=lowerCAmelCase,	generator=lowerCAmelCase,	prev_timestep=lowerCAmelCase,	).prev_sample

              lowerCamelCase_							=self.prior.post_process_latents(lowerCAmelCase						)

              lowerCamelCase_							=prior_latents

              lowerCamelCase_							=self.decoder_pipe(
                  image=lowerCAmelCase,	height=lowerCAmelCase,	width=lowerCAmelCase,	num_inference_steps=lowerCAmelCase,	guidance_scale=lowerCAmelCase,	generator=lowerCAmelCase,	output_type=lowerCAmelCase,	return_dict=lowerCAmelCase,	num_images_per_prompt=lowerCAmelCase,	eta=lowerCAmelCase,	)
              return output




