
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


_UpperCAmelCase    			=					logging.get_logger(__name__)  # pylint: disable=invalid-name


def 		_lowerCamelCase       (   self			,   _a			,   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						image.to(device=lowercase_  )
  _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			,   lowercase_			,   1  )
  _lowerCamelCase      =						image_embeddings.view(bs_embed * num_images_per_prompt			,   lowercase_			,   -1  )

  if do_classifier_free_guidance:
    _lowerCamelCase      =						torch.zeros_like(lowercase_  )

    # 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 		__magic_name__				( UpperCAmelCase_					):


  """simple docstring"""





  def __init__(					self	,		a__	,		a__	,		a__	,		a__	,		a__ = None	,		):
    super().__init__()

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

    _lowerCamelCase      =						decoder_pipe_kwargs.get('''torch_dtype'''	,		__UpperCamelCase		) or prior.dtype

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

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

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


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		a__	,		a__ = None	,		a__ = None	,		):
    if text_model_output is None:
      _lowerCamelCase      =						len(__UpperCamelCase		) if isinstance(__UpperCamelCase	,		__UpperCamelCase		) else 1
      # get prompt text embeddings
      _lowerCamelCase      =						self.tokenizer(
          __UpperCamelCase	,		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(__UpperCamelCase		)

      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(__UpperCamelCase		)		)

      _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(__UpperCamelCase	,		dim=0		)
    _lowerCamelCase      =						text_encoder_hidden_states.repeat_interleave(__UpperCamelCase	,		dim=0		)
    _lowerCamelCase      =						text_mask.repeat_interleave(__UpperCamelCase	,		dim=0		)

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

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

      _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	,		__UpperCamelCase		)
      _lowerCamelCase      =						uncond_embeddings.view(batch_size * num_images_per_prompt	,		__UpperCamelCase		)

      _lowerCamelCase      =						uncond_text_encoder_hidden_states.shape[1]
      _lowerCamelCase      =						uncond_text_encoder_hidden_states.repeat(1	,		__UpperCamelCase	,		1		)
      _lowerCamelCase      =						uncond_text_encoder_hidden_states.view(
          batch_size * num_images_per_prompt	,		__UpperCamelCase	,		-1		)
      _lowerCamelCase      =						uncond_text_mask.repeat_interleave(__UpperCamelCase	,		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     _UpperCAmelCase  (					self		):
    if self.device != torch.device('''meta'''		) or not hasattr(self.prior	,		'''_hf_hook'''		):
      return self.device
    for module in self.prior.modules():
      if (
          hasattr(__UpperCamelCase	,		'''_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     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		a__	,		a__	,		a__		):
    if latents is None:
      _lowerCamelCase      =						randn_tensor(__UpperCamelCase	,		generator=__UpperCamelCase	,		device=__UpperCamelCase	,		dtype=__UpperCamelCase		)
    else:
      if latents.shape != shape:
        raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
      _lowerCamelCase      =						latents.to(__UpperCamelCase		)

    _lowerCamelCase      =						latents * scheduler.init_noise_sigma
    return latents


  def     _UpperCAmelCase  (					self	,		a__ = None		):
    self.decoder_pipe.to(__UpperCamelCase		)
    super().to(__UpperCamelCase		)


  @torch.no_grad()
  def __call__(					self	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = 1	,		a__ = 25	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = 4.0	,		a__ = 8.0	,		a__ = 50	,		a__ = 1	,		a__ = 0.0	,		a__ = "pil"	,		a__ = True	,		):
    if prompt is not None:
      if isinstance(__UpperCamelCase	,		__UpperCamelCase		):
        _lowerCamelCase      =						1
      elif isinstance(__UpperCamelCase	,		__UpperCamelCase		):
        _lowerCamelCase      =						len(__UpperCamelCase		)
      else:
        raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase		)}'''		)
    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(
        __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase		)

    # prior

    self.prior_scheduler.set_timesteps(__UpperCamelCase	,		device=__UpperCamelCase		)
    _lowerCamelCase      =						self.prior_scheduler.timesteps

    _lowerCamelCase      =						self.prior.config.embedding_dim

    _lowerCamelCase      =						self.prepare_latents(
        (batch_size, embedding_dim)	,		text_embeddings.dtype	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		self.prior_scheduler	,		)

    for i, t in enumerate(self.progress_bar(__UpperCamelCase		)		):
      # 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(
          __UpperCamelCase	,		timestep=__UpperCamelCase	,		proj_embedding=__UpperCamelCase	,		encoder_hidden_states=__UpperCamelCase	,		attention_mask=__UpperCamelCase	,		).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(
          __UpperCamelCase	,		timestep=__UpperCamelCase	,		sample=__UpperCamelCase	,		generator=__UpperCamelCase	,		prev_timestep=__UpperCamelCase	,		).prev_sample

    _lowerCamelCase      =						self.prior.post_process_latents(__UpperCamelCase		)

    _lowerCamelCase      =						prior_latents

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










'''simple docstring'''







import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	lowerCamelCase_				(			UpperCAmelCase_,			unittest.TestCase				):




					'''simple docstring'''






					lowercase__       =					PreTrainedTokenizerFast
					lowercase__       =					False
					lowercase__       =					True
					lowercase__       =					"""tokenizer_file"""






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			False  # because we don't have pretrained_vocab_files_map
							super().setUp()
							_snake_case      			=			True

							_snake_case      			=			["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
							_snake_case      			=			"SaulLu/dummy-tokenizer-bytelevel-bpe"

							# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
							_snake_case      			=			[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

							_snake_case      			=			PreTrainedTokenizerFast.from_pretrained(model_paths[0]					)
							tokenizer.save_pretrained(self.tmpdirname					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tmpdirname
							# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
											try:
													_snake_case      			=			tempfile.mkdtemp()
													_snake_case      			=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase,    **__UpperCamelCase					)

													tokenizer.save_pretrained(self.tmpdirname					)
													super().test_training_new_tokenizer()
											finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname					)
													_snake_case      			=			tmpdirname_orig






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tmpdirname
							# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
											try:
													_snake_case      			=			tempfile.mkdtemp()
													_snake_case      			=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase,    **__UpperCamelCase					)

													tokenizer.save_pretrained(self.tmpdirname					)
													super().test_training_new_tokenizer_with_special_tokens_change()
											finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname					)
													_snake_case      			=			tmpdirname_orig






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name					)

							_snake_case      			=			("a" for _ in range(1_000					))
							_snake_case      			=			tokenizer.train_new_from_iterator(text_iterator=__UpperCamelCase,    length=1_000,    vocab_size=50					)

							_snake_case      			=			new_tokenizer.encode("a🤗"					)
							self.assertEqual(__UpperCamelCase,    [64, 172, 253, 97, 245]					)





@require_tokenizers
class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			AutoTokenizer.from_pretrained("bert-base-cased"					)
							_snake_case      			=			json.loads(tokenizer._tokenizer.to_str()					)
							_snake_case      			=			len(__UpperCamelCase					)

							with tempfile.TemporaryDirectory() as tmp_dir:
									# Hack to save this in the tokenizer_config.json
									_snake_case      			=			["tokenizer.4.0.0.json"]
									tokenizer.save_pretrained(__UpperCamelCase					)
									json.dump(__UpperCamelCase,    open(os.path.join(__UpperCamelCase,    "tokenizer.4.0.0.json"					),    "w"					)					)

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
									self.assertEqual(len(__UpperCamelCase					),    len(__UpperCamelCase					) + 1					)
									_snake_case      			=			json.loads(new_tokenizer._tokenizer.to_str()					)
									self.assertIn("huggingface",    json_tokenizer["model"]["vocab"]					)

									# Will need to be adjusted if we reach v42 and this test is still here.
									# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
									shutil.move(os.path.join(__UpperCamelCase,    "tokenizer.4.0.0.json"					),    os.path.join(__UpperCamelCase,    "tokenizer.42.0.0.json"					)					)
									_snake_case      			=			["tokenizer.42.0.0.json"]
									tokenizer.save_pretrained(__UpperCamelCase					)
									_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
									self.assertEqual(len(__UpperCamelCase					),    len(__UpperCamelCase					)					)
									_snake_case      			=			json.loads(new_tokenizer._tokenizer.to_str()					)
									self.assertNotIn("huggingface",    json_tokenizer["model"]["vocab"]					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
							_snake_case      			=			"hf-internal-testing/test-two-tokenizers"

							# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
							_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
							self.assertEqual(len(__UpperCamelCase					),    28_997					)
							_snake_case      			=			json.loads(tokenizer._tokenizer.to_str()					)
							self.assertIn("huggingface",    json_tokenizer["model"]["vocab"]					)

							# Testing an older version by monkey-patching the version in the module it's used.
							import transformers as old_transformers

							_snake_case      			=			"3.0.0"
							_snake_case      			=			old_transformers.models.auto.AutoTokenizer.from_pretrained(__UpperCamelCase					)
							self.assertEqual(len(__UpperCamelCase					),    28_996					)
							_snake_case      			=			json.loads(old_tokenizer._tokenizer.to_str()					)
							self.assertNotIn("huggingface",    json_tokenizer["model"]["vocab"]					)





@require_tokenizers
class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# See https://github.com/huggingface/transformers/pull/12550
							# and https://github.com/huggingface/tokenizers/issues/537
							_snake_case      			=			PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"					)
							_snake_case      			=			"The Matrix is a 1999 science fiction action film."

							with concurrent.futures.ThreadPoolExecutor() as executor:
									_snake_case      			=			[executor.submit(self.fetch,    __UpperCamelCase,    __UpperCamelCase					) for i in range(10					)]
									_snake_case      			=			[future.result() for future in futures]
									self.assertEqual(__UpperCamelCase,    [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10					)]					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							return tokenizer.encode(__UpperCamelCase,    truncation="longest_first",    padding="longest"					)





