
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class        lowercase      (	_UpperCAmelCase						,     unittest.TestCase     ):
  __lowercase : List[Any]			  =       LEDTokenizer
  __lowercase : Dict			  =       LEDTokenizerFast
  __lowercase : int			  =       True



  def __UpperCamelCase				(				self )						->      List[Any]:
    """simple docstring"""
    super().setUp()
    UpperCamelCase						=  [
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''\u0120''',
        '''\u0120l''',
        '''\u0120n''',
        '''\u0120lo''',
        '''\u0120low''',
        '''er''',
        '''\u0120lowest''',
        '''\u0120newer''',
        '''\u0120wider''',
        '''<unk>''',
    ]
    UpperCamelCase						=  dict(zip(lowerCamelCase_       ,   range(len(lowerCamelCase_ ) ) ) )
    UpperCamelCase						=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
    UpperCamelCase						=  {'''unk_token''': '''<unk>'''}

    UpperCamelCase						=  os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file'] )
    UpperCamelCase						=  os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['merges_file'] )
    with open(self.vocab_file       ,   'w'       ,   encoding='utf-8' ) as fp:
      fp.write(json.dumps(lowerCamelCase_ ) + '\n' )
    with open(self.merges_file       ,   'w'       ,   encoding='utf-8' ) as fp:
      fp.write('\n'.join(lowerCamelCase_ ) )



  def __UpperCamelCase				(				self       ,   **A_ )						->      List[Any]:
    """simple docstring"""
    kwargs.update(self.special_tokens_map )
    return self.tokenizer_class.from_pretrained(self.tmpdirname       ,   **lowerCamelCase_ )



  def __UpperCamelCase				(				self       ,   **A_ )						->      Union[str, Any]:
    """simple docstring"""
    kwargs.update(self.special_tokens_map )
    return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,   **lowerCamelCase_ )



  def __UpperCamelCase				(				self       ,   A_ )						->      Optional[int]:
    """simple docstring"""
    return "lower newer", "lower newer"



  @cached_property
  def __UpperCamelCase				(				self )						->      int:
    """simple docstring"""
    return LEDTokenizer.from_pretrained('allenai/led-base-16384' )



  @cached_property
  def __UpperCamelCase				(				self )						->      str:
    """simple docstring"""
    return LEDTokenizerFast.from_pretrained('allenai/led-base-16384' )



  @require_torch
  def __UpperCamelCase				(				self )						->      Tuple:
    """simple docstring"""
    UpperCamelCase						=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
    UpperCamelCase						=  [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      UpperCamelCase						=  tokenizer(lowerCamelCase_       ,   max_length=len(lowerCamelCase_ )       ,   padding=lowerCamelCase_       ,   return_tensors='pt' )
      self.assertIsInstance(lowerCamelCase_       ,   lowerCamelCase_ )

      self.assertEqual((2, 9)       ,   batch.input_ids.shape )
      self.assertEqual((2, 9)       ,   batch.attention_mask.shape )
      UpperCamelCase						=  batch.input_ids.tolist()[0]
      self.assertListEqual(lowerCamelCase_       ,   lowerCamelCase_ )



  @require_torch
  def __UpperCamelCase				(				self )						->      Any:
    """simple docstring"""
    UpperCamelCase						=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      UpperCamelCase						=  tokenizer(lowerCamelCase_       ,   padding=lowerCamelCase_       ,   return_tensors='pt' )
      self.assertIn('input_ids'       ,   lowerCamelCase_ )
      self.assertIn('attention_mask'       ,   lowerCamelCase_ )
      self.assertNotIn('labels'       ,   lowerCamelCase_ )
      self.assertNotIn('decoder_attention_mask'       ,   lowerCamelCase_ )



  @require_torch
  def __UpperCamelCase				(				self )						->      Optional[int]:
    """simple docstring"""
    UpperCamelCase						=  [
        '''Summary of the text.''',
        '''Another summary.''',
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      UpperCamelCase						=  tokenizer(text_target=lowerCamelCase_       ,   max_length=32       ,   padding='max_length'       ,   return_tensors='pt' )
      self.assertEqual(32       ,   targets['input_ids'].shape[1] )



  @require_torch
  def __UpperCamelCase				(				self )						->      Optional[int]:
    """simple docstring"""




    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      UpperCamelCase						=  tokenizer(
          ['I am a small frog' * 1_024, 'I am a small frog']       ,   padding=lowerCamelCase_       ,   truncation=lowerCamelCase_       ,   return_tensors='pt' )
      self.assertIsInstance(lowerCamelCase_       ,   lowerCamelCase_ )
      self.assertEqual(batch.input_ids.shape       ,   (2, 5_122) )



  @require_torch
  def __UpperCamelCase				(				self )						->      List[str]:
    """simple docstring"""
    UpperCamelCase						=  ['''A long paragraph for summarization.''']
    UpperCamelCase						=  [
        '''Summary of the text.''',
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      UpperCamelCase						=  tokenizer(lowerCamelCase_       ,   return_tensors='pt' )
      UpperCamelCase						=  tokenizer(text_target=lowerCamelCase_       ,   return_tensors='pt' )
      UpperCamelCase						=  inputs['''input_ids''']
      UpperCamelCase						=  targets['''input_ids''']
      self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
      self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
      self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
      self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )



  @require_torch
  def __UpperCamelCase				(				self )						->      Any:
    """simple docstring"""




    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      UpperCamelCase						=  ['''Summary of the text.''', '''Another summary.''']
      UpperCamelCase						=  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

      UpperCamelCase						=  tokenizer(lowerCamelCase_       ,   padding=lowerCamelCase_ )
      UpperCamelCase						=  [[0] * len(lowerCamelCase_ ) for x in encoded_output['''input_ids''']]
      UpperCamelCase						=  tokenizer.pad(lowerCamelCase_ )
      self.assertSequenceEqual(outputs['global_attention_mask']       ,   lowerCamelCase_ )



  def __UpperCamelCase				(				self )						->      Dict:
    """simple docstring"""
    pass



  def __UpperCamelCase				(				self )						->      Optional[int]:
    """simple docstring"""




    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
      with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
        UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(lowerCamelCase_       ,   **lowerCamelCase_ )
        UpperCamelCase						=  self.tokenizer_class.from_pretrained(lowerCamelCase_       ,   **lowerCamelCase_ )
        UpperCamelCase						=  '''A, <mask> AllenNLP sentence.'''
        UpperCamelCase						=  tokenizer_r.encode_plus(lowerCamelCase_       ,   add_special_tokens=lowerCamelCase_       ,   return_token_type_ids=lowerCamelCase_ )
        UpperCamelCase						=  tokenizer_p.encode_plus(lowerCamelCase_       ,   add_special_tokens=lowerCamelCase_       ,   return_token_type_ids=lowerCamelCase_ )
        self.assertEqual(sum(tokens_r['token_type_ids'] )       ,   sum(tokens_p['token_type_ids'] ) )
        self.assertEqual(
            sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )       ,   sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )       ,   )

        UpperCamelCase						=  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
        UpperCamelCase						=  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
        self.assertSequenceEqual(tokens_p['input_ids']       ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
        self.assertSequenceEqual(tokens_r['input_ids']       ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

        self.assertSequenceEqual(
            lowerCamelCase_       ,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
        self.assertSequenceEqual(
            lowerCamelCase_       ,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )


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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE     ):



		@register_to_config
		def __init__(				self       ,   A_       ,   A_ = None       ,   A_ = None )						->      Any:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase						=  learnable

				if self.learnable:
						assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
						assert length is not None, "learnable=True requires `length` to be set"

						UpperCamelCase						=  torch.zeros(A_       ,   A_ )
				else:
						UpperCamelCase						=  None

				UpperCamelCase						=  torch.nn.Parameter(A_ )






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : VQModel
		__lowercase : CLIPTextModel
		__lowercase : CLIPTokenizer
		__lowercase : TransformeraDModel
		__lowercase : LearnedClassifierFreeSamplingEmbeddings
		__lowercase : VQDiffusionScheduler



		def __init__(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				super().__init__()

				self.register_modules(
				    vqvae=A_       ,   transformer=A_       ,   text_encoder=A_       ,   tokenizer=A_       ,   scheduler=A_       ,   learned_classifier_free_sampling_embeddings=A_       ,   )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  len(A_ ) if isinstance(A_       ,   A_ ) else 1

				# get prompt text embeddings
				UpperCamelCase						=  self.tokenizer(
				    A_       ,   padding='max_length'       ,   max_length=self.tokenizer.model_max_length       ,   return_tensors='pt'       ,   )
				UpperCamelCase						=  text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						UpperCamelCase						=  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}''' )
						UpperCamelCase						=  text_input_ids[:, : self.tokenizer.model_max_length]
				UpperCamelCase						=  self.text_encoder(text_input_ids.to(self.device ) )[0]

				# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
				# While CLIP does normalize the pooled output of the text transformer when combining
				# the image and text embeddings, CLIP does not directly normalize the last hidden state.
				#
				# CLIP normalizing the pooled output.
				# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
				UpperCamelCase						=  prompt_embeds / prompt_embeds.norm(dim=-1       ,   keepdim=A_ )

				# duplicate text embeddings for each generation per prompt
				UpperCamelCase						=  prompt_embeds.repeat_interleave(A_       ,   dim=0 )

				if do_classifier_free_guidance:
						if self.learned_classifier_free_sampling_embeddings.learnable:
								UpperCamelCase						=  self.learned_classifier_free_sampling_embeddings.embeddings
								UpperCamelCase						=  negative_prompt_embeds.unsqueeze(0 ).repeat(A_       ,   1       ,   1 )
						else:
								UpperCamelCase						=  [''] * batch_size

								UpperCamelCase						=  text_input_ids.shape[-1]
								UpperCamelCase						=  self.tokenizer(
								    A_       ,   padding='max_length'       ,   max_length=A_       ,   truncation=A_       ,   return_tensors='pt'       ,   )
								UpperCamelCase						=  self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
								# See comment for normalizing text embeddings
								UpperCamelCase						=  negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1       ,   keepdim=A_ )

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						UpperCamelCase						=  negative_prompt_embeds.shape[1]
						UpperCamelCase						=  negative_prompt_embeds.repeat(1       ,   A_       ,   1 )
						UpperCamelCase						=  negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,   A_       ,   -1 )

						# 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
						UpperCamelCase						=  torch.cat([negative_prompt_embeds, prompt_embeds] )

				return prompt_embeds



		@torch.no_grad()
		def __call__(				self       ,   A_       ,   A_ = 100       ,   A_ = 5.0       ,   A_ = 1.0       ,   A_ = 1       ,   A_ = None       ,   A_ = None       ,   A_ = "pil"       ,   A_ = True       ,   A_ = None       ,   A_ = 1       ,   )						->      Union[ImagePipelineOutput, Tuple]:
				"""simple docstring"""
				if isinstance(A_       ,   A_ ):
						UpperCamelCase						=  1
				elif isinstance(A_       ,   A_ ):
						UpperCamelCase						=  len(A_ )
				else:
						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_ )}''' )

				UpperCamelCase						=  batch_size * num_images_per_prompt

				UpperCamelCase						=  guidance_scale > 1.0

				UpperCamelCase						=  self._encode_prompt(A_       ,   A_       ,   A_ )

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(A_       ,   A_ ) or callback_steps <= 0)
				):
						raise ValueError(
						    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
						    F''' {type(A_ )}.''' )

				# get the initial completely masked latents unless the user supplied it

				UpperCamelCase						=  (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
						UpperCamelCase						=  self.transformer.num_vector_embeds - 1
						UpperCamelCase						=  torch.full(A_       ,   A_ ).to(self.device )
				else:
						if latents.shape != latents_shape:
								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
						if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
								raise ValueError(
								    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
								    F''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
						UpperCamelCase						=  latents.to(self.device )

				# set timesteps
				self.scheduler.set_timesteps(A_       ,   device=self.device )

				UpperCamelCase						=  self.scheduler.timesteps.to(self.device )

				UpperCamelCase						=  latents

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

						# predict the un-noised image
						# model_output == `log_p_x_0`
						UpperCamelCase						=  self.transformer(A_       ,   encoder_hidden_states=A_       ,   timestep=A_ ).sample

						if do_classifier_free_guidance:
								UpperCamelCase			,						UpperCamelCase						=  model_output.chunk(2 )
								UpperCamelCase						=  model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
								model_output -= torch.logsumexp(A_       ,   dim=1       ,   keepdim=A_ )

						UpperCamelCase						=  self.truncate(A_       ,   A_ )

						# remove `log(0)`'s (`-inf`s)
						UpperCamelCase						=  model_output.clamp(-70 )

						# compute the previous noisy sample x_t -> x_t-1
						UpperCamelCase						=  self.scheduler.step(A_       ,   timestep=A_       ,   sample=A_       ,   generator=A_ ).prev_sample

						# call the callback, if provided
						if callback is not None and i % callback_steps == 0:
								callback(A_       ,   A_       ,   A_ )

				UpperCamelCase						=  self.vqvae.config.vq_embed_dim
				UpperCamelCase						=  (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				UpperCamelCase						=  self.vqvae.quantize.get_codebook_entry(A_       ,   shape=A_ )
				UpperCamelCase						=  self.vqvae.decode(A_       ,   force_not_quantize=A_ ).sample

				UpperCamelCase						=  (image / 2 + 0.5).clamp(0       ,   1 )
				UpperCamelCase						=  image.cpu().permute(0       ,   2       ,   3       ,   1 ).numpy()

				if output_type == "pil":
						UpperCamelCase						=  self.numpy_to_pil(A_ )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      torch.FloatTensor:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  torch.sort(A_       ,   1       ,   descending=A_ )
				UpperCamelCase						=  torch.exp(A_ )
				UpperCamelCase						=  sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

				# Ensure that at least the largest probability is not zeroed out
				UpperCamelCase						=  torch.full_like(keep_mask[:, 0:1, :]       ,   A_ )
				UpperCamelCase						=  torch.cat((all_true, keep_mask)       ,   dim=1 )
				UpperCamelCase						=  keep_mask[:, :-1, :]

				UpperCamelCase						=  keep_mask.gather(1       ,   indices.argsort(1 ) )

				UpperCamelCase						=  log_p_x_0.clone()

				UpperCamelCase						=  -torch.inf  # -inf = log(0)

				return rv
