

import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


UpperCamelCase__  :      Any			=    logging.get_logger(__name__)

UpperCamelCase__  :      Any			=    {
    "artists_file": "artists.json",
    "lyrics_file": "lyrics.json",
    "genres_file": "genres.json",
}

UpperCamelCase__  :      int			=    {
    "artists_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json",
    },
    "genres_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json",
    },
    "lyrics_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json",
    },
}

UpperCamelCase__  :      Optional[Any]			=    {
    "jukebox": 512,
}



class       __snake_case  (    a__       ):
  __lowerCAmelCase     :	Dict						 =  VOCAB_FILES_NAMES
  __lowerCAmelCase     :	Optional[int]						 =  PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase     :	Optional[Any]						 =  PRETRAINED_LYRIC_TOKENS_SIZES
  __lowerCAmelCase     :	int						 =  ['input_ids', 'attention_mask']

  def __init__(					self						,		_A						,		_A						,		_A						,		_A=["v3", "v2", "v2"]						,		_A=512						,		_A=5						,		_A="<|endoftext|>"						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						AddedToken(_A						,		lstrip=_A						,		rstrip=_A) if isinstance(_A						,		_A) else unk_token
      super().__init__(
          unk_token=_A						,		n_genres=_A						,		version=_A						,		max_n_lyric_tokens=_A						,		**_A						,		)
      SCREAMING_SNAKE_CASE_									=						version
      SCREAMING_SNAKE_CASE_									=						max_n_lyric_tokens
      SCREAMING_SNAKE_CASE_									=						n_genres

      with open(_A						,		encoding='utf-8') as vocab_handle:
          SCREAMING_SNAKE_CASE_									=						json.load(_A)

      with open(_A						,		encoding='utf-8') as vocab_handle:
          SCREAMING_SNAKE_CASE_									=						json.load(_A)

      with open(_A						,		encoding='utf-8') as vocab_handle:
          SCREAMING_SNAKE_CASE_									=						json.load(_A)

      SCREAMING_SNAKE_CASE_									=						r'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
      # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
      if len(self.lyrics_encoder) == 79:
          SCREAMING_SNAKE_CASE_									=						oov.replace(r'\-\''						,		r'\-+\'')

      SCREAMING_SNAKE_CASE_									=						regex.compile(_A)
      SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.artists_encoder.items()}
      SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.genres_encoder.items()}
      SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.lyrics_encoder.items()}

  @property
  def 						lowerCAmelCase__     (					self):
      return len(self.artists_encoder) + len(self.genres_encoder) + len(self.lyrics_encoder)

  def 						lowerCAmelCase__     (					self):
      return dict(self.artists_encoder						,		self.genres_encoder						,		self.lyrics_encoder)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						[self.artists_encoder.get(_A						,		0) for artist in list_artists]
      for genres in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						[self.genres_encoder.get(_A						,		0) for genre in list_genres[genres]]
          SCREAMING_SNAKE_CASE_									=						list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]))

      SCREAMING_SNAKE_CASE_									=						[[self.lyrics_encoder.get(_A						,		0) for character in list_lyrics[0]], [], []]
      return artists_id, list_genres, lyric_ids

  def 						lowerCAmelCase__     (					self						,		_A):
      return list(_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		**_A):
      SCREAMING_SNAKE_CASE_									=						self.prepare_for_tokenization(_A						,		_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						self._tokenize(_A)
      return artist, genre, lyrics

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A = False):

      for idx in range(len(self.version)):
          if self.version[idx] == "v3":
              SCREAMING_SNAKE_CASE_									=						artists[idx].lower()
              SCREAMING_SNAKE_CASE_									=						[genres[idx].lower()]
          else:
              SCREAMING_SNAKE_CASE_									=						self._normalize(artists[idx]) + '.v2'
              SCREAMING_SNAKE_CASE_									=						[
                  self._normalize(_A) + '.v2' for genre in genres[idx].split('_')
              ]  # split is for the full dictionary with combined genres

      if self.version[0] == "v2":
          SCREAMING_SNAKE_CASE_									=						regex.compile(r'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+')
          SCREAMING_SNAKE_CASE_									=						'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
          SCREAMING_SNAKE_CASE_									=						{vocab[index]: index + 1 for index in range(len(_A))}
          SCREAMING_SNAKE_CASE_									=						0
          SCREAMING_SNAKE_CASE_									=						len(_A) + 1
          SCREAMING_SNAKE_CASE_									=						self.vocab
          SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.vocab.items()}
          SCREAMING_SNAKE_CASE_									=						''
      else:
          SCREAMING_SNAKE_CASE_									=						regex.compile(r'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+')

      SCREAMING_SNAKE_CASE_									=						self._run_strip_accents(_A)
      SCREAMING_SNAKE_CASE_									=						lyrics.replace('\\'						,		'\n')
      SCREAMING_SNAKE_CASE_									=						self.out_of_vocab.sub(''						,		_A), [], []
      return artists, genres, lyrics

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						unicodedata.normalize('NFD'						,		_A)
      SCREAMING_SNAKE_CASE_									=						[]
      for char in text:
          SCREAMING_SNAKE_CASE_									=						unicodedata.category(_A)
          if cat == "Mn":
              continue
          output.append(_A)
      return "".join(_A)

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						(
          [chr(_A) for i in range(ord('a')						,		ord('z') + 1)]
          + [chr(_A) for i in range(ord('A')						,		ord('Z') + 1)]
          + [chr(_A) for i in range(ord('0')						,		ord('9') + 1)]
          + ['.']
      )
      SCREAMING_SNAKE_CASE_									=						frozenset(_A)
      SCREAMING_SNAKE_CASE_									=						re.compile(r'_+')
      SCREAMING_SNAKE_CASE_									=						''.join([c if c in accepted else '_' for c in text.lower()])
      SCREAMING_SNAKE_CASE_									=						pattern.sub('_'						,		_A).strip('_')
      return text

  def 						lowerCAmelCase__     (					self						,		_A):
      return " ".join(_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = False):

      if not isinstance(_A						,		_A):
          SCREAMING_SNAKE_CASE_									=						TensorType(_A)

      # Get a function reference for the correct framework
      if tensor_type == TensorType.TENSORFLOW:
          if not is_tf_available():
              raise ImportError(
                  'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.')
          import tensorflow as tf

          SCREAMING_SNAKE_CASE_									=						tf.constant
          SCREAMING_SNAKE_CASE_									=						tf.is_tensor
      elif tensor_type == TensorType.PYTORCH:
          if not is_torch_available():
              raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.')
          import torch

          SCREAMING_SNAKE_CASE_									=						torch.tensor
          SCREAMING_SNAKE_CASE_									=						torch.is_tensor
      elif tensor_type == TensorType.JAX:
          if not is_flax_available():
              raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.')
          import jax.numpy as jnp  # noqa: F811

          SCREAMING_SNAKE_CASE_									=						jnp.array
          SCREAMING_SNAKE_CASE_									=						_is_jax
      else:
          SCREAMING_SNAKE_CASE_									=						np.asarray
          SCREAMING_SNAKE_CASE_									=						_is_numpy

      # Do the tensor conversion in batch

      try:
          if prepend_batch_axis:
              SCREAMING_SNAKE_CASE_									=						[inputs]

          if not is_tensor(_A):
              SCREAMING_SNAKE_CASE_									=						as_tensor(_A)
      except:  # noqa E722
          raise ValueError(
              'Unable to create tensor, you should probably activate truncation and/or padding '
              'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.')

      return inputs

  def __call__(					self						,		_A						,		_A						,		_A=""						,		_A="pt"):
      SCREAMING_SNAKE_CASE_									=						[0, 0, 0]
      SCREAMING_SNAKE_CASE_									=						[artist] * len(self.version)
      SCREAMING_SNAKE_CASE_									=						[genres] * len(self.version)

      SCREAMING_SNAKE_CASE_									=						self.tokenize(_A						,		_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						self._convert_token_to_id(_A						,		_A						,		_A)

      SCREAMING_SNAKE_CASE_									=						[-INFINITY] * len(full_tokens[-1])
      SCREAMING_SNAKE_CASE_									=						[
          self.convert_to_tensors(
              [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]						,		tensor_type=_A)
          for i in range(len(self.version))
      ]
      return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks})

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):

      if not os.path.isdir(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return

      SCREAMING_SNAKE_CASE_									=						os.path.join(
          _A						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file'])
      with open(_A						,		'w'						,		encoding='utf-8') as f:
          f.write(json.dumps(self.artists_encoder						,		ensure_ascii=_A))

      SCREAMING_SNAKE_CASE_									=						os.path.join(
          _A						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file'])
      with open(_A						,		'w'						,		encoding='utf-8') as f:
          f.write(json.dumps(self.genres_encoder						,		ensure_ascii=_A))

      SCREAMING_SNAKE_CASE_									=						os.path.join(
          _A						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file'])
      with open(_A						,		'w'						,		encoding='utf-8') as f:
          f.write(json.dumps(self.lyrics_encoder						,		ensure_ascii=_A))

      return (artists_file, genres_file, lyrics_file)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						self.artists_decoder.get(_A)
      SCREAMING_SNAKE_CASE_									=						[self.genres_decoder.get(_A) for genre in genres_index]
      SCREAMING_SNAKE_CASE_									=						[self.lyrics_decoder.get(_A) for character in lyric_index]
      return artist, genres, lyrics





import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class       __snake_case  :

  def __init__(					self						,		_A						,		_A=99						,		_A=13						,		_A=16						,		_A=7						,		_A=True						,		_A=True						,		_A=True						,		_A=False						,		_A=True						,		_A=2						,		_A=32						,		_A=4						,		_A=4						,		_A=30						,		_A=0						,		_A=1						,		_A=2						,		_A=None						,		):
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						batch_size
      SCREAMING_SNAKE_CASE_									=						decoder_seq_length
      # For common tests
      SCREAMING_SNAKE_CASE_									=						self.decoder_seq_length
      SCREAMING_SNAKE_CASE_									=						is_training
      SCREAMING_SNAKE_CASE_									=						use_attention_mask
      SCREAMING_SNAKE_CASE_									=						use_labels

      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						d_model
      SCREAMING_SNAKE_CASE_									=						d_model
      SCREAMING_SNAKE_CASE_									=						decoder_layers
      SCREAMING_SNAKE_CASE_									=						decoder_layers
      SCREAMING_SNAKE_CASE_									=						decoder_ffn_dim
      SCREAMING_SNAKE_CASE_									=						decoder_attention_heads
      SCREAMING_SNAKE_CASE_									=						decoder_attention_heads
      SCREAMING_SNAKE_CASE_									=						eos_token_id
      SCREAMING_SNAKE_CASE_									=						bos_token_id
      SCREAMING_SNAKE_CASE_									=						pad_token_id
      SCREAMING_SNAKE_CASE_									=						decoder_start_token_id
      SCREAMING_SNAKE_CASE_									=						use_cache
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings

      SCREAMING_SNAKE_CASE_									=						None
      SCREAMING_SNAKE_CASE_									=						decoder_seq_length
      SCREAMING_SNAKE_CASE_									=						2
      SCREAMING_SNAKE_CASE_									=						1

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.decoder_seq_length]						,		self.vocab_size)

      SCREAMING_SNAKE_CASE_									=						None
      if self.use_attention_mask:
          SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.decoder_seq_length]						,		vocab_size=2)

      SCREAMING_SNAKE_CASE_									=						None
      if self.use_labels:
          SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.decoder_seq_length]						,		self.vocab_size)

      SCREAMING_SNAKE_CASE_									=						TrOCRConfig(
          vocab_size=self.vocab_size						,		d_model=self.d_model						,		decoder_layers=self.decoder_layers						,		decoder_ffn_dim=self.decoder_ffn_dim						,		decoder_attention_heads=self.decoder_attention_heads						,		eos_token_id=self.eos_token_id						,		bos_token_id=self.bos_token_id						,		use_cache=self.use_cache						,		pad_token_id=self.pad_token_id						,		decoder_start_token_id=self.decoder_start_token_id						,		max_position_embeddings=self.max_position_embeddings						,		)

      return (config, input_ids, attention_mask, lm_labels)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		):
      SCREAMING_SNAKE_CASE_									=						True
      SCREAMING_SNAKE_CASE_									=						TrOCRDecoder(config=_A).to(_A).eval()
      SCREAMING_SNAKE_CASE_									=						input_ids[:2]

      input_ids[input_ids == 0] += 1
      # first forward pass
      SCREAMING_SNAKE_CASE_									=						model(_A						,		use_cache=_A)
      SCREAMING_SNAKE_CASE_									=						model(_A)
      SCREAMING_SNAKE_CASE_									=						model(_A						,		use_cache=_A)

      self.parent.assertTrue(len(_A) == len(_A))
      self.parent.assertTrue(len(_A) == len(_A) + 1)

      SCREAMING_SNAKE_CASE_									=						outputs['past_key_values']

      # create hypothetical next token and extent to next_input_ids
      SCREAMING_SNAKE_CASE_									=						ids_tensor((2, 1)						,		config.vocab_size - 1) + 1

      # append to next input_ids and
      SCREAMING_SNAKE_CASE_									=						torch.cat([input_ids, next_tokens]						,		dim=-1)

      SCREAMING_SNAKE_CASE_									=						model(_A)['last_hidden_state']
      SCREAMING_SNAKE_CASE_									=						model(_A						,		past_key_values=_A)['last_hidden_state']

      # select random slice
      SCREAMING_SNAKE_CASE_									=						ids_tensor((1,)						,		output_from_past.shape[-1]).item()
      SCREAMING_SNAKE_CASE_									=						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
      SCREAMING_SNAKE_CASE_									=						output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      assert torch.allclose(_A						,		_A						,		atol=1E-3)


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						config_and_inputs

      SCREAMING_SNAKE_CASE_									=						{'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict






@require_torch
class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Tuple						 =  (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
  __lowerCAmelCase     :	Union[str, Any]						 =  (TrOCRForCausalLM,) if is_torch_available() else ()
  __lowerCAmelCase     :	str						 =  {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
  __lowerCAmelCase     :	Any						 =  True
  __lowerCAmelCase     :	str						 =  False

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						TrOCRStandaloneDecoderModelTester(self						,		is_training=_A)
      SCREAMING_SNAKE_CASE_									=						ConfigTester(self						,		config_class=_A)

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      self.config_tester.run_common_tests()

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past(*_A)

  def 						lowerCAmelCase__     (					self):
      return


  @unittest.skip('The model doesn\'t support left padding')  # and it's not used enough to be worth fixing :)
  def 						lowerCAmelCase__     (					self):
      pass


