
import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    MgpstrTokenizer
   __UpperCamelCase		:    List[str] 				=    False
   __UpperCamelCase		:    Tuple 				=    {}
   __UpperCamelCase		:    List[str] 				=    False


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      super().setUp()

      # fmt: off
      _A:  List[str]					=       ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
      # fmt: on
      _A:  str					=       dict(zip(lowerCAmelCase_			,			range(len(lowerCAmelCase_     )     )     )     )

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


   def  __magic_name__				(					self     :							Optional[Any]			,			**lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      return MgpstrTokenizer.from_pretrained(self.tmpdirname			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A:  List[Any]					=       '''tester'''
      _A:  Union[str, Any]					=       '''tester'''
      return input_text, output_text


   @unittest.skip('''MGP-STR always lower cases letters.'''     )
   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      pass


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Any					=       self.get_tokenizers(do_lower_case=lowerCAmelCase_     )
      for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
            _A:  List[str]					=       '''[SPECIAL_TOKEN]'''

            tokenizer.add_special_tokens({'''cls_token''': special_token}     )
            _A:  Optional[int]					=       tokenizer.encode([special_token]			,			add_special_tokens=lowerCAmelCase_     )
            self.assertEqual(len(lowerCAmelCase_     )			,			1     )

            _A:  Optional[int]					=       tokenizer.decode(lowerCAmelCase_			,			skip_special_tokens=lowerCAmelCase_     )
            self.assertTrue(special_token not in decoded     )


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  int					=       self.get_tokenizers()
      for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
            _A			,  _A:  List[str]					=       self.get_input_output_texts(lowerCAmelCase_     )

            _A:  Dict					=       tokenizer.tokenize(lowerCAmelCase_     )
            _A:  int					=       tokenizer.convert_tokens_to_ids(lowerCAmelCase_     )
            _A:  Tuple					=       tokenizer.encode(lowerCAmelCase_			,			add_special_tokens=lowerCAmelCase_     )
            self.assertListEqual(lowerCAmelCase_			,			lowerCAmelCase_     )

            _A:  Optional[Any]					=       tokenizer.convert_ids_to_tokens(lowerCAmelCase_     )
            self.assertNotEqual(len(lowerCAmelCase_     )			,			0     )
            _A:  List[str]					=       tokenizer.decode(lowerCAmelCase_     )
            self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )

            self.assertEqual(text_a.replace(''' '''			,			''''''     )			,			lowerCAmelCase_     )


   @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''     )
   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      pass


   @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''     )
   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      pass





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp






@slow
@require_flax
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      return F"""gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase_     ) for s in shape]     )}.npy"""


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Any=0			,			lowerCAmelCase_     :							Tuple=(4, 4, 6_4, 6_4)			,			lowerCAmelCase_     :							List[str]=False     ):

      """simple docstring"""
      _A:  List[str]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Optional[int]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return image


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Optional[Any]="CompVis/stable-diffusion-v1-4"     ):

      """simple docstring"""
      _A:  Tuple					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  str					=       '''bf16''' if fpaa else None

      _A			,  _A:  Union[str, Any]					=       FlaxUNetaDConditionModel.from_pretrained(
          lowerCAmelCase_			,			subfolder='''unet'''			,			dtype=lowerCAmelCase_			,			revision=lowerCAmelCase_     )
      return model, params


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							Optional[int]=0			,			lowerCAmelCase_     :							str=(4, 7_7, 7_6_8)			,			lowerCAmelCase_     :							Dict=False     ):

      """simple docstring"""
      _A:  Optional[int]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Union[str, Any]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return hidden_states


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
           [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
           [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Any     ):

      """simple docstring"""
      _A			,  _A:  Optional[Any]					=       self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''			,			fpaa=lowerCAmelCase_     )
      _A:  List[str]					=       self.get_latents(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )
      _A:  Optional[int]					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )

      _A:  List[str]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  Any					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  Tuple					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
           [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
           [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      _A			,  _A:  Union[str, Any]					=       self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_latents(lowerCAmelCase_			,			shape=(4, 4, 9_6, 9_6)			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			shape=(4, 7_7, 1_0_2_4)			,			fpaa=lowerCAmelCase_     )

      _A:  Optional[int]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  List[str]					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  List[str]					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )



