






"""simple docstring"""






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  UpperCAmelCase_     :				Tuple         =				"""pt"""
elif is_tf_available():
  UpperCAmelCase_     :				Optional[Any]         =				"""tf"""
else:
  UpperCAmelCase_     :				str         =				"""jax"""



class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						ByTaTokenizer
      __UpperCamelCase							=						False
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          super().setUp()
          SCREAMING_SNAKE_CASE_   :						Dict							=	ByTaTokenizer()
          tokenizer.save_pretrained(self.tmpdirname)
      @cached_property
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          return ByTaTokenizer.from_pretrained('''google/byt5-small''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			**lowercase_  :						Tuple):




          '''simple docstring'''


          return self.tokenizer_class.from_pretrained(self.tmpdirname		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Optional[Any]		,			lowercase_  :						List[Any]=False		,			lowercase_  :						Tuple=20		,			lowercase_  :						List[str]=5):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
          for i in range(len(lowercase_)):
              try:
                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.decode([i]		,			clean_up_tokenization_spaces=lowercase_)
              except UnicodeDecodeError:
                  pass
              toks.append((i, tok))

          SCREAMING_SNAKE_CASE_   :						Dict							=	list(filter(lambda lowercase_: re.match(r'''^[ a-zA-Z]+$'''		,			t[1])		,			lowercase_))
          SCREAMING_SNAKE_CASE_   :						Any							=	list(filter(lambda lowercase_: [t[0]] == tokenizer.encode(t[1]		,			add_special_tokens=lowercase_)		,			lowercase_))
          if max_length is not None and len(lowercase_) > max_length:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	toks[:max_length]
          if min_length is not None and len(lowercase_) < min_length and len(lowercase_) > 0:
              while len(lowercase_) < min_length:
                  SCREAMING_SNAKE_CASE_   :						int							=	toks + toks
        # toks_str = [t[1] for t in toks]
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[t[0] for t in toks]

          # Ensure consistency
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer.decode(lowercase_		,			clean_up_tokenization_spaces=lowercase_)
          if " " not in output_txt and len(lowercase_) > 1:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	(
                  tokenizer.decode([toks_ids[0]]		,			clean_up_tokenization_spaces=lowercase_)
                  + ''' '''
                  + tokenizer.decode(toks_ids[1:]		,			clean_up_tokenization_spaces=lowercase_)
              )
          if with_prefix_space:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	''' ''' + output_txt
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
          return output_txt, output_ids
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''])
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(['''hi''', '''I went to the gym''', ''''''])
          self.assertListEqual(batch_with_eos_added['''input_ids''']		,			batch_without_eos_added['''input_ids'''])
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Any							=	'''Unicode €.'''
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)

          # decoding
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''Unicode €.</s>''')

          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer('''e è é ê ë''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)
          # decoding
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''e è é ê ë</s>''')

          # encode/decode, but with `encode` instead of `__call__`
          self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))		,			'''e è é ê ë</s>''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Dict							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
          # fmt: on
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer(lowercase_		,			padding=lowercase_		,			return_tensors=lowercase_)
          self.assertIsInstance(lowercase_		,			lowercase_)

          if FRAMEWORK != "jax":
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	list(batch.input_ids.numpy()[0])
          else:
              SCREAMING_SNAKE_CASE_   :						int							=	list(batch.input_ids.tolist()[0])

          self.assertListEqual(lowercase_		,			lowercase_)

          self.assertEqual((2, 37)		,			batch.input_ids.shape)
          self.assertEqual((2, 37)		,			batch.attention_mask.shape)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(lowercase_		,			padding=lowercase_		,			return_tensors=lowercase_)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn('''input_ids'''		,			lowercase_)
          self.assertIn('''attention_mask'''		,			lowercase_)
          self.assertNotIn('''decoder_input_ids'''		,			lowercase_)
          self.assertNotIn('''decoder_attention_mask'''		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						List[str]							=	[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer(
              text_target=lowercase_		,			max_length=32		,			padding='''max_length'''		,			truncation=lowercase_		,			return_tensors=lowercase_)
          self.assertEqual(32		,			targets['''input_ids'''].shape[1])
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	['''A long paragraph for summarization. </s>''']
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''Summary of the text. </s>''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
          SCREAMING_SNAKE_CASE_   :						Tuple							=	[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
          # fmt: on

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(lowercase_		,			text_target=lowercase_)

          self.assertEqual(lowercase_		,			batch['''input_ids'''][0])
          self.assertEqual(lowercase_		,			batch['''labels'''][0])
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  self.assertNotEqual(tokenizer.model_max_length		,			42)

        # Now let's start the test
          SCREAMING_SNAKE_CASE_   :						str							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	''' He is very happy, UNwant\u00E9d,running'''
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  tokenizer.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	after_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  self.assertListEqual(lowercase_		,			lowercase_)

                  shutil.rmtree(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	self.get_tokenizers(model_max_length=42)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_   :						Any							=	''' He is very happy, UNwant\u00E9d,running'''
                  tokenizer.add_tokens(['''bim''', '''bambam'''])
                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer.additional_special_tokens
                  additional_special_tokens.append('''new_additional_special_token''')
                  tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens})
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  tokenizer.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	after_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  self.assertListEqual(lowercase_		,			lowercase_)
                  self.assertIn('''new_additional_special_token'''		,			after_tokenizer.additional_special_tokens)
                  self.assertEqual(after_tokenizer.model_max_length		,			42)

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.__class__.from_pretrained(lowercase_		,			model_max_length=43)
                  self.assertEqual(tokenizer.model_max_length		,			43)

                  shutil.rmtree(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(lowercase_)

                  with open(os.path.join(lowercase_		,			'''special_tokens_map.json''')		,			encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_   :						List[Any]							=	json.load(lowercase_)

                  with open(os.path.join(lowercase_		,			'''tokenizer_config.json''')		,			encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_   :						Tuple							=	json.load(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	[F'<extra_id_{i}>' for i in range(125)]

                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]
                  SCREAMING_SNAKE_CASE_   :						int							=	added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]

                  with open(os.path.join(lowercase_		,			'''special_tokens_map.json''')		,			'''w'''		,			encoding='''utf-8''') as outfile:
                      json.dump(lowercase_		,			lowercase_)
                  with open(os.path.join(lowercase_		,			'''tokenizer_config.json''')		,			'''w'''		,			encoding='''utf-8''') as outfile:
                      json.dump(lowercase_		,			lowercase_)

                  # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                  # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                  # "special_tokens_map.json" files
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer_class.from_pretrained(
                      lowercase_		,			)
                  self.assertIn(
                      '''an_additional_special_token'''		,			tokenizer_without_change_in_init.additional_special_tokens)
                  # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                  self.assertEqual(
                      ['''an_additional_special_token''']		,			tokenizer_without_change_in_init.convert_ids_to_tokens(
                          tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']))		,			)

                  # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''		,			lstrip=lowercase_)]
                  SCREAMING_SNAKE_CASE_   :						int							=	tokenizer_class.from_pretrained(
                      lowercase_		,			additional_special_tokens=lowercase_		,			)

                  self.assertIn('''a_new_additional_special_token'''		,			tokenizer.additional_special_tokens)
                  self.assertEqual(
                      ['''a_new_additional_special_token''']		,			tokenizer.convert_ids_to_tokens(
                          tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']))		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer_class.from_pretrained(lowercase_)

                  self.assertTrue(tokenizer.decode([255]) == '''''')
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.get_tokenizers(fast=lowercase_		,			do_lower_case=lowercase_)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						Any							=	['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.convert_tokens_to_string(lowercase_)

                  self.assertIsInstance(lowercase_		,			lowercase_)



      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						int							=	[
                      '''bos_token''',
                      '''eos_token''',
                      '''unk_token''',
                      '''sep_token''',
                      '''pad_token''',
                      '''cls_token''',
                      '''mask_token''',
                  ]

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	0
                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer.convert_ids_to_tokens(
                      lowercase_		,			skip_special_tokens=lowercase_)

                  for attr in attributes_list:
                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[])

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[token_id_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[token_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[token_id_to_test_setters])










"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
  import jax.numpy as jnp

if is_tf_available():
  import tensorflow as tf

if is_torch_available():
  import torch




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	{
              '''task_specific_params''': {
                  '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
                  '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
                  '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
              }
          }
          SCREAMING_SNAKE_CASE_   :						List[str]							=	{
              '''task_specific_params.summarization.length_penalty''': 1.0,
              '''task_specific_params.summarization.max_length''': 128,
              '''task_specific_params.summarization.min_length''': 12,
              '''task_specific_params.summarization.num_beams''': 4,
              '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
              '''task_specific_params.summarization_cnn.max_length''': 142,
              '''task_specific_params.summarization_cnn.min_length''': 56,
              '''task_specific_params.summarization_cnn.num_beams''': 4,
              '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
              '''task_specific_params.summarization_xsum.max_length''': 62,
              '''task_specific_params.summarization_xsum.min_length''': 11,
              '''task_specific_params.summarization_xsum.num_beams''': 6,
          }

          self.assertEqual(flatten_dict(lowercase_)		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			x.transpose()))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4		,			5)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			x.transpose((1, 2, 0))))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						str							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Dict							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			transpose(lowercase_		,			axes=(1, 2, 0)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						str							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						int							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			transpose(lowercase_		,			axes=(1, 2, 0)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						Any							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			np.asarray(transpose(lowercase_))))

          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Any							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			np.asarray(transpose(lowercase_		,			axes=(1, 2, 0)))))
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			np.reshape(lowercase_		,			(4, 3))))

          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4		,			5)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			np.reshape(lowercase_		,			(12, 5))))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			reshape(lowercase_		,			(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			reshape(lowercase_		,			(12, 5)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			reshape(lowercase_		,			(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			reshape(lowercase_		,			(12, 5)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			np.asarray(reshape(lowercase_		,			(4, 3)))))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			np.asarray(reshape(lowercase_		,			(12, 5)))))
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(1		,			3		,			4)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			np.squeeze(lowercase_)))

          SCREAMING_SNAKE_CASE_   :						str							=	np.random.randn(1		,			4		,			1		,			5)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			np.squeeze(lowercase_		,			axis=2)))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						str							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			squeeze(lowercase_		,			axis=2).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						Dict							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						Any							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			squeeze(lowercase_		,			axis=2).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			np.asarray(squeeze(lowercase_))))

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						str							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			np.asarray(squeeze(lowercase_		,			axis=2))))
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			np.expand_dims(lowercase_		,			axis=1)))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			expand_dims(lowercase_		,			axis=1).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						Any							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			expand_dims(lowercase_		,			axis=1).numpy()))



      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			np.asarray(expand_dims(lowercase_		,			axis=1))))


