
'''simple docstring'''
from typing import List

import numpy as np




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							int:


		'''simple docstring'''


		snake_case  :       List[Any]     			=						{key: len(SCREAMING_SNAKE_CASE__			) for key, value in gen_kwargs.items() if isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)}
		if len(set(lists_lengths.values()			)			) > 1:
				raise RuntimeError(
				    (
				        '''Sharding is ambiguous for this dataset: '''
				        + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
				        + '''\n'''.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items()			)
				        + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
				        + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
				    )			)
		snake_case  :       List[Any]     			=						max(lists_lengths.values() ,       default=0			)
		return max(1 ,       SCREAMING_SNAKE_CASE__			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[range]:


		'''simple docstring'''


		snake_case  :       Dict     			=						[]
		for group_idx in range(SCREAMING_SNAKE_CASE__			):
				snake_case  :       List[Any]     			=						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
				if num_shards_to_add == 0:
						break
				snake_case  :       Optional[Any]     			=						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
				snake_case  :       Any     			=						range(SCREAMING_SNAKE_CASE__ ,       start + num_shards_to_add			)
				shards_indices_per_group.append(SCREAMING_SNAKE_CASE__			)
		return shards_indices_per_group




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[dict]:


		'''simple docstring'''


		snake_case  :       Dict     			=						_number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE__			)
		if num_shards == 1:
				return [dict(SCREAMING_SNAKE_CASE__			)]
		else:
				snake_case  :       Optional[Any]     			=						_distribute_shards(num_shards=SCREAMING_SNAKE_CASE__ ,       max_num_jobs=SCREAMING_SNAKE_CASE__			)
				return [
				    {
				        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
				        if isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
				        else value
				        for key, value in gen_kwargs.items()
				    }
				    for group_idx in range(len(SCREAMING_SNAKE_CASE__			)			)
				]




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							dict:


		'''simple docstring'''


		return {
		    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
		    if isinstance(gen_kwargs_list[0][key] ,       SCREAMING_SNAKE_CASE__			)
		    else gen_kwargs_list[0][key]
		    for key in gen_kwargs_list[0]
		}




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							dict:


		'''simple docstring'''


		snake_case  :       Optional[int]     			=						{len(SCREAMING_SNAKE_CASE__			) for value in gen_kwargs.values() if isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)}
		snake_case  :       Tuple     			=						{}
		for size in list_sizes:
				snake_case  :       List[Any]     			=						list(range(SCREAMING_SNAKE_CASE__			)			)
				rng.shuffle(indices_per_size[size]			)
		# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
		snake_case  :       Any     			=						dict(SCREAMING_SNAKE_CASE__			)
		for key, value in shuffled_kwargs.items():
				if isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			):
						snake_case  :       List[Any]     			=						[value[i] for i in indices_per_size[len(SCREAMING_SNAKE_CASE__			)]]
		return shuffled_kwargs




'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Any     			=						TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''      )

				snake_case  :       List[Any]     			=						tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,	dtype=tf.intaa  ,	)  # J'aime le camembert !"

				snake_case  :       Any     			=						model(UpperCamelCase__      )['''last_hidden_state''']
				snake_case  :       int     			=						tf.TensorShape((1, 10, 768)      )
				self.assertEqual(output.shape  ,	UpperCamelCase__      )
				# compare the actual values for a slice.
				snake_case  :       Dict     			=						tf.convert_to_tensor(
				    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,	dtype=tf.floataa  ,	)
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,	expected_slice.numpy()  ,	atol=1e-4      )      )


