




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


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

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
    import torch







class __snake_case			:







   def __init__(      self		:						int					,						_snake_case		:						Tuple					,						_snake_case		:						Optional[int]=14					,						_snake_case		:						List[str]=7					,						_snake_case		:						int=True					,						_snake_case		:						str=True					,						_snake_case		:						str=False					,						_snake_case		:						List[str]=True					,						_snake_case		:						List[str]=99					,						_snake_case		:						Optional[Any]=32					,						_snake_case		:						Optional[Any]=4					,						_snake_case		:						Optional[Any]=4					,						_snake_case		:						Dict=4					,						_snake_case		:						Optional[Any]=37					,						_snake_case		:						Union[str, Any]="gelu"					,						_snake_case		:						Optional[int]=0.1					,						_snake_case		:						str=0.1					,						_snake_case		:						Optional[int]=512					,						_snake_case		:						int=0.0_2					,						):

        """simple docstring"""


        UpperCAmelCase_          =							parent
        UpperCAmelCase_          =							batch_size
        UpperCAmelCase_          =							seq_length
        UpperCAmelCase_          =							is_training
        UpperCAmelCase_          =							use_input_mask
        UpperCAmelCase_          =							use_token_type_ids
        UpperCAmelCase_          =							use_labels
        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							hidden_size
        UpperCAmelCase_          =							rotary_dim
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads
        UpperCAmelCase_          =							intermediate_size
        UpperCAmelCase_          =							hidden_act
        UpperCAmelCase_          =							hidden_dropout_prob
        UpperCAmelCase_          =							attention_probs_dropout_prob
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							vocab_size - 1
        UpperCAmelCase_          =							vocab_size - 1
        UpperCAmelCase_          =							vocab_size - 1







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size)

        UpperCAmelCase_          =							None
        if self.use_input_mask:
             UpperCAmelCase_          =							random_attention_mask([self.batch_size, self.seq_length])

        UpperCAmelCase_          =							GPTJConfig(
            vocab_size=self.vocab_size					,						n_embd=self.hidden_size					,						n_layer=self.num_hidden_layers					,						n_head=self.num_attention_heads					,						n_positions=self.max_position_embeddings					,						use_cache=_snake_case					,						bos_token_id=self.bos_token_id					,						eos_token_id=self.eos_token_id					,						pad_token_id=self.pad_token_id					,						rotary_dim=self.rotary_dim					,						)

        return (config, input_ids, input_mask)







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.prepare_config_and_inputs()
        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							config_and_inputs
        UpperCAmelCase_          =							{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						str					,						_snake_case		:						str					,						_snake_case		:						Dict					,						_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							20
        UpperCAmelCase_          =							model_class_name(_snake_case)

        UpperCAmelCase_          =							model.init_cache(input_ids.shape[0]					,						_snake_case)
        UpperCAmelCase_          =							jnp.ones((input_ids.shape[0], max_decoder_length)					,						dtype='''i4''')

        UpperCAmelCase_          =							jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]					,						(input_ids.shape[0], input_ids.shape[-1] - 1))
        UpperCAmelCase_          =							model(
            input_ids[:, :-1]					,						attention_mask=_snake_case					,						past_key_values=_snake_case					,						position_ids=_snake_case					,						)

        UpperCAmelCase_          =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,						dtype='''i4''')
        UpperCAmelCase_          =							model(
            input_ids[:, -1:]					,						attention_mask=_snake_case					,						past_key_values=outputs_cache.past_key_values					,						position_ids=_snake_case					,						)

        UpperCAmelCase_          =							model(_snake_case)

        UpperCAmelCase_          =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3					,						msg=F"""Max diff is {diff}""")







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Any					,						_snake_case		:						List[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							20
        UpperCAmelCase_          =							model_class_name(_snake_case)

        UpperCAmelCase_          =							jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]					,						axis=-1					,						)

        UpperCAmelCase_          =							model.init_cache(input_ids.shape[0]					,						_snake_case)
        UpperCAmelCase_          =							jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]					,						(input_ids.shape[0], input_ids.shape[-1] - 1))

        UpperCAmelCase_          =							model(
            input_ids[:, :-1]					,						attention_mask=_snake_case					,						past_key_values=_snake_case					,						position_ids=_snake_case					,						)
        UpperCAmelCase_          =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,						dtype='''i4''')
        UpperCAmelCase_          =							model(
            input_ids[:, -1:]					,						past_key_values=outputs_cache.past_key_values					,						attention_mask=_snake_case					,						position_ids=_snake_case					,						)

        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case)

        UpperCAmelCase_          =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3					,						msg=F"""Max diff is {diff}""")







@require_flax
class __snake_case			(			a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    str          =  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   UpperCAmelCase__  :    List[Any]          =  (FlaxGPTJForCausalLM,) if is_flax_available() else ()







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							FlaxGPTJModelTester(self)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        for model_class_name in self.all_model_classes:
             UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(_snake_case					,						_snake_case					,						_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        for model_class_name in self.all_model_classes:
             UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 _snake_case					,						_snake_case					,						_snake_case					,						_snake_case)







   @tooslow
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							GPTaTokenizer.from_pretrained('''gpt2'''					,						pad_token='''<|endoftext|>'''					,						padding_side='''left''')
        UpperCAmelCase_          =							tokenizer(['''Hello this is a long string''', '''Hey''']					,						return_tensors='''np'''					,						padding=_snake_case					,						truncation=_snake_case)

        UpperCAmelCase_          =							FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
        UpperCAmelCase_          =							False
        UpperCAmelCase_          =							model.config.eos_token_id

        UpperCAmelCase_          =							jax.jit(model.generate)

        UpperCAmelCase_          =							jit_generate(
            inputs['''input_ids''']					,						attention_mask=inputs['''attention_mask''']					,						pad_token_id=tokenizer.pad_token_id).sequences

        UpperCAmelCase_          =							tokenizer.batch_decode(_snake_case					,						skip_special_tokens=_snake_case)

        UpperCAmelCase_          =							[
            '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
            '''Hey, I\'m a little late to the party. I\'m going to''',
        ]

        self.assertListEqual(_snake_case					,						_snake_case)







   @is_pt_flax_cross_test
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__):
                  # prepare inputs
                  UpperCAmelCase_          =							self._prepare_for_class(_snake_case					,						_snake_case)
                  UpperCAmelCase_          =							{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  UpperCAmelCase_          =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  UpperCAmelCase_          =							getattr(_snake_case					,						_snake_case)

                  UpperCAmelCase_     ,       UpperCAmelCase_          =							pt_inputs['''input_ids'''].shape
                  UpperCAmelCase_          =							np.random.randint(0					,						seq_length - 1					,						size=(batch_size,))
                  for batch_idx, start_index in enumerate(_snake_case):
                       UpperCAmelCase_          =							0
                       UpperCAmelCase_          =							1
                       UpperCAmelCase_          =							0
                       UpperCAmelCase_          =							1
                  UpperCAmelCase_          =							pt_model_class(_snake_case).eval()
                  UpperCAmelCase_          =							model_class(_snake_case					,						dtype=jnp.floataa)

                  UpperCAmelCase_          =							convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,						_snake_case)
                  UpperCAmelCase_          =							fx_state

                  with torch.no_grad():
                       UpperCAmelCase_          =							pt_model(**_snake_case).to_tuple()

                  UpperCAmelCase_          =							fx_model(**_snake_case).to_tuple()
                  self.assertEqual(len(_snake_case)					,						len(_snake_case)					,						'''Output lengths differ between Flax and PyTorch''')
                  for fx_output, pt_output in zip(_snake_case					,						_snake_case):
                       self.assert_almost_equals(fx_output[:, -1]					,						pt_output[:, -1].numpy()					,						4e-2)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       pt_model.save_pretrained(_snake_case)
                       UpperCAmelCase_          =							model_class.from_pretrained(_snake_case					,						from_pt=_snake_case)

                  UpperCAmelCase_          =							fx_model_loaded(**_snake_case).to_tuple()
                  self.assertEqual(
                      len(_snake_case)					,						len(_snake_case)					,						'''Output lengths differ between Flax and PyTorch''')
                  for fx_output_loaded, pt_output in zip(_snake_case					,						_snake_case):
                       self.assert_almost_equals(fx_output_loaded[:, -1]					,						pt_output[:, -1].numpy()					,						4e-2)







   @is_pt_flax_cross_test
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__):
                  # prepare inputs
                  UpperCAmelCase_          =							self._prepare_for_class(_snake_case					,						_snake_case)
                  UpperCAmelCase_          =							{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  UpperCAmelCase_          =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  UpperCAmelCase_          =							getattr(_snake_case					,						_snake_case)

                  UpperCAmelCase_          =							pt_model_class(_snake_case).eval()
                  UpperCAmelCase_          =							model_class(_snake_case					,						dtype=jnp.floataa)

                  UpperCAmelCase_          =							load_flax_weights_in_pytorch_model(_snake_case					,						fx_model.params)
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							pt_inputs['''input_ids'''].shape
                  UpperCAmelCase_          =							np.random.randint(0					,						seq_length - 1					,						size=(batch_size,))
                  for batch_idx, start_index in enumerate(_snake_case):
                       UpperCAmelCase_          =							0
                       UpperCAmelCase_          =							1
                       UpperCAmelCase_          =							0
                       UpperCAmelCase_          =							1

                  # make sure weights are tied in PyTorch
                  pt_model.tie_weights()

                  with torch.no_grad():
                       UpperCAmelCase_          =							pt_model(**_snake_case).to_tuple()

                  UpperCAmelCase_          =							fx_model(**_snake_case).to_tuple()
                  self.assertEqual(len(_snake_case)					,						len(_snake_case)					,						'''Output lengths differ between Flax and PyTorch''')
                  for fx_output, pt_output in zip(_snake_case					,						_snake_case):
                       self.assert_almost_equals(fx_output[:, -1]					,						pt_output[:, -1].numpy()					,						4e-2)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       fx_model.save_pretrained(_snake_case)
                       UpperCAmelCase_          =							pt_model_class.from_pretrained(_snake_case					,						from_flax=_snake_case)

                  with torch.no_grad():
                       UpperCAmelCase_          =							pt_model_loaded(**_snake_case).to_tuple()

                  self.assertEqual(
                      len(_snake_case)					,						len(_snake_case)					,						'''Output lengths differ between Flax and PyTorch''')
                  for fx_output, pt_output in zip(_snake_case					,						_snake_case):
                       self.assert_almost_equals(fx_output[:, -1]					,						pt_output[:, -1].numpy()					,						4e-2)







   @tooslow
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        for model_class_name in self.all_model_classes:
             UpperCAmelCase_          =							model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
             UpperCAmelCase_          =							model(np.ones((1, 1)))
             self.assertIsNotNone(_snake_case)













from math import factorial

lowerCAmelCase_    =				{str(digit): factorial(digit) for digit in range(1_0)}






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase ,				_UpperCamelCase ):
								raise TypeError('''Parameter number must be int''' )

				if number < 0:
								raise ValueError('''Parameter number must be greater than or equal to 0''' )

				# Converts number in string to iterate on its digits and adds its factorial.
				return sum(DIGIT_FACTORIAL[digit] for digit in str(_UpperCamelCase ) )






def      lowerCamelCase_						(				_UpperCamelCase = 60 ,				_UpperCamelCase = 1_000_000 )		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase ,				_UpperCamelCase ) or not isinstance(_UpperCamelCase ,				_UpperCamelCase ):
								raise TypeError('''Parameters chain_length and number_limit must be int''' )

				if chain_length <= 0 or number_limit <= 0:
								raise ValueError(
								    '''Parameters chain_length and number_limit must be greater than 0''' )

				# the counter for the chains with the exact desired length
				snake_case_						:    Optional[Any]				=  0
				# the cached sizes of the previous chains
				snake_case_						:    dict[int, int]				=  {}

				for start_chain_element in range(1 ,				_UpperCamelCase ):
								# The temporary set will contain the elements of the chain
								snake_case_						:    List[str]				=  set()
								snake_case_						:    List[Any]				=  0

								# Stop computing the chain when you find a cached size, a repeating item or the
								# length is greater then the desired one.
								snake_case_						:    Any				=  start_chain_element
								while (
								    chain_element not in chain_sets_lengths
								    and chain_element not in chain_set
								    and chain_set_length <= chain_length
								):
												chain_set.add(_UpperCamelCase )
												chain_set_length += 1
												snake_case_						:    List[Any]				=  digit_factorial_sum(_UpperCamelCase )

								if chain_element in chain_sets_lengths:
												chain_set_length += chain_sets_lengths[chain_element]

								snake_case_						:    List[str]				=  chain_set_length

								# If chain contains the exact amount of elements increase the counter
								if chain_set_length == chain_length:
												chains_counter += 1

				return chains_counter


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution()}''')
