



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       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			Any=14			,				lowerCAmelCase  :			List[Any]=7			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			Union[str, Any]=True			,				lowerCAmelCase  :			List[Any]=False			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			str=99			,				lowerCAmelCase  :			int=32			,				lowerCAmelCase  :			Optional[int]=4			,				lowerCAmelCase  :			str=4			,				lowerCAmelCase  :			Union[str, Any]=4			,				lowerCAmelCase  :			Union[str, Any]=37			,				lowerCAmelCase  :			Any="gelu"			,				lowerCAmelCase  :			List[Any]=0.1			,				lowerCAmelCase  :			List[Any]=0.1			,				lowerCAmelCase  :			Any=5_12			,				lowerCAmelCase  :			List[str]=0.02			,				)	->				List[Any]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[str])	->				Tuple:




         """simple docstring"""

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

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

         lowercase__     =  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=lowerCAmelCase			,				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 			UpperCAmelCase		(						self  :			Any)	->				Optional[Any]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			str)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  20
         lowercase__     =  model_class_name(lowerCAmelCase)

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

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

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

         lowercase__     =  model(lowerCAmelCase)

         lowercase__     =  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 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Dict)	->				Tuple:




         """simple docstring"""

         lowercase__     =  20
         lowercase__     =  model_class_name(lowerCAmelCase)

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

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

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

         lowercase__     =  model(lowerCAmelCase			,				attention_mask=lowerCAmelCase)

         lowercase__     =  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       UpperCAmelCase__(							UpperCamelCase__     ,							UpperCamelCase__     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   A    :  Any       				=    (FlaxGPTJForCausalLM,) if is_flax_available() else ()


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				int:




         """simple docstring"""

         lowercase__     =  FlaxGPTJModelTester(self)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				str:




         """simple docstring"""

         for model_class_name in self.all_model_classes:
               lowercase__,	lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Optional[int]:




         """simple docstring"""

         for model_class_name in self.all_model_classes:
               lowercase__,	lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)


   @tooslow
   def 			UpperCAmelCase		(						self  :			int)	->				str:




         """simple docstring"""

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

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

         lowercase__     =  jax.jit(model.generate)

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

         lowercase__     =  tokenizer.batch_decode(lowerCAmelCase			,				skip_special_tokens=lowerCAmelCase)

         lowercase__     =  [
             '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(lowerCAmelCase			,				lowerCAmelCase)


   @is_pt_flax_cross_test
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				str:




         """simple docstring"""

         lowercase__,	lowercase__     =  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
                     lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase)
                     lowercase__     =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

                     lowercase__     =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,				lowerCAmelCase)
                     lowercase__     =  fx_state

                     with torch.no_grad():
                           lowercase__     =  pt_model(**lowerCAmelCase).to_tuple()

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

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

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


   @is_pt_flax_cross_test
   def 			UpperCAmelCase		(						self  :			List[Any])	->				List[str]:




         """simple docstring"""

         lowercase__,	lowercase__     =  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
                     lowercase__     =  self._prepare_for_class(lowerCAmelCase			,				lowerCAmelCase)
                     lowercase__     =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

                     lowercase__     =  pt_model_class(lowerCAmelCase).eval()
                     lowercase__     =  model_class(lowerCAmelCase			,				dtype=jnp.floataa)

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

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

                     with torch.no_grad():
                           lowercase__     =  pt_model(**lowerCAmelCase).to_tuple()

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

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

                     with torch.no_grad():
                           lowercase__     =  pt_model_loaded(**lowerCAmelCase).to_tuple()

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



   @tooslow
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Optional[int]:




         """simple docstring"""

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





import argparse
import hashlib  # hashlib is only used inside the Test class
import struct



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Optional[Any]			,				lowerCAmelCase  :			str)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  data
         lowercase__     =  [0X6_7_4_5_2_3_0_1, 0XE_F_C_D_A_B_8_9, 0X9_8_B_A_D_C_F_E, 0X1_0_3_2_5_4_7_6, 0XC_3_D_2_E_1_F_0]


   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			Optional[int])	->				str:




         """simple docstring"""

         return ((n << b) | (n >> (32 - b))) & 0XF_F_F_F_F_F_F_F


   def 			UpperCAmelCase		(						self  :			Dict)	->				Dict:




         """simple docstring"""

         lowercase__     =  B'\x80' + B'\x00' * (63 - (len(self.data) + 8) % 64)
         lowercase__     =  self.data + padding + struct.pack('>Q'			,				8 * len(self.data))
         return padded_data


   def 			UpperCAmelCase		(						self  :			int)	->				Tuple:




         """simple docstring"""

         return [
             self.padded_data[i : i + 64] for i in range(0			,				len(self.padded_data)			,				64)
         ]


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			int)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  list(struct.unpack('>16L'			,				lowerCAmelCase)) + [0] * 64
         for i in range(16			,				80):
               lowercase__     =  self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])			,				1)
         return w



   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.padding()
         lowercase__     =  self.split_blocks()
         for block in self.blocks:
               lowercase__     =  self.expand_block(lowerCAmelCase)
               lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  self.h
               for i in range(0			,				80):
                     if 0 <= i < 20:
                           lowercase__     =  (b & c) | ((~b) & d)
                           lowercase__     =  0X5_A_8_2_7_9_9_9
                     elif 20 <= i < 40:
                           lowercase__     =  b ^ c ^ d
                           lowercase__     =  0X6_E_D_9_E_B_A_1
                     elif 40 <= i < 60:
                           lowercase__     =  (b & c) | (b & d) | (c & d)
                           lowercase__     =  0X8_F_1_B_B_C_D_C
                     elif 60 <= i < 80:
                           lowercase__     =  b ^ c ^ d
                           lowercase__     =  0XC_A_6_2_C_1_D_6
                     lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  (
                         self.rotate(lowerCAmelCase			,				5) + f + e + k + expanded_block[i] & 0XF_F_F_F_F_F_F_F,
                         a,
                         self.rotate(lowerCAmelCase			,				30),
                         c,
                         d,
                     )
               lowercase__     =  (
                   self.h[0] + a & 0XF_F_F_F_F_F_F_F,
                   self.h[1] + b & 0XF_F_F_F_F_F_F_F,
                   self.h[2] + c & 0XF_F_F_F_F_F_F_F,
                   self.h[3] + d & 0XF_F_F_F_F_F_F_F,
                   self.h[4] + e & 0XF_F_F_F_F_F_F_F,
               )
         return ("{:08x}" * 5).format(*self.h)

def   _lowerCAmelCase   (						):
      lowercase__     =  B'Test String'
      assert SHAaHash(A__	).final_hash() == hashlib.shaa(A__	).hexdigest()  # noqa: S324

def   _lowerCAmelCase   (						):
      lowercase__     =  argparse.ArgumentParser(description='Process some strings or files'	)
      parser.add_argument(
          '--string'						,  dest='input_string'						,  default='Hello World!! Welcome to Cryptography'						,  help='Hash the string'						,  )
      parser.add_argument('--file'						,  dest='input_file'						,  help='Hash contents of a file'	)
      lowercase__     =  parser.parse_args()
      lowercase__     =  args.input_string
      # In any case hash input should be a bytestring
      if args.input_file:
            with open(args.input_file						,  'rb'	) as f:
                  lowercase__     =  f.read()
      else:
            lowercase__     =  bytes(A__						,  'utf-8'	)
      print(SHAaHash(A__	).final_hash()	)


if __name__ == "__main__":
       main()
       import doctest

       doctest.testmod()
