


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 lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase=14	,   _UpperCAmelCase=7	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=False	,   _UpperCAmelCase=True	,   _UpperCAmelCase=99	,   _UpperCAmelCase=32	,   _UpperCAmelCase=4	,   _UpperCAmelCase=4	,   _UpperCAmelCase=4	,   _UpperCAmelCase=37	,   _UpperCAmelCase="gelu"	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=5_12	,   _UpperCAmelCase=0.02	,   ):
       snake_case_ =   parent
       snake_case_ =   batch_size
       snake_case_ =   seq_length
       snake_case_ =   is_training
       snake_case_ =   use_input_mask
       snake_case_ =   use_token_type_ids
       snake_case_ =   use_labels
       snake_case_ =   vocab_size
       snake_case_ =   hidden_size
       snake_case_ =   rotary_dim
       snake_case_ =   num_hidden_layers
       snake_case_ =   num_attention_heads
       snake_case_ =   intermediate_size
       snake_case_ =   hidden_act
       snake_case_ =   hidden_dropout_prob
       snake_case_ =   attention_probs_dropout_prob
       snake_case_ =   max_position_embeddings
       snake_case_ =   initializer_range
       snake_case_ =   None
       snake_case_ =   vocab_size - 1
       snake_case_ =   vocab_size - 1
       snake_case_ =   vocab_size - 1



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size      )

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

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   20
       snake_case_ =   model_class_name(_UpperCAmelCase      )

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

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

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

       snake_case_ =   model(_UpperCAmelCase      )

       snake_case_ =   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	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   20
       snake_case_ =   model_class_name(_UpperCAmelCase      )

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

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

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

       snake_case_ =   model(_UpperCAmelCase	,   attention_mask=_UpperCAmelCase      )

       snake_case_ =   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 lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 __snake_case				=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   FlaxGPTJModelTester(self      )



 def 		UpperCamelCase__  (			self      ):
       for model_class_name in self.all_model_classes:
             snake_case_ ,					snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       for model_class_name in self.all_model_classes:
             snake_case_ ,					snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      )



 @tooslow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   GPTaTokenizer.from_pretrained('''gpt2'''	,   pad_token='''<|endoftext|>'''	,   padding_side='''left'''      )
       snake_case_ =   tokenizer(['''Hello this is a long string''', '''Hey''']	,   return_tensors='''np'''	,   padding=_UpperCAmelCase	,   truncation=_UpperCAmelCase      )

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

       snake_case_ =   jax.jit(model.generate      )

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

       snake_case_ =   tokenizer.batch_decode(_UpperCAmelCase	,   skip_special_tokens=_UpperCAmelCase      )

       snake_case_ =   [
           '''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(_UpperCAmelCase	,   _UpperCAmelCase      )



 @is_pt_flax_cross_test
 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   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
                   snake_case_ =   self._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase      )
                   snake_case_ =   {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

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

                   snake_case_ =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,   _UpperCAmelCase      )
                   snake_case_ =   fx_state

                   with torch.no_grad():
                         snake_case_ =   pt_model(**_UpperCAmelCase      ).to_tuple()

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

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

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



 @is_pt_flax_cross_test
 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   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
                   snake_case_ =   self._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase      )
                   snake_case_ =   {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

                   snake_case_ =   pt_model_class(_UpperCAmelCase      ).eval()
                   snake_case_ =   model_class(_UpperCAmelCase	,   dtype=jnp.floataa      )

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

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

                   with torch.no_grad():
                         snake_case_ =   pt_model(**_UpperCAmelCase      ).to_tuple()

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

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

                   with torch.no_grad():
                         snake_case_ =   pt_model_loaded(**_UpperCAmelCase      ).to_tuple()

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





 @tooslow
 def 		UpperCamelCase__  (			self      ):
       for model_class_name in self.all_model_classes:
             snake_case_ =   model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''      )
             snake_case_ =   model(np.ones((1, 1)      )      )
             self.assertIsNotNone(_UpperCAmelCase      )



from collections import namedtuple

UpperCAmelCase    =					namedtuple("""from_to""", """from_ to""")

UpperCAmelCase    =					{
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1000),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00_454, 264.172),
    """cubicyard""": from_to(0.76_455, 1.30_795),
    """cubicfoot""": from_to(0.028, 35.3_147),
    """cup""": from_to(0.000_236_588, 4_226.75),
}






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      if from_type not in METRIC_CONVERSION:
            raise ValueError(
                f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
                + ''', '''.join(SCREAMING_SNAKE_CASE      )      )
      if to_type not in METRIC_CONVERSION:
            raise ValueError(
                f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
                + ''', '''.join(SCREAMING_SNAKE_CASE      )      )
      return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
   import doctest

   doctest.testmod()