
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   []
  for line in lines:
    lowercase__	: Dict     		=   re.sub(R"""#.*"""			,			""""""			,			lowercase_ )  # remove comments
    if line:
      filtered_lines.append(lowercase_ )
  lowercase__	: int     		=   """\n""".join(lowercase_ )

  # Make a hash from all this code
  lowercase__	: int     		=   full_str.encode("""utf-8""" )
  return shaaaa(lowercase_ ).hexdigest()


# get importable module names and hash for caching
lowerCamelCase__     :		Tuple        = {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
lowerCamelCase__     :		Dict        = {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowerCamelCase__     :		Dict        = {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
lowerCamelCase__     :		Dict[str, List[str]]        = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
     _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")








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       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=14       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	):


   '''simple docstring'''




   lowercase__	: str     		=   parent
   lowercase__	: Optional[int]     		=   batch_size
   lowercase__	: Optional[int]     		=   seq_length
   lowercase__	: Union[str, Any]     		=   is_training
   lowercase__	: Any     		=   use_input_mask
   lowercase__	: Optional[int]     		=   use_token_type_ids
   lowercase__	: Optional[Any]     		=   use_labels
   lowercase__	: Optional[int]     		=   vocab_size
   lowercase__	: Optional[Any]     		=   hidden_size
   lowercase__	: Any     		=   rotary_dim
   lowercase__	: Optional[Any]     		=   num_hidden_layers
   lowercase__	: Tuple     		=   num_attention_heads
   lowercase__	: Tuple     		=   intermediate_size
   lowercase__	: List[str]     		=   hidden_act
   lowercase__	: Optional[Any]     		=   hidden_dropout_prob
   lowercase__	: int     		=   attention_probs_dropout_prob
   lowercase__	: Any     		=   max_position_embeddings
   lowercase__	: Optional[int]     		=   initializer_range
   lowercase__	: Optional[int]     		=   None
   lowercase__	: str     		=   vocab_size - 1
   lowercase__	: Any     		=   vocab_size - 1
   lowercase__	: Dict     		=   vocab_size - 1


 def   lowercase__  (       self):


   '''simple docstring'''




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

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

   lowercase__	: List[Any]     		=   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=SCREAMING_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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.prepare_config_and_inputs()
   lowercase__		,     lowercase__		,     lowercase__	: Optional[Any]     		=   config_and_inputs
   lowercase__	: Optional[Any]     		=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
   return config, inputs_dict


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   20
   lowercase__	: int     		=   model_class_name(SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[Any]     		=   model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   jnp.ones((input_ids.shape[0], max_decoder_length)       ,	dtype="""i4""")

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

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

   lowercase__	: Tuple     		=   model(SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   20
   lowercase__	: List[Any]     		=   model_class_name(SCREAMING_SNAKE_CASE_)

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

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

   lowercase__	: Any     		=   model(
       input_ids[:, :-1]       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	past_key_values=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: int     		=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="""i4""")
   lowercase__	: Tuple     		=   model(
       input_ids[:, -1:]       ,	past_key_values=outputs_cache.past_key_values       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   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 (						UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Dict	   =      (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 __lowerCAmelCase	:  str	   =      (FlaxGPTJForCausalLM,) if is_flax_available() else ()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   FlaxGPTJModelTester(self)


 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




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


 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   GPTaTokenizer.from_pretrained("""gpt2"""       ,	pad_token="""<|endoftext|>"""       ,	padding_side="""left""")
   lowercase__	: List[str]     		=   tokenizer(["""Hello this is a long string""", """Hey"""]       ,	return_tensors="""np"""       ,	padding=SCREAMING_SNAKE_CASE_       ,	truncation=SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""")
   lowercase__	: Optional[Any]     		=   False
   lowercase__	: List[str]     		=   model.config.eos_token_id

   lowercase__	: List[Any]     		=   jax.jit(model.generate)

   lowercase__	: Tuple     		=   jit_generate(
       inputs["""input_ids"""]       ,	attention_mask=inputs["""attention_mask"""]       ,	pad_token_id=tokenizer.pad_token_id).sequences

   lowercase__	: List[str]     		=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,	skip_special_tokens=SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   [
       """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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: int     		=   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__	: List[Any]     		=   self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: Any     		=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

       lowercase__		,     lowercase__	: Dict     		=   pt_inputs["""input_ids"""].shape
       lowercase__	: int     		=   np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,))
       for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_):
         lowercase__	: str     		=   0
         lowercase__	: List[Any]     		=   1
         lowercase__	: Dict     		=   0
         lowercase__	: Any     		=   1
       lowercase__	: List[Any]     		=   pt_model_class(SCREAMING_SNAKE_CASE_).eval()
       lowercase__	: Optional[int]     		=   model_class(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.floataa)

       lowercase__	: List[str]     		=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: List[Any]     		=   fx_state

       with torch.no_grad():
         lowercase__	: Optional[int]     		=   pt_model(**SCREAMING_SNAKE_CASE_).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(SCREAMING_SNAKE_CASE_)
         lowercase__	: List[str]     		=   model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,	from_pt=SCREAMING_SNAKE_CASE_)

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


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''simple docstring'''




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

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

       lowercase__	: str     		=   pt_model_class(SCREAMING_SNAKE_CASE_).eval()
       lowercase__	: Union[str, Any]     		=   model_class(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.floataa)

       lowercase__	: Optional[int]     		=   load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_       ,	fx_model.params)
       lowercase__		,     lowercase__	: str     		=   pt_inputs["""input_ids"""].shape
       lowercase__	: List[Any]     		=   np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,))
       for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_):
         lowercase__	: Tuple     		=   0
         lowercase__	: int     		=   1
         lowercase__	: str     		=   0
         lowercase__	: str     		=   1

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

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

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

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

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

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



 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




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






