





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		)

class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	def __init__(						self							:     str					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     Union[str, Any]=13					,      __lowerCamelCase							:     List[str]=7					,      __lowerCamelCase							:     Tuple=True					,      __lowerCamelCase							:     Any=True					,      __lowerCamelCase							:     Dict=True					,      __lowerCamelCase							:     Union[str, Any]=True					,      __lowerCamelCase							:     Tuple=99					,      __lowerCamelCase							:     Dict=32					,      __lowerCamelCase							:     Dict=5					,      __lowerCamelCase							:     Optional[Any]=4					,      __lowerCamelCase							:     Union[str, Any]=37					,      __lowerCamelCase							:     Tuple="gelu"					,      __lowerCamelCase							:     Union[str, Any]=0.1					,      __lowerCamelCase							:     int=0.1					,      __lowerCamelCase							:     Dict=512					,      __lowerCamelCase							:     Optional[Any]=16					,      __lowerCamelCase							:     int=2					,      __lowerCamelCase							:     Optional[int]=0.02					,      __lowerCamelCase							:     int=4					,      )       -> Dict:

			'''simple docstring'''




			__lowercase											=     parent
			__lowercase											=     batch_size
			__lowercase											=     seq_length
			__lowercase											=     is_training
			__lowercase											=     use_attention_mask
			__lowercase											=     use_token_type_ids
			__lowercase											=     use_labels
			__lowercase											=     vocab_size
			__lowercase											=     hidden_size
			__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											=     type_vocab_size
			__lowercase											=     type_sequence_label_size
			__lowercase											=     initializer_range
			__lowercase											=     num_choices




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

			'''simple docstring'''




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

			__lowercase											=     None
			if self.use_attention_mask:
					__lowercase											=     random_attention_mask([self.batch_size, self.seq_length]   )

			__lowercase											=     None
			if self.use_token_type_ids:
					__lowercase											=     ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size   )

			__lowercase											=     RobertaPreLayerNormConfig(
			    vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      type_vocab_size=self.type_vocab_size					,      is_decoder=__lowerCamelCase					,      initializer_range=self.initializer_range					,      )

			return config, input_ids, token_type_ids, attention_mask




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

			'''simple docstring'''




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






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

			'''simple docstring'''




			__lowercase											=     self.prepare_config_and_inputs()
			__lowercase						,      __lowercase						,      __lowercase						,      __lowercase											=     config_and_inputs

			__lowercase											=     True
			__lowercase											=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
			__lowercase											=     ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2   )

			return (
			    config,
			    input_ids,
			    token_type_ids,
			    encoder_hidden_states,
			    encoder_attention_mask,
			)





@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		True

	__UpperCamelCase   =		(
	    (
	        FlaxRobertaPreLayerNormModel,
	        FlaxRobertaPreLayerNormForCausalLM,
	        FlaxRobertaPreLayerNormForMaskedLM,
	        FlaxRobertaPreLayerNormForSequenceClassification,
	        FlaxRobertaPreLayerNormForTokenClassification,
	        FlaxRobertaPreLayerNormForMultipleChoice,
	        FlaxRobertaPreLayerNormForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




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

			'''simple docstring'''




			__lowercase											=     FlaxRobertaPreLayerNormModelTester(self   )






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

			'''simple docstring'''




			for model_class_name in self.all_model_classes:
					__lowercase											=     model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,      from_pt=__lowerCamelCase   )
					__lowercase											=     model(np.ones((1, 1)   )   )
					self.assertIsNotNone(__lowerCamelCase   )





@require_flax
class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	@slow
	def    UpperCAmelCase			(						self							:     Any   )       -> Any:

			'''simple docstring'''




			__lowercase											=     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,      from_pt=__lowerCamelCase   )

			__lowercase											=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					,      dtype=jnp.intaa   )
			__lowercase											=     model(__lowerCamelCase   )[0]
			__lowercase											=     [1, 11, 50_265]
			self.assertEqual(list(output.shape   )					,      __lowerCamelCase   )
			# compare the actual values for a slice.
			__lowercase											=     np.array(
			    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]					,      dtype=np.floataa   )
			self.assertTrue(np.allclose(output[:, :3, :3]					,      __lowerCamelCase					,      atol=1E-4   )   )






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

			'''simple docstring'''




			__lowercase											=     FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,      from_pt=__lowerCamelCase   )

			__lowercase											=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					,      dtype=jnp.intaa   )
			__lowercase											=     model(__lowerCamelCase   )[0]
			# compare the actual values for a slice.
			__lowercase											=     np.array(
			    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]					,      dtype=np.floataa   )
			self.assertTrue(np.allclose(output[:, :3, :3]					,      __lowerCamelCase					,      atol=1E-4   )   )











import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   '''http://www.mocksite.com/file1.txt'''
SCREAMING_SNAKE_CASE_   :      str			=   '''"text": ["foo", "foo"]'''
SCREAMING_SNAKE_CASE_   :      Optional[int]			=   '''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''

class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		2_00
	__UpperCamelCase   =		{'''Content-Length''': '''100'''}
	__UpperCamelCase   =		{}




	def    UpperCAmelCase			(						self							:     str					,      **__lowerCamelCase							:     Optional[int]   )       -> str:

			'''simple docstring'''




			return [bytes(__lowerCamelCase					,      'utf-8'   )]





def  SCREAMING_SNAKE_CASE				(      *snake_case						,   **snake_case							)		->    int:
		return MockResponse()



@pytest.mark.parametrize('urls_type'						,   [str, list, dict]							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Optional[Any]:
		import requests

		monkeypatch.setattr(snake_case						,   'request'						,   snake_case							)

		__lowercase											=     URL
		if issubclass(snake_case						,   snake_case							):
				__lowercase											=     url
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     [url]
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     {'train': url}
		__lowercase											=     'dummy'
		__lowercase											=     'downloads'
		__lowercase											=     tmp_path
		__lowercase											=     DownloadConfig(
		    cache_dir=os.path.join(snake_case						,   snake_case							)						,   use_etag=snake_case						,   )
		__lowercase											=     DownloadManager(dataset_name=snake_case						,   download_config=snake_case							)
		__lowercase											=     dl_manager.download(snake_case							)
		__lowercase											=     urls
		for downloaded_paths in [downloaded_paths]:
				if isinstance(snake_case						,   snake_case							):
						__lowercase											=     [downloaded_paths]
						__lowercase											=     [urls]
				elif isinstance(snake_case						,   snake_case							):
						assert "train" in downloaded_paths.keys()
						__lowercase											=     downloaded_paths.values()
						__lowercase											=     urls.values()
				assert downloaded_paths
				for downloaded_path, input_url in zip(snake_case						,   snake_case							):
						assert downloaded_path == dl_manager.downloaded_paths[input_url]
						__lowercase											=     Path(snake_case							)
						__lowercase											=     downloaded_path.parts
						assert parts[-1] == HASH
						assert parts[-2] == cache_subdir
						assert downloaded_path.exists()
						__lowercase											=     downloaded_path.read_text()
						assert content == CONTENT
						__lowercase											=     downloaded_path.with_suffix('.json'							)
						assert metadata_downloaded_path.exists()
						__lowercase											=     json.loads(metadata_downloaded_path.read_text()							)
						assert metadata_content == {"url": URL, "etag": None}



@pytest.mark.parametrize('paths_type'						,   [str, list, dict]							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Union[str, Any]:
		__lowercase											=     str(snake_case							)
		if issubclass(snake_case						,   snake_case							):
				__lowercase											=     filename
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     [filename]
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     {'train': filename}
		__lowercase											=     'dummy'
		__lowercase											=     xz_file.parent
		__lowercase											=     'extracted'
		__lowercase											=     DownloadConfig(
		    cache_dir=snake_case						,   use_etag=snake_case						,   )
		__lowercase											=     DownloadManager(dataset_name=snake_case						,   download_config=snake_case							)
		__lowercase											=     dl_manager.extract(snake_case							)
		__lowercase											=     paths
		for extracted_paths in [extracted_paths]:
				if isinstance(snake_case						,   snake_case							):
						__lowercase											=     [extracted_paths]
						__lowercase											=     [paths]
				elif isinstance(snake_case						,   snake_case							):
						assert "train" in extracted_paths.keys()
						__lowercase											=     extracted_paths.values()
						__lowercase											=     paths.values()
				assert extracted_paths
				for extracted_path, input_path in zip(snake_case						,   snake_case							):
						assert extracted_path == dl_manager.extracted_paths[input_path]
						__lowercase											=     Path(snake_case							)
						__lowercase											=     extracted_path.parts
						assert parts[-1] == hash_url_to_filename(snake_case						,   etag=snake_case							)
						assert parts[-2] == extracted_subdir
						assert extracted_path.exists()
						__lowercase											=     extracted_path.read_text()
						__lowercase											=     text_file.read_text()
						assert extracted_file_content == expected_file_content



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:
		assert path.endswith('.jsonl'							)
		for num_items, line in enumerate(snake_case						,   start=1							):
				__lowercase											=     json.loads(line.decode('utf-8'							)							)
				assert item.keys() == {"col_1", "col_2", "col_3"}
		assert num_items == 4



@pytest.mark.parametrize('archive_jsonl'						,   ['tar_jsonl_path', 'zip_jsonl_path']							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:
		__lowercase											=     request.getfixturevalue(snake_case							)
		__lowercase											=     DownloadManager()
		for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(snake_case							)						,   start=1							):
				_test_jsonl(snake_case						,   snake_case							)
		assert num_jsonl == 2



@pytest.mark.parametrize('archive_nested_jsonl'						,   ['tar_nested_jsonl_path', 'zip_nested_jsonl_path']							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Any:
		__lowercase											=     request.getfixturevalue(snake_case							)
		__lowercase											=     DownloadManager()
		for num_tar, (path, file) in enumerate(dl_manager.iter_archive(snake_case							)						,   start=1							):
				for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(snake_case							)						,   start=1							):
						_test_jsonl(snake_case						,   snake_case							)
		assert num_tar == 1
		assert num_jsonl == 2



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     DownloadManager()
		for num_file, file in enumerate(dl_manager.iter_files(snake_case							)						,   start=1							):
				assert os.path.basename(snake_case							) == ("test.txt" if num_file == 1 else "train.txt")
		assert num_file == 2




