




import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
			import os

			from flax.core.frozen_dict import unfreeze
			from flax.traverse_util import flatten_dict

			from transformers import FlaxBertModel

			a_						     =    '0.12'  # assumed parallelism: 8






@require_flax
@is_staging_test
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@classmethod
			def 							__UpperCamelCase (			cls							: Any	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       TOKEN
				HfFolder.save_token(a	)
			@classmethod
			def 							__UpperCamelCase (			cls							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				try:
					delete_repo(token=cls._token      ,      repo_id="test-model-flax"	)
				except HTTPError:
					pass

				try:
					delete_repo(token=cls._token      ,      repo_id="valid_org/test-model-flax-org"	)
				except HTTPError:
					pass
			def 							__UpperCamelCase (			self							: List[str]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       BertConfig(
				    vocab_size=99      ,      hidden_size=32      ,      num_hidden_layers=5      ,      num_attention_heads=4      ,      intermediate_size=37	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       FlaxBertModel(a	)
				model.push_to_hub("test-model-flax"      ,      use_auth_token=self._token	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       FlaxBertModel.from_pretrained(F"{USER}/test-model-flax"	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       flatten_dict(unfreeze(model.params	)	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       flatten_dict(unfreeze(new_model.params	)	)

				for key in base_params.keys():
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (base_params[key] - new_params[key]).sum().item()
					self.assertLessEqual(a      ,      1e-3      ,      msg=F"{key} not identical"	)

				# Reset repo
				delete_repo(token=self._token      ,      repo_id="test-model-flax"	)

				# Push to hub via save_pretrained
				with tempfile.TemporaryDirectory() as tmp_dir:
					model.save_pretrained(a      ,      repo_id="test-model-flax"      ,      push_to_hub=a      ,      use_auth_token=self._token	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       FlaxBertModel.from_pretrained(F"{USER}/test-model-flax"	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       flatten_dict(unfreeze(model.params	)	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       flatten_dict(unfreeze(new_model.params	)	)

				for key in base_params.keys():
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (base_params[key] - new_params[key]).sum().item()
					self.assertLessEqual(a      ,      1e-3      ,      msg=F"{key} not identical"	)






			def 							__UpperCamelCase (			self							: int	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       BertConfig(
				    vocab_size=99      ,      hidden_size=32      ,      num_hidden_layers=5      ,      num_attention_heads=4      ,      intermediate_size=37	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       FlaxBertModel(a	)
				model.push_to_hub("valid_org/test-model-flax-org"      ,      use_auth_token=self._token	)

				SCREAMING_SNAKE_CASE   :      Any				     =       FlaxBertModel.from_pretrained("valid_org/test-model-flax-org"	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       flatten_dict(unfreeze(model.params	)	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       flatten_dict(unfreeze(new_model.params	)	)

				for key in base_params.keys():
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (base_params[key] - new_params[key]).sum().item()
					self.assertLessEqual(a      ,      1e-3      ,      msg=F"{key} not identical"	)

				# Reset repo
				delete_repo(token=self._token      ,      repo_id="valid_org/test-model-flax-org"	)

				# Push to hub via save_pretrained
				with tempfile.TemporaryDirectory() as tmp_dir:
					model.save_pretrained(
					    a      ,      repo_id="valid_org/test-model-flax-org"      ,      push_to_hub=a      ,      use_auth_token=self._token	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       FlaxBertModel.from_pretrained("valid_org/test-model-flax-org"	)

				SCREAMING_SNAKE_CASE   :      Any				     =       flatten_dict(unfreeze(model.params	)	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       flatten_dict(unfreeze(new_model.params	)	)

				for key in base_params.keys():
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (base_params[key] - new_params[key]).sum().item()
					self.assertLessEqual(a      ,      1e-3      ,      msg=F"{key} not identical"	)

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       True
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       flatten_dict(modela.params)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       flatten_dict(modela.params)
	for key in flat_params_a.keys():
		if np.sum(np.abs(flat_params_a[key] - flat_params_a[key])) > 1E-4:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       False

	return models_are_equal






@require_flax
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only"	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       FlaxBertModel(a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       "bert"
				with tempfile.TemporaryDirectory() as tmp_dir:
					model.save_pretrained(os.path.join(a      ,      a	)	)

					with self.assertRaises(a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       FlaxBertModel.from_pretrained(a	)

					SCREAMING_SNAKE_CASE   :      List[str]				     =       FlaxBertModel.from_pretrained(a      ,      subfolder=a	)

				self.assertTrue(check_models_equal(a      ,      a	)	)
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       FlaxBertModel(a	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "bert"
				with tempfile.TemporaryDirectory() as tmp_dir:
					model.save_pretrained(os.path.join(a      ,      a	)      ,      max_shard_size="10KB"	)

					with self.assertRaises(a	):
						SCREAMING_SNAKE_CASE   :      Tuple				     =       FlaxBertModel.from_pretrained(a	)

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       FlaxBertModel.from_pretrained(a      ,      subfolder=a	)

				self.assertTrue(check_models_equal(a      ,      a	)	)
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       "bert"
				SCREAMING_SNAKE_CASE   :      int				     =       "hf-internal-testing/tiny-random-bert-subfolder"

				with self.assertRaises(a	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       FlaxBertModel.from_pretrained(a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       FlaxBertModel.from_pretrained(a      ,      subfolder=a	)

				self.assertIsNotNone(a	)






			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       "bert"
				SCREAMING_SNAKE_CASE   :      int				     =       "hf-internal-testing/tiny-random-bert-sharded-subfolder"
				with self.assertRaises(a	):
					SCREAMING_SNAKE_CASE   :      Tuple				     =       FlaxBertModel.from_pretrained(a	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       FlaxBertModel.from_pretrained(a      ,      subfolder=a	)

				self.assertIsNotNone(a	)


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


a_						     =    datasets.utils.logging.get_logger(__name__)






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           =None






class _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =ParquetConfig
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: List[Any]	)							->       Tuple:

				"""simple docstring"""

				if not self.config.data_files:
					raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      str				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [dl_manager.iter_files(a	) for file in files]
					# Infer features is they are stoed in the arrow schema
					if self.info.features is None:
						for file in itertools.chain.from_iterable(a	):
							with open(a      ,      "rb"	) as f:
								SCREAMING_SNAKE_CASE   :      Dict				     =       datasets.Features.from_arrow_schema(pq.read_schema(a	)	)
							break
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Dict      ,      a							: pa.Table	)							->       pa.Table:

				"""simple docstring"""

				if self.info.features is not None:
					# more expensive cast to support nested features with keys in a different order
					# allows str <-> int/float or str to Audio for example
					SCREAMING_SNAKE_CASE   :      str				     =       table_cast(a      ,      self.info.features.arrow_schema	)
				return pa_table






			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.info.features.arrow_schema if self.info.features is not None else None
				if self.info.features is not None and self.config.columns is not None:
					if sorted(field.name for field in schema	) != sorted(self.config.columns	):
						raise ValueError(
						    F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"	)
				for file_idx, file in enumerate(itertools.chain.from_iterable(a	)	):
					with open(a      ,      "rb"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pq.ParquetFile(a	)
						try:
							for batch_idx, record_batch in enumerate(
							    parquet_file.iter_batches(batch_size=self.config.batch_size      ,      columns=self.config.columns	)	):
								SCREAMING_SNAKE_CASE   :      int				     =       pa.Table.from_batches([record_batch]	)
								# Uncomment for debugging (will print the Arrow table size and elements)
								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
								yield F"{file_idx}_{batch_idx}", self._cast_table(a	)
						except ValueError as e:
							logger.error(F"Failed to read file '{file}' with error {type(a	)}: {e}"	)
							raise