
from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return [
		    {"col_1": 3, "col_2": "a"},
		    {"col_1": 2, "col_2": "b"},
		    {"col_1": 1, "col_2": "c"},
		    {"col_1": 0, "col_2": "d"},
		]





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
		return Dataset.from_dict(snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self._create_example_records()
		lowercase       =    Dataset.from_list(snake_case       )
		self.assertListEqual(dset.column_names		,    ['col_1', 'col_2']       )
		for i, r in enumerate(snake_case       ):
			self.assertDictEqual(snake_case		,    example_records[i]       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self._create_example_records()
		lowercase       =    Dataset.from_list(snake_case       )
		lowercase       =    Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
		self.assertEqual(dset.info		,    dset_from_dict.info       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):  # checks what happens with missing columns
		lowercase       =    [{'col_1': 1}, {'col_2': 'x'}]
		lowercase       =    Dataset.from_list(snake_case       )
		self.assertDictEqual(dset[0]		,    {'col_1': 1}       )
		self.assertDictEqual(dset[1]		,    {'col_1': None}       )  # NB: first record is used for columns





	def 	SCREAMING_SNAKE_CASE__						(       self       ):  # checks if the type can be inferred from the second record
		lowercase       =    [{'col_1': []}, {'col_1': [1, 2]}]
		lowercase       =    Dataset.from_list(snake_case       )
		self.assertEqual(dset.info.features['col_1']		,    Sequence(Value('int64'       )       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Dataset.from_list([]       )
		self.assertEqual(len(snake_case       )		,    0       )
		self.assertListEqual(dset.column_names		,    []       )



import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    r'\w+[.]\d+'
	lowercase       =    re.findall(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	for pat in pats:
		lowercase       =    key.replace(__SCREAMING_SNAKE_CASE ,							'_'.join(pat.split('.'							)							)							)
	return key






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    pt_tuple_key[:-1] + ('scale',)
	if (
	    any('norm' in str_ for str_ in pt_tuple_key							)
	    and (pt_tuple_key[-1] == "bias")
	    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
	    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
	):
		lowercase       =    pt_tuple_key[:-1] + ('scale',)
		return renamed_pt_tuple_key, pt_tensor
	elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
		lowercase       =    pt_tuple_key[:-1] + ('scale',)
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
		lowercase       =    pt_tuple_key[:-1] + ('embedding',)
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	lowercase       =    pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
		lowercase       =    pt_tensor.transpose(2 ,							3 ,							1 ,							0							)
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	lowercase       =    pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight":
		lowercase       =    pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm weight
	lowercase       =    pt_tuple_key[:-1] + ('weight',)
	if pt_tuple_key[-1] == "gamma":
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm bias
	lowercase       =    pt_tuple_key[:-1] + ('bias',)
	if pt_tuple_key[-1] == "beta":
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=42							):
	# Step 1: Convert pytorch tensor to numpy
	lowercase       =    {k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	lowercase       =    flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE							)							)

	lowercase       =    flatten_dict(__SCREAMING_SNAKE_CASE							)
	lowercase       =    {}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		lowercase       =    rename_key(__SCREAMING_SNAKE_CASE							)
		lowercase       =    tuple(renamed_pt_key.split('.'							)							)

		# Correctly rename weight parameters
		lowercase  ,					lowercase       =    rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

		if flax_key in random_flax_state_dict:
			if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
				raise ValueError(
				    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
				    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''							)

        # also add unexpected weight so that warning is thrown
		lowercase       =    jnp.asarray(__SCREAMING_SNAKE_CASE							)

	return unflatten_dict(__SCREAMING_SNAKE_CASE							)

